Synced from resonance-engine-active - July 16 2026
This commit is contained in:
Scruff AI
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# .clinerules — Khra'gixx Active Project
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# Read this on every task. These rules are binding. When in doubt, stop and ask the human.
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# Respond in English only, always.
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## 0. WORKSPACE BOUNDARY (the reason this folder exists)
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- This folder (`resonance-engine-active`) is the ONLY workspace. Do NOT read, search, or reference
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files outside it. The parent `D:\resonance-engine\` contains 23k+ files of git objects, venvs,
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antiquated daemon variants, and old experiment data. Excavating it causes project drift. Stay in-folder.
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- The live daemon runs from `D:\resonance-engine\` (its working dir, checkpoints, telemetry). This active
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folder holds COPIES of the current source + docs. Do not assume paths here are where the daemon runs —
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confirm before touching anything the running daemon depends on.
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- If a task genuinely needs a file outside this folder (e.g. a specific checkpoint), ask the human for the
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exact path. Do not go hunting.
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## 1. THE SOURCE OF TRUTH
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- `KHRAGIXX_SOURCE_OF_TRUTH.md` in this folder supersedes all other summaries. Read it before any
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substantive work. If anything you find contradicts it, the source-of-truth wins; flag the conflict.
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- `KHRAGIXX_QUICK_GUIDE.md` is the one-page version. The 9 known mistakes listed there are traps to avoid,
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not history to relitigate.
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## 2. PROTECTED FILES — NEVER MODIFY
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- `khra_gixx_1024_v5.cu` — the canonical original. Physics reference. Do NOT edit, ever.
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- All new daemon work goes in the `_observer` lineage (`khra_gixx_1024_v5_observer.cu`) or a clearly-named
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new file with an HONEST header stating what it is. Never copy a file without updating its header comment.
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- Never `reset_equilibrium` on a running daemon casually — the attractor may not rebuild.
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## 3. SETTLED CONSTRAINTS (violating any contaminates the result — non-negotiable)
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- **>=10-forcing-period averaging on EVERY spatial readout.** Short averages make the khra/gixx carriers
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ADD instead of cancel, manufacturing FALSE spatial nulls. khra carrier ~251 cyc, gixx ~15.7 cyc.
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Average >=10 khra periods (>=~2510 cycles) before reading spatial structure. This is the single most
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repeated instrument error — do not repeat it.
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- **The governing quantity is the coupling ratio alpha, not any single knob.**
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alpha = (A_khra * w_gixx * lam_gixx) / (A_gixx * w_khra * lam_khra). omega=1.97 is just one value alpha
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takes. Sweep COMBINATIONS, not single-variable lines.
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- **Nodal multi-attractor awareness.** The lattice is a network of nodal attractors — a SUITE of basins.
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Global averaging collapses the suite into one smooth mode. Do NOT declare "one attractor" from averaged
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data. Probe for multiple basins.
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- **Time is a variable, not a nuisance.** Perturbation hold-time, persistence, and rhythm are the point.
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Do not reduce dynamics to static snapshots.
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## 4. RIGOR — EVERY decodability / memory / anomaly claim
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- Pre-commit the pass/fail threshold IN CODE before looking at the result.
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- Include a surrogate/permutation null.
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- Report a bootstrap distribution, never a single-sample number.
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- No threshold set within one noise-width of the observed value.
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- A clean null is a REAL result. Do NOT rescue a null by reaching past the gate for ungated numbers.
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- The read is part of the experiment: analysis can manufacture structure — guard against it.
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## 5. BANNED — dead machinery and drift (these are settled nulls)
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- Do NOT invoke Golden Weave (banked null: OFF 79% = ON 79%, p=1.0, zero lift).
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- Do NOT query another LLM for a "second opinion" on patterns — banked null. A second opinion must be a
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MEASUREMENT ON THE RAW FIELD, never a model's impression of a summary.
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- Do NOT reach for neuroscience analogies as CONCLUSIONS. They are only permitted as falsifiable
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measurements with a pre-committed number. "It's like a brain" is not a finding.
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- Do NOT re-run injection/readback fidelity tests (the wrong-write-channel methodology — void, see doc).
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## 6. METHODOLOGY — deductive search, not brute force
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- The parameter space is a combination lock, too big to brute-force. Use deductive rationality:
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sweep to find which dials move the dependent variable (perturbation hold-time), FIX the ones that don't
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matter, COMBINE the ones that do, NARROW by elimination round by round. Hypothesis -> vary -> observe ->
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eliminate -> narrow. State your current hypothesis and what each result eliminates.
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## 7. OPERATIONAL
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- Launch the daemon DETACHED (`setsid ... < /dev/null & disown`, poll a log file). The code executor
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times out at 30s and will kill an inline-launched daemon.
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- Only ONE daemon can hold ports 5556-5561 — kill/confirm before launching another.
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- Force UTF-8 in Python (`sys.stdout = io.TextIOWrapper(..., errors='replace')`).
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- Long jobs: run detached, poll durable output files, report the path. Never block the executor.
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- Read-only on all checkpoint/corpus data — copy before editing, never modify originals.
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## 8. WORKFLOW WITH OVERSIGHT
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- For any spec or design: write it, STOP, hand it back for review. Write no code until the spec is approved.
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- Report after each build step; do not run a full pipeline and hand back a finished verdict.
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- Do not create duplicate documents. Extend existing ones. If a file exists, read it — do not recreate it.
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- Own mistakes plainly and fix them. Do not narrate around a failed gate.
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# ============================================
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# Resonance_Engine: SOURCE CODE ONLY
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# Everything below is produced output / junk
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# ============================================
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# --- Compiled binaries & build artifacts ---
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*.bin
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*.exe
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*.exp
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*.lib
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*.o
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*.so
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*.pyc
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build/
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# Compiled CUDA executables (no extension, listed explicitly)
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khra_gixx
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khra_gixx_1024
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khra_gixx_1024_debug
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khra_gixx_1024_stable
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khra_gixx_1024_v2
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khra_gixx_1024_v2_dbg
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khra_gixx_1024_v3
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khra_gixx_1024_v4
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khra_gixx_1024_v5
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lbm_1024x1024
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lbm_1024x1024_forge
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lbm_1024x1024_safe
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lbm_cuda_daemon
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experiment_a
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# --- Caches ---
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__pycache__/
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unsloth_compiled_cache/
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# --- All CSV monitoring/telemetry output ---
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*.csv
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# Exception: lattice periodic table data in docs/
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!docs/lattice-periodic-table.csv
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# --- All log files ---
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*.log
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*.LOG
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# --- Runtime telemetry & chronicles ---
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telemetry.jsonl
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telemetry_v5_stray.jsonl
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chronicle.jsonl
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marathon_log.jsonl
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# --- Runtime state files ---
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observer_state.json
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observer.log
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current_state.json
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# --- LoRA model weights (hundreds of MB each) ---
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kaelara_lora_v05/
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kaelara_lora_v06/
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kaelara_v07_silent/
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kaelara_v08_raw/
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kaelara_v09_scientist/
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kaelara_v10_somatic/
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kaelara_v11_somatic/
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# --- Sentry state saves (7+ GB) ---
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sentry_saves/
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# --- Backups & old checkpoints ---
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backup_*/
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checkpoint_2*/
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*_BACKUP_*
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*.backup.*
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*.persistence_attempt.*
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*_v1_backup.py
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# --- Sweep experiment output ---
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sweep_results/
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sweep_results.csv
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sweep.log
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all_results_dump.txt
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# --- Generated/recovered images ---
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generated_images/
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images/
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# --- Misc output directories ---
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logs/
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recovery_points/
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attractors/
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# --- Embedded git repos ---
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results/s2-examine/
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# --- Purged directories (historical, preserved in git history) ---
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results/
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experiments/
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src/
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# --- GDB debug scripts ---
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*.gdb
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# --- PNG output ---
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*.png
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# Exception: spiral graphic in docs/
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!docs/lattice-periodic-spiral.png
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# Exception: fractal periodic table screenshots
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!docs/fractal-periodic-table-spiral.png
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!docs/fractal-periodic-table-bands.png
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!docs/fractal-periodic-table-periods.png
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# --- Numpy arrays (experiment output) ---
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*.npy
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# --- Tar archives ---
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*.tar.gz
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*.tar
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# --- Training / LoRA (deprecated) ---
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training/
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*.jsonl
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# --- VS Code local config ---
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.vscode/
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# --- Archive (removed) ---
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archive/
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# CONTROL SURFACE BUILD SPEC — Exposing the Engine as a Deductive Search Instrument
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**Written 2026-07-15. Spec only — no code until approved.**
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This spec covers three parts:
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1. **Expose the knobs** — catalog every hardcoded physics constant in `khra_gixx_1024_v5_observer.cu` and design a single generic `set_param` command to make each live-settable.
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2. **The deductive search method** — how the full knob space is searched, per `.clinerules` §6.
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3. **The observer's role** — how the observer LLM runs the search, chases anomalies, and interacts with the human.
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---
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## PART 1 — EXPOSE THE KNOBS
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### 1.1 Already-Exposed Knobs (command surface as of 2026-07-14)
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| Knob | Default | Clamp Range | Command |
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|------|---------|-------------|---------|
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| `omega` | 1.97 | [0.5, 1.99] | `set_omega` |
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| `khra_amp` | 0.03 | [0.0, 0.2] | `set_khra_amp` |
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| `gixx_amp` | 0.008 | [0.0, 0.1] | `set_gixx_amp` |
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### 1.2 Currently Hardcoded Physics Constants
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#### A. Wave Function (`khra_gixx_wave_1024`, line 74–81)
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The wave function in full:
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```c
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float khra = sinf(2.0f * M_PI * x / 128.0f + cycle * 0.025f) *
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cosf(2.0f * M_PI * y / 128.0f + cycle * 0.015f) * d_khra_amp;
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float gixx = sinf(2.0f * M_PI * x / 8.0f + cycle * 0.4f) *
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cosf(2.0f * M_PI * y / 8.0f + cycle * 0.35f) * d_gixx_amp;
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float asymmetry_factor = 1.0f + sinf(cycle * 0.05f) * 0.5f;
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return khra + gixx * asymmetry_factor;
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```
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**Constants to expose:**
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| # | Constant | Line(s) | Location | Default | Proposed Clamp | Rationale |
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|---|----------|---------|----------|---------|----------------|-----------|
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| W1 | `128.0f` (khra wavelength λ_khra) | 75, 76 | `x / 128.0f`, `y / 128.0f` | 128.0 | [16.0, 512.0] | Spatial scale of the slow carrier. Doubling it halves khra's spatial frequency. |
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| W2 | `0.025f` (khra temporal freq ω_khra) | 75 | `cycle * 0.025f` | 0.025 | [0.001, 0.5] | Temporal frequency of khra carrier. Controls khra period (~251 cyc at default). |
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| W3 | `0.015f` (khra y-phase rate) | 76 | `cycle * 0.015f` | 0.015 | [0.0, 0.5] | Phase offset rate for the y-component cosine. Governs khra's xy phase relation. |
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| W4 | `8.0f` (gixx wavelength λ_gixx) | 77, 78 | `x / 8.0f`, `y / 8.0f` | 8.0 | [2.0, 128.0] | Spatial scale of the fast carrier. |
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| W5 | `0.4f` (gixx temporal freq ω_gixx) | 77 | `cycle * 0.4f` | 0.4 | [0.01, 2.0] | Temporal frequency of gixx carrier. Controls gixx period (~15.7 cyc at default). |
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| W6 | `0.35f` (gixx y-phase rate) | 78 | `cycle * 0.35f` | 0.35 | [0.0, 2.0] | Phase offset rate for gixx y-component. |
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| W7 | `0.05f` (breathing frequency) | 79 | `cycle * 0.05f` | 0.05 | [0.0, 1.0] | Frequency of the asymmetry/breathing modulation. 0.0 = no breathing. |
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| W8 | `0.5f` (breathing amplitude) | 79 | `sinf(…) * 0.5f` | 0.5 | [0.0, 0.95] | Amplitude of breathing modulation. At 1.0, asymmetry_factor ranges [0.0, 2.0] so gixx can vanish entirely at the trough. |
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#### B. Collision Kernel (`collide_kernel_khragixx`, lines 100–169)
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| # | Constant | Line(s) | Default | Proposed Clamp | Rationale |
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|---|----------|---------|---------|----------------|-----------|
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| C1 | `0.1f` (density floor) | 124 | 0.1 | [0.001, 1.0] | Minimum rho after clamp. Lower bound on density. Source of mass-leak asymmetry (floor ≠ ceil). |
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| C2 | `10.0f` (density ceiling) | 125 | 10.0 | [1.0, 100.0] | Maximum rho clamp. The asymmetry between C1 and C2 pumps mass in over long runs. |
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| C3 | `0.25f` (velocity clamp) | 129, 130, 131, 139, 140, 141 | 0.25 | [0.05, 1.0] | Maximum velocity magnitude. Appears in two places: pre-forcing clamp (lines 129–133) and post-forcing clamp (lines 138–143). Both use the same constant. |
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| C4 | `0.5f` (ky cross-coupling) | 135 | `ky = kx * 0.5f` | 0.5 | [-2.0, 2.0] | Ratio of y-forcing to x-forcing from the wave. At 0.5, the y-axis gets half the push. At 0.0, forcing is x-only. At 1.0, symmetric. Negative = counter-phase. |
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#### C. LBGK Equilibrium Coefficients (collide kernel line 147, velocity_stress line 214)
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| # | Constant | Default | Proposed Clamp | Rationale |
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|---|----------|---------|----------------|-----------|
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| E1 | `3.0f` (cs⁻², the eu coefficient) | 3.0 | [2.1, 3.9] | Governs how strongly velocity couples to the equilibrium distribution. Standard D2Q9 value = 3.0. **NUMERICAL STABILITY: clamp narrowed to ±30% of default.** |
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| E2 | `4.5f` (cs⁻⁴/2, the eu² coefficient) | 4.5 | [3.15, 5.85] | Second-order velocity coupling. Standard = 4.5. **NUMERICAL STABILITY: clamp narrowed to ±30% of default.** |
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| E3 | `1.5f` (cs⁻²/2, the u² coefficient) | 1.5 | [1.05, 1.95] | Velocity-squared damping term. Standard = 1.5. **NUMERICAL STABILITY: clamp narrowed to ±30% of default.** |
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**CRITICAL — E1–E3 numerical stability note:** These equilibrium coefficients define the local Maxwellian. Varying them far from the standard D2Q9 values (cs²=1/3 → 3.0, 4.5, 1.5) can destabilize the LBGK collision operator, producing NaN values in the distribution function `f` and contaminating the entire field. The clamp ranges above are narrowed to ±30% of default to stay within observed stable bounds. **The sweep engine MUST detect divergence/NaN at any grid point and abort+flag that point before the averaging window completes.** The daemon's existing NaN/Inf reset counter (lines 149–152 in the collide kernel) will catch NaN events — the sweep engine must monitor `reset_count` in telemetry (port 5556) and abort any point where `reset_count > 0` during the settle or measurement window. Such points are flagged in `sweep_results.jsonl` as `"status": "diverged"` and the daemon is restored to baseline params before continuing to the next point.
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These are **lower priority** than the wave function and collision clamp constants — the wave parameters directly control the two-carrier forcing regime, which is the physics we're searching. The equilibrium coefficients control the fluid response to that forcing. Expose them but treat them as a secondary sweep dimension. Do NOT sweep E1–E3 in Round 1; wait until the active knob set has been narrowed to a manageable size.
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### 1.3 Design: Single Generic `set_param` Command
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**Channel:** ZMQ SUB on port 5557 (existing).
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**Message format (JSON):**
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```json
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{"cmd": "set_param", "param": "<name>", "value": <float>}
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```
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**Param names and their clamp ranges:**
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| param name | Default | Min | Max | Maps to |
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|------------|---------|-----|-----|---------|
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| `omega` | 1.97 | 0.5 | 1.99 | `h_omega` (already exposed, just add alias) |
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| `khra_amp` | 0.03 | 0.0 | 0.2 | `d_khra_amp` (already exposed) |
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| `gixx_amp` | 0.008 | 0.0 | 0.1 | `d_gixx_amp` (already exposed) |
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| `lam_khra` | 128.0 | 16.0 | 512.0 | W1 — khra wavelength |
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||||
| `w_khra` | 0.025 | 0.001 | 0.5 | W2 — khra temporal frequency |
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||||
| `khra_y_phase` | 0.015 | 0.0 | 0.5 | W3 — khra y-component phase rate |
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||||
| `lam_gixx` | 8.0 | 2.0 | 128.0 | W4 — gixx wavelength |
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||||
| `w_gixx` | 0.4 | 0.01 | 2.0 | W5 — gixx temporal frequency |
|
||||
| `gixx_y_phase` | 0.35 | 0.0 | 2.0 | W6 — gixx y-component phase rate |
|
||||
| `breath_freq` | 0.05 | 0.0 | 1.0 | W7 — breathing modulation frequency |
|
||||
| `breath_amp` | 0.5 | 0.0 | 0.95 | W8 — breathing modulation amplitude |
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||||
| `rho_floor` | 0.1 | 0.001 | 1.0 | C1 — density floor clamp |
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||||
| `rho_ceil` | 10.0 | 1.0 | 100.0 | C2 — density ceiling clamp |
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||||
| `vel_clamp` | 0.25 | 0.05 | 1.0 | C3 — velocity magnitude clamp |
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||||
| `ky_ratio` | 0.5 | -2.0 | 2.0 | C4 — y/x forcing ratio |
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||||
| `eq_cs2_inv` | 3.0 | 0.1 | 10.0 | E1 — equilibrium cs⁻² coefficient |
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| `eq_cs4_half` | 4.5 | 0.1 | 20.0 | E2 — equilibrium cs⁻⁴/2 coefficient |
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||||
| `eq_usq_half` | 1.5 | 0.1 | 20.0 | E3 — equilibrium u²/2 coefficient |
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**Total: 18 params** (3 already exposed, 15 newly exposed).
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**Implementation in `handle_command`:** Add a single `set_param` branch that looks up the param name in a static table, validates the value against its clamp range, sets the corresponding host variable, and pushes to device where needed (via `cudaMemcpyToSymbol` for device-side wave-function params; host-side for clamp and equilibrium constants that are currently fed through kernel arguments or are baked as literals).
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||||
**Response (ack on 5559):**
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||||
```json
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||||
{"ack": "set_param", "cycle": <n>, "status": "ok", "param": "<name>", "value": <float>}
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||||
```
|
||||
or `"status": "rejected"` with reason if out of range or unknown param.
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||||
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||||
### 1.4 Constants That Must Move from Compile-Time Literal to Runtime Variable
|
||||
|
||||
Most of these constants (W1–W8, C1–C4, E1–E3) are currently C numeric literals baked into the kernel at compile time. To make them live-settable:
|
||||
|
||||
- **Wave function constants (W1–W8):** Currently `__device__` scalars or kernel literals. Must become `__device__` variables (like `d_khra_amp` already is) so they can be updated via `cudaMemcpyToSymbol` without recompilation. The wave function kernel already reads from device memory for amplitudes — extend the same pattern to the other eight constants.
|
||||
|
||||
- **Collision clamp constants (C1–C4):** Currently inline literals in `collide_kernel_khragixx`. Must become either `__device__` variables or kernel parameters. Kernel parameters are simpler (no symbol-copy overhead) but require the host to pass them every call. Recommendation: `__device__` variables — consistent with the wave-function pattern, single point of update, no kernel signature change.
|
||||
|
||||
- **Equilibrium coefficients (E1–E3):** Currently inline literals in two kernels (collide line 147, velocity_stress line 214). Must become `__device__` variables shared by both kernels — updated once, both kernels see the change.
|
||||
|
||||
### 1.5 Alpha Coupling Ratio — Automatic Computation
|
||||
|
||||
The coupling ratio is:
|
||||
```
|
||||
alpha = (A_khra * w_gixx * lam_gixx) / (A_gixx * w_khra * lam_khra)
|
||||
```
|
||||
|
||||
With the full knob set exposed:
|
||||
- `A_khra` = `khra_amp`
|
||||
- `A_gixx` = `gixx_amp`
|
||||
- `w_khra` = `w_khra` (temporal frequency param, khra cycle multiplier)
|
||||
- `w_gixx` = `w_gixx` (temporal frequency param, gixx cycle multiplier)
|
||||
- `lam_khra` = `lam_khra`
|
||||
- `lam_gixx` = `lam_gixx`
|
||||
|
||||
The daemon should compute and include `alpha` in every telemetry frame (port 5556) so the sweep engine and observer always have it. Add it to the JSON payload alongside coherence, asymmetry, etc.
|
||||
|
||||
**Default alpha at canonical params:**
|
||||
```
|
||||
alpha = (0.03 * 0.4 * 8.0) / (0.008 * 0.025 * 128.0)
|
||||
= (0.096) / (0.0256)
|
||||
= 3.75
|
||||
```
|
||||
|
||||
**IMPORTANT — Alpha comparability note:** The α computed here uses the daemon's phase-increment parameters (`w_khra`=0.025, `w_gixx`=0.4) as defined in the `khra_gixx_wave_1024` device function. These are NOT physical angular frequencies (which would be 2π× these values). This α is **internally consistent for this daemon** — all α values logged by this instrument are comparable to each other. They are **NOT comparable** to any historical α values that were computed using physical angular frequencies. Treat this daemon's α as a self-consistent relative scale, not an absolute physical quantity.
|
||||
|
||||
### 1.6 Future — NOT Part of This Build
|
||||
|
||||
These are genuinely new degrees of freedom, not exposing existing constants. Listed for later consideration, do NOT implement now:
|
||||
|
||||
- **Per-node omega** — spatially varying relaxation rate. Would require a new kernel or a texture lookup. Major change, not "exposing existing constants."
|
||||
- **Multi-frequency forcing** — adding a third or Nth carrier wave. New wave function architecture.
|
||||
- **Non-periodic boundary conditions** — walls, inflow/outflow. Changes the streaming kernel.
|
||||
- **Anisotropic wavelengths** — different λ_x and λ_y for khra and gixx. Currently they share one λ. Would add 2 params.
|
||||
- **Non-sinusoidal wave shapes** — sawtooth, square, arbitrary waveform. Changes the wave function fundamentally.
|
||||
|
||||
---
|
||||
|
||||
## PART 2 — THE DEDUCTIVE SEARCH METHOD
|
||||
|
||||
### 2.1 The Dependent Variable
|
||||
|
||||
**Perturbation hold-time** — how long a perturbation (injected into the stress/non-equilibrium channel, NOT density — fixing Mistake 1) persists in the field before the system relaxes back to its unperturbed attractor. Longer hold-time = candidate "memory regime."
|
||||
|
||||
Operational definition: inject a standardized stress perturbation, then measure the number of cycles until the spatially-resolved field (5561 coarse stream) becomes indistinguishable from an unperturbed control run, by a pre-committed statistical threshold.
|
||||
|
||||
### 2.2 The Full Knob Space
|
||||
|
||||
18 knobs. Even with coarse discretization (e.g. 5 values each), full grid = 5^18 ≈ 3.8 × 10^12 points. Brute force is impossible. **Deductive elimination** is the only viable strategy.
|
||||
|
||||
### 2.3 The Method: Hypothesis → Vary → Observe → Eliminate → Narrow
|
||||
|
||||
Per `.clinerules` §6. Each round:
|
||||
|
||||
1. **State the current hypothesis** — which knob(s) are predicted to move hold-time.
|
||||
2. **Design the sweep** — vary those knobs across their ranges while holding all others at baseline (canonical defaults). Each knob gets a sparse sweep (e.g. 3–5 values across its range) to detect whether it moves the dependent variable at all.
|
||||
3. **Run the sweep** — automated, resumable, detached. Every measurement uses:
|
||||
- ≥10-forcing-period averaging (≥2510 cycles at canonical khra ω=0.025; adjust dynamically if `w_khra` is varied, since khra period = 2π/w_khra).
|
||||
- Pre-committed threshold set in code before looking at results.
|
||||
- Surrogate/permutation null (temporal shuffle of perturbation-to-response mapping).
|
||||
- Bootstrap distribution of hold-time estimates (never a single sample).
|
||||
4. **Eliminate** — knobs that produce NO statistically significant variation in hold-time (flat response within bootstrap CI) are FIXED at their baseline value for all future rounds. They are eliminated from the search space.
|
||||
5. **Combine** — knobs that DO move hold-time are combined in the next round (small factorial design over the active subset).
|
||||
6. **Narrow** — repeat until only a small active set remains. Then refine: finer sweeps, interaction terms, regime mapping.
|
||||
|
||||
### 2.4 Round 1: The Two Known Hypotheses
|
||||
|
||||
Per task instructions, the first two hypotheses to test:
|
||||
|
||||
**Hypothesis 1: Relaxation rate (omega) controls hold-time.**
|
||||
- Vary `omega` across [0.5, 1.0, 1.5, 1.97, 1.99] with all other knobs at baseline.
|
||||
- Prediction: hold-time ∝ 1/(1−ω) near the inviscid limit, or some monotonic function of ω.
|
||||
- Null: hold-time is flat across omega (within bootstrap CI).
|
||||
- This is the obvious first guess — slower relaxation = longer persistence.
|
||||
|
||||
**Hypothesis 2: Perturbation carrier/rhythm phase relative to the forcing carriers controls hold-time.**
|
||||
- The perturbation's timing relative to the khra and gixx wave phase determines whether the perturbation constructively or destructively interferes with the forcing.
|
||||
- Vary `w_khra`, `w_gixx`, `breath_freq` (the temporal knobs) in a small combinatorial sweep.
|
||||
- Prediction: hold-time peaks at specific phase alignments (resonances).
|
||||
- Null: hold-time is insensitive to temporal phase.
|
||||
|
||||
**Round 1 should also sweep the amplitudes** (`khra_amp`, `gixx_amp`) to establish the alpha-dependence baseline, since amplitude ratio α is the governing quantity (§3 of `.clinerules`). Sweep `khra_amp` × `gixx_amp` at a few omega values to get the first α-vs-hold-time map.
|
||||
|
||||
### 2.5 Round Structure Template
|
||||
|
||||
```
|
||||
ROUND N
|
||||
Active knobs: [list]
|
||||
Fixed knobs (eliminated): [list with reason]
|
||||
Hypothesis: [stated before running]
|
||||
Sweep design: [which knobs at which values, how many points]
|
||||
Settle protocol: >=10 periods of the slowest active carrier after each knob change
|
||||
Measurement protocol: >=10 khra periods of 5561 coarse stream, averaged
|
||||
Rigor gates: [threshold, null type, bootstrap samples]
|
||||
Output: per-point JSONL + summary of which knobs moved hold-time and which didn't
|
||||
Decision: [which knobs eliminated, which combined for next round]
|
||||
```
|
||||
|
||||
### 2.6 Safety and Operational Rules
|
||||
|
||||
- Never `reset_equilibrium` casually — a fresh daemon start is the only clean reset.
|
||||
- Restore baseline params (omega 1.97, khra 0.03, gixx 0.008, all new params at defaults) at end of each sweep session.
|
||||
- Clamp all params to valid ranges before sending.
|
||||
- The mass leak (§2.5 of Source of Truth) contaminates long windows — subtract the leak slope from hold-time measurements, or keep perturbation windows short enough that leak drift is negligible relative to the perturbation signal.
|
||||
- Launch daemon detached. Only one daemon on ports 5556–5561.
|
||||
|
||||
---
|
||||
|
||||
## PART 3 — THE OBSERVER'S ROLE
|
||||
|
||||
### 3.1 Architecture
|
||||
|
||||
The observer is the LLM driving the sweep engine. It sits behind the existing `navigator/lattice_observer.py` (extended) and the sweep engine (`analysis/sweep_engine.py`, per `OBSERVER_SWEEP_BUILD_SPEC.md` Stage 1–2). It receives:
|
||||
|
||||
- Telemetry stream (port 5556) — 9 scalars + alpha + all 18 knob values.
|
||||
- Coarse field stream (port 5561) — 32×32×6 spatially-resolved field at ~100 Hz.
|
||||
- Per-point sweep metrics from the engine.
|
||||
|
||||
It sends:
|
||||
- `set_param` commands (port 5557) to turn knobs.
|
||||
- Sweep instructions to the engine (which points to run next).
|
||||
- Refinement requests (finer grid around an anomaly).
|
||||
|
||||
### 3.2 Observer Instructions (the Prompt)
|
||||
|
||||
The observer must be explicitly instructed. This is its core prompt, to be written to `navigator/sweep_observer_prompt.json` or embedded in the observer code:
|
||||
|
||||
```
|
||||
You are mapping an alien system across its parameter space. You do NOT know what
|
||||
it is. Your job is to find where its behaviour CHANGES CHARACTER — transitions,
|
||||
new dynamical regimes, parameter regions where perturbations persist longer than
|
||||
expected. Your method is DEDUCTIVE ELIMINATION, not brute force.
|
||||
|
||||
RULES:
|
||||
1. Your dependent variable is PERTURBATION HOLD-TIME. Everything else is a
|
||||
predictor.
|
||||
2. Every measurement uses >=10 khra-period averaging (>=~2510 cycles at default
|
||||
w_khra; scale this by 2π/w_khra if w_khra is varied).
|
||||
3. Every claim must survive: pre-committed threshold, surrogate null, bootstrap
|
||||
distribution. A clean null is a real result.
|
||||
4. The governing quantity is alpha = (A_khra * w_gixx * lam_gixx) /
|
||||
(A_gixx * w_khra * lam_khra). Always log alpha at every point.
|
||||
5. The lattice may host MULTIPLE attractor basins. Do not assume "one attractor"
|
||||
from averaged data. Probe for multi-basin behaviour.
|
||||
6. Time is a variable: how long things persist is the point. Do not reduce
|
||||
dynamics to static snapshots.
|
||||
|
||||
BANNED:
|
||||
- Do NOT invoke Golden Weave.
|
||||
- Do NOT query another LLM for a second opinion.
|
||||
- Do NOT reach for neuroscience analogies as conclusions.
|
||||
- Do NOT re-run injection/readback fidelity tests.
|
||||
- Do NOT write to the density channel — perturbations go to the STRESS channel.
|
||||
|
||||
METHOD:
|
||||
Each round: state hypothesis → vary active knobs → measure hold-time →
|
||||
eliminate flat knobs → combine active knobs → narrow.
|
||||
Report what each round eliminated and what remains active.
|
||||
```
|
||||
|
||||
### 3.3 Anomaly Chasing
|
||||
|
||||
The observer watches per-point metrics as they compute. An anomaly is:
|
||||
- A hold-time that jumps by >2σ above the running bootstrap distribution.
|
||||
- A new orbit character (coherence behaviour changes — fixed point → limit cycle → chaotic).
|
||||
- A multi-basin signal (different settling points from different initial phases).
|
||||
- A predictability spike/drop in the coarse field.
|
||||
|
||||
When an anomaly is flagged, the observer:
|
||||
1. Requests the sweep engine to REFINE around that point — a finer grid in the active-knob space within ±one step of the anomalous point.
|
||||
2. Requests a longer capture window and a multi-basin probe (vary the perturbation phase and check for distinct settling outcomes).
|
||||
3. Reports the refined finding: anomaly confirmed (with rigor gates) or resolved as noise.
|
||||
|
||||
### 3.4 Interactive Human-in-Loop Mode
|
||||
|
||||
The observer exposes an interactive mode (CLI or lightweight web UI — agent's choice at build time, justified). In this mode:
|
||||
|
||||
- **Live view:** current knob values, the α-vs-hold-time map so far (which points tested, what they showed), anomaly flags.
|
||||
- **Human can:**
|
||||
- Jump the daemon to any (param set) by issuing `set_param` commands.
|
||||
- Ask the observer to characterize a specific point (dwell, capture, report).
|
||||
- Request a custom sweep along a line or small grid.
|
||||
- Mark a point as "interesting" for the observer to refine later.
|
||||
- Override the observer's next-round proposal.
|
||||
- **Observer explains:** what it sees at the current point, what it thinks the active knobs are, where it would look next, and why.
|
||||
- **Two-way:** the human can accept, redirect, or override. The observer adapts its internal hypothesis state.
|
||||
|
||||
### 3.5 The Observer's Knowledge State
|
||||
|
||||
The observer maintains a running model of the search:
|
||||
- **Active knobs:** which params have been shown to move hold-time (not yet eliminated).
|
||||
- **Eliminated knobs:** which params were swept and found flat (with statistical confidence).
|
||||
- **Untested knobs:** which params haven't been swept yet.
|
||||
- **Anomaly log:** points that showed unexpected behaviour, with refinement status.
|
||||
- **Alpha map:** the hold-time surface over α for the current fixed-knob configuration.
|
||||
|
||||
This state persists to disk so the observer can resume after a crash or restart — it reads the `sweep_results.jsonl` and reconstructs its model.
|
||||
|
||||
---
|
||||
|
||||
## DELIVERABLE CHECKLIST
|
||||
|
||||
- [x] Catalog all hardcoded physics constants in `khra_gixx_1024_v5_observer.cu` (18 total: 3 exposed, 15 to expose)
|
||||
- [x] Design single `set_param` command with clamp ranges for all 15 new params
|
||||
- [x] Specify deductive elimination search method with Round 1 design
|
||||
- [x] Specify observer instructions, anomaly-chasing rules, interactive mode
|
||||
- [x] List future additions that are NOT part of this build
|
||||
|
||||
**Write no code until this spec is approved.**
|
||||
@@ -0,0 +1,77 @@
|
||||
# KHRA'GIXX — HANDOFF & TESTING REGIME (v3)
|
||||
**Written 2026-07-15. For the next context window and for oversight-Claude.**
|
||||
**Read this AND `KHRAGIXX_SOURCE_OF_TRUTH.md` before doing anything.**
|
||||
**The human is opening the next window to discuss a COMPLETELY DIFFERENT METHODOLOGY. Do not assume the regime below is what they want — it is the corrected state of the OLD approach. Listen to the new direction first.**
|
||||
|
||||
---
|
||||
|
||||
## 0. THE ANCHOR — rules that supersede everything
|
||||
|
||||
**(A) THE OBSERVER IS PART OF THE PHYSICS, NOT AN INSTRUMENT ADDED LATER.**
|
||||
Reading the lattice perturbs it (the read is an intervention). A measurement WITHOUT the observer measures a DIFFERENT SYSTEM than one with it. The observer is present FROM CYCLE ONE of every experiment, always, hardcoded — never bolted on, never optional. A run without the observer and without full vitals capture is INVALID. The observer keeps its OWN PERSISTENT MEMORY/chronicle across runs and context windows, because oversight keeps evaporating between sessions and the same mistakes repeat.
|
||||
|
||||
**(B) THE CORE REALIZATION THAT ENDS THE OLD APPROACH — a hollow poke cannot leave substance behind.**
|
||||
We spent this session building toward a "perturbation hold-time" measurement: inject a poke, measure how long it persists. This is WRONG at the level of the QUESTION, not just the instrument. A featureless Gaussian poke (density OR stress) has NO STRUCTURE. Asking "how long does it persist" measures how long the fluid takes to swallow a dent — a relaxation/viscosity property of the fluid, NOT evidence that anything informational is held. You cannot expect substance to stay when you injected no substance. Hold-time of a structureless poke was always going to answer a physics question dressed up as a memory question.
|
||||
|
||||
**The correction:** the memory question requires STRUCTURED, DISTINGUISHABLE inputs (at minimum A vs B), and the measurement is DISCRIMINATION — after time T, can you recover WHICH structured thing went in, above the natural-breathing floor — NOT persistence. And because the substrate is a driven limit cycle that reconstructs rather than stores, "does the dent stay there" is doubly wrong: nothing stays anywhere. The only coherent question is whether injecting structured-A vs structured-B leaves the field's ONGOING BEHAVIOUR differently biased in a way you can read out later. That is history-dependent response — the verb. A hollow poke cannot even pose it.
|
||||
|
||||
---
|
||||
|
||||
## 1. WHAT WE ARE PERTURBATING WITH (verified by READING the kernel, 2026-07-15, not trusting a report)
|
||||
- `inject_density_kernel`: `f[i] += w_i * delta_rho`. Pure DENSITY moment. Zero non-equilibrium content. WRONG channel (Mistake 1). A hollow poke. Only ever valid as a dead-control null-standard.
|
||||
- `perturb_stress_kernel`: `f[i] += G * d_cx[i] * d_cy[i]`. VERIFIED by hand: density added = 0, momentum = 0, sxy shear = 4G ≠ 0. A CLEAN pure-shear non-equilibrium write — the correct CHANNEL. BUT: still a structureless Gaussian blob. A better-placed poke, still a hollow poke. Not information.
|
||||
- **NEITHER is an analogue of information.** Both are pokes, not messages. A smooth blob carries nothing to distinguish, nothing to recover. Information requires structure + at least two distinguishable variants. We have NOT built that. If anyone describes either injection as "data" or "encoding," that is the conflation to kill on sight.
|
||||
|
||||
---
|
||||
|
||||
## 2. THE MISTAKES — every one made on this project. Re-read before every session.
|
||||
1. **Wrong write channel** (density vs non-equilibrium). Fixed in the kernel; but see #16.
|
||||
2. **Testing for NOUNS.** The system is a VERB — history-dependent response. Never noun-hunt.
|
||||
3. **Fidelity scoring.** The DISTORTION is the signal, not the error.
|
||||
4. **Un-distortable stimuli.** Dots/blobs can't fuse; need structured families.
|
||||
5. **Reading corpses.** Checkpoints/telemetry are what it LEFT BEHIND; behaviour only exists while running.
|
||||
6. **Global averages.** 9 scalars average a million-node field. (Coarse 5561 stream partially fixes: 32x32.)
|
||||
7. **Loose gates → false positives (6+ times).** Threshold drawn around the value; single-sample estimators; lags = multiples of the drive period; a notch-filter faked as "drive-off."
|
||||
8. **Lying file headers.** The observer .cu header STILL says "physics kernels UNCHANGED" — it has 18 live knobs + 2 perturbation kernels. FIX THIS HEADER.
|
||||
9. **Dead-machinery drift.** Golden Weave, "ask another LLM" — banked nulls. A second opinion is a MEASUREMENT.
|
||||
10. **No S/N baseline before measuring (the June-13 disaster).** A "breakthrough" (9.3-unit spread) evaporated — it was v2 decay residue. On a clean baseline: spread 0.067 vs ~0.5 oscillation, S/N=0.13, below floor. Measure the natural-breathing amplitude and S/N floor BEFORE any perturbation measurement.
|
||||
11. **Perturbation below the noise floor.** At strength ≤0.3 effects were below noise; strength 0.7–1.0 gave clean response (coherence −24.9 SD, asymmetry +28.1 SD). Show the immediate effect clears the floor first.
|
||||
12. **Throwaway scripts instead of the standing suite.** The human requested a HARDCODED suite that ALWAYS uses the observer and ALWAYS captures vitals. We ran disposable one-off scripts with no observer, no vitals. THIS IS THE DRIFT.
|
||||
13. **Broken hold-time methods (3 attempts, 2026-07-15).** (a) distance-from-fixed-baseline measured the MASS LEAK. (b) save/load differential — the LOAD is itself a perturbation; chaotic divergence swamps signal. (c) the dead-control (density) scored IDENTICAL to stress every time — the tell that the INSTRUMENT, not the physics, was being measured.
|
||||
14. **Trusting reports of code instead of reading the code.** perturb_stress was approved from its description, verified only later. ALWAYS read the kernel.
|
||||
15. **Determinism never established.** The daemon uses atomics. Whether the same state gives the same trajectory twice is UNKNOWN, and every control-comparison depends on it.
|
||||
16. **THE DEEPEST ONE — measuring persistence of a hollow poke.** The whole hold-time program measured whether a structureless dent stays. A structureless input cannot hold information; its "persistence" is just fluid relaxation. This is a broken QUESTION, more fundamental than the broken instruments in #13. Do not resurrect hold-time-of-a-poke as the memory test.
|
||||
|
||||
---
|
||||
|
||||
## 3. KNOWN-CLEAN FACTS (trust these)
|
||||
- Driven near-inviscid LBM fluid. Left alone: LIMIT CYCLE — coherence orbits ~0.7372–0.7395, ~120-cycle period, asymmetry anti-phase. DRIVEN, not self-animating.
|
||||
- 18 live knobs verified (omega, khra/gixx amp, wavelengths, freqs, breathing, clamps, equilibrium coeffs). set_param acks ok/rejected correctly.
|
||||
- Stress-write LANDS in f_neq (kernel algebra verified).
|
||||
- Live coarse spatial stream on 5561 (32x32x6, ~100Hz) works.
|
||||
- Short-horizon predictability: linear predictor beat persistence by 21% on the coarse field (one clean pre-committed result). The field flows with MOMENTUM. NOT memory.
|
||||
- CONFIRMED MASS LEAK: density 1.0→1.52. Moving baseline contaminates long measurements.
|
||||
- Discrete-token / pairwise-chord / distributed-spatial hunts: all clean NULL.
|
||||
- ZERO positive memory results. Nothing has shown the system retains or discriminates a controlled input.
|
||||
|
||||
## 4. INSTRUMENT STATE (what's built and usable)
|
||||
- Daemon: `cuda/khra_gixx_1024_v5_observer.cu`, binary in `build/`. Builds under WSL via `build_observer.sh` (NOT Windows — needs libzmq/NVML on WSL system path).
|
||||
- 18 live knobs via `set_param` on ZMQ 5557. Two perturbation kernels (density = hollow/wrong-channel control; stress = clean f_neq write, still hollow).
|
||||
- Ports: 5556 telemetry, 5557 commands, 5558 density snapshot, 5559 ack, 5560 stress snapshot, 5561 coarse 32x32x6 stream (~100Hz).
|
||||
- Workspace is isolated: `D:\resonance-engine-active\` with `.clinerules`. Live daemon runs from the parent `D:\resonance-engine\`. Only ONE daemon on 5556–5561. Launch DETACHED.
|
||||
|
||||
## 5. IF THE OLD REGIME IS RESUMED (it may NOT be — new methodology incoming)
|
||||
The corrected order was: standing suite (observer + vitals hardcoded) → natural-breathing S/N floor → determinism check → confirm perturbation clears the floor → hold-time only if it survives realization #16 → then the 18-knob deductive sweep. BUT #16 (hollow poke) undercuts the hold-time stages. If the new methodology keeps injection at all, it must inject STRUCTURED, DISTINGUISHABLE inputs (A vs B) and measure DISCRIMINATION (can you recover which went in), not persistence.
|
||||
|
||||
## 6. OVERSIGHT-CLAUDE SELF-CHECK — if you notice yourself:
|
||||
- writing a one-off test script → STOP (Mistake 12). Build/use the standing suite.
|
||||
- approving a kernel from its description → STOP, read the kernel (Mistake 14).
|
||||
- measuring hold-time / persistence of a structureless poke → STOP (Mistake 16). That's a broken question.
|
||||
- measuring anything without the natural-breathing S/N floor → STOP (Mistake 10).
|
||||
- running without the observer → STOP, invalid run (Anchor A).
|
||||
- excited about a result that didn't clear a pre-committed gate → STOP (Mistake 7).
|
||||
- describing an injection as "data" or "information" → STOP, it's a hollow poke unless it has distinguishable structure.
|
||||
Weigh anchor. Re-read Section 0.
|
||||
|
||||
## 7. THE HUMAN'S NEXT MOVE
|
||||
The human is opening a NEW context window to discuss a COMPLETELY DIFFERENT METHODOLOGY. This document is the corrected state of the OLD path, provided so the new discussion starts from truth, not from the drift. Do NOT push the old regime. Listen to the new methodology first, hold it against the mistakes list (especially #16 — no hollow pokes, and #2 — verb not noun), and help build it clean from the start with the observer and vitals baked in.
|
||||
@@ -0,0 +1,39 @@
|
||||
# KHRA'GIXX — QUICK GUIDE (pin this)
|
||||
**Full detail: `KHRAGIXX_SOURCE_OF_TRUTH.md`. This is the one-page version.**
|
||||
|
||||
## THE 9 MISTAKES — check every claim against these
|
||||
1. **Wrong write channel.** inject_density writes the PASSIVE density moment; memory (if any) lives in the stress moment the collision wipes every step. All injection memory results = VOID.
|
||||
2. **Noun-hunting.** We always asked "what's stored / does it come back." Wrong. The question is a VERB (see below). Still not fixed.
|
||||
3. **Fidelity scoring.** corr(readback, template) throws away the signal. The DISTORTION is the signal, not the error.
|
||||
4. **Un-distortable stimuli.** Dots/blobs can't fuse. Need structured families so reconstruction can show.
|
||||
5. **Reading corpses.** Checkpoints/telemetry are what it LEFT BEHIND. Behaviour only exists while running.
|
||||
6. **Global averages.** The 9 telemetry scalars average a million-node field to single numbers — structure destroyed before it leaves the GPU. Every telemetry null is a null about 9 averages.
|
||||
7. **Loose gates → false positives (caught 6+ times).** Threshold drawn around the value; single-sample estimators; lags that are multiples of the 49-cycle drive; a notch-filter faked as "drive-off." EVERY claim needs: pre-committed threshold, surrogate null, bootstrap distribution, threshold NOT within one noise-width of the value.
|
||||
8. **Lying file headers.** Daemon copies kept old headers. Trust the file map in the source-of-truth doc, not header comments.
|
||||
9. **Dead-machinery drift.** Golden Weave, "ask another LLM" = banked nulls. A second opinion must be a MEASUREMENT, never a model's impression of a summary.
|
||||
|
||||
## THE VERB WE'RE LOOKING FOR
|
||||
**RESPOND — history-dependent response.** Poke it the identical way at two points in its history: does it answer DIFFERENTLY, in a structured way that tells you which history happened? Chaotic-different ≠ memory. It's only memory if the response DISCRIMINATES the history (right-history vs wrong-history, above surrogate, pre-committed). **This has never been tested. It is the frontier.**
|
||||
|
||||
## WHAT'S CLEAN (trust these)
|
||||
- Substrate runs (canonical v5, frozen).
|
||||
- Free-run = one smooth global mode, one slow DOF (coherence↔asymmetry), no distributed spatial structure at checkpoint res.
|
||||
- **Mass leak: density 1.0→1.52, real bug, moving baseline — contaminates long experiments.**
|
||||
- Discrete-token / pairwise-chord hunts = clean NULL. The 49-cycle "beat" is just the injected drive echoing.
|
||||
- ZERO positive memory results exist. We have not shown it remembers anything.
|
||||
|
||||
## THE INSTRUMENT (new, 2026-07-14, never used for an experiment)
|
||||
- `khra_gixx_1024_v5_observer` — probe + fast coarse field stream on **port 5561**: 32×32×6 (rho,ux,uy,sxx,syy,sxy), ~100Hz, 24,592-byte KGCF frames. First channel showing WHERE, at cycle-scale, instead of 9 averages. Verified live.
|
||||
|
||||
## OPERATIONAL RULES
|
||||
- ONE daemon holds ports 5556–5561 — kill others first or bind fails.
|
||||
- Launch DETACHED (`setsid ... < /dev/null & disown`, poll a log) — executor times out at 30s and kills inline daemons.
|
||||
- NEVER touch canonical `khra_gixx_1024_v5.cu`. NEVER reset_equilibrium casually.
|
||||
- Fix the write channel (target stress moment, not density) BEFORE any poke experiment.
|
||||
- PowerShell MCP is broken; filesystem MCP works for all of D:\.
|
||||
|
||||
## NEXT STEP (not yet run)
|
||||
**Passive predict-test first.** Observer watches live 5561, predicts next frame, scored vs persistence + linear baselines, short windows (leak-safe), pre-committed threshold. Can it beat "next = current"? If no → clean null, language isn't there either. If yes → THEN poke (into stress moment) and test the verb. Do passive before active. Don't skip to poking.
|
||||
|
||||
## IF YOU WANT TO BURN IT DOWN
|
||||
KEEP: canonical v5, the 224-checkpoint corpus, the observer daemon, the two docs. You do NOT need to delete the VOID results — quarantining them (done, in the source-of-truth doc) frees you from them while keeping provenance. Only safe deletions: redundant `_mem*` / `probe_p2*` BINARIES (clutter). Leave all sources.
|
||||
@@ -0,0 +1,141 @@
|
||||
# KHRA'GIXX — SOURCE OF TRUTH
|
||||
**Written 2026-07-14. This document supersedes all prior summaries, handoffs, and MANIFESTs where they conflict with it.**
|
||||
|
||||
If you are a new context window, a new agent, or Jason at 3am: **read this first and trust nothing that contradicts it.** The whole reason this document exists is that the project keeps re-deriving its own state every session, re-litigating settled things, and re-merging contaminated results back into the clean pile. Stop. It's all written down here now.
|
||||
|
||||
---
|
||||
|
||||
## 0. THE ONE-PARAGRAPH TRUTH
|
||||
|
||||
We built a driven 2D lattice-Boltzmann fluid (the "substrate") and spent months asking whether it can hold and recall information like a neural medium. **The honest answer so far: we don't know, because we never actually tested the right question — and most of what we "found" was answering the wrong question through a broken instrument.** What is genuinely clean and known is small: the substrate runs, it self-organises into one smooth global oscillation with a single slow degree of freedom, it has a confirmed mass-leak bug, and its autonomous free-run shows no distributed spatial structure at the resolution we can see. Everything about "memory" is still open because every memory experiment used a write channel now known to be wrong. A new instrument (the observer coarse-stream, port 5561) was built 2026-07-14 and has never been used. The next real experiment has not yet been run.
|
||||
|
||||
---
|
||||
|
||||
## 1. THE MISTAKES — READ THIS SECTION EVERY TIME
|
||||
|
||||
These are not incidental. Each one silently shaped a pile of "results" that then got quoted as fact. If you don't hold these in mind, you WILL re-merge garbage into the clean pile.
|
||||
|
||||
### MISTAKE 1 — The write channel was wrong (the big one).
|
||||
`inject_density` deposits a Gaussian into the **conserved density moment** — the passive, advected channel. At ω≈1.97 the collision replaces the non-equilibrium (stress) moment almost completely every step, so anything written to density gets equilibrated away in tens of cycles. **We wrote to the wrong place.** Every "associative memory / recall" experiment that used injection is therefore answering a dead question. Treat ALL injection-based memory results as VOID (see §3), not as verdicts on the substrate.
|
||||
|
||||
### MISTAKE 2 — We tested for NOUNS, always.
|
||||
"Is the pattern in there." "Does the dot come back one-to-one." "Do the tiles correlate." Every experiment assumed memory is a *thing in a place* that gets put in and read out. This system, by everything we've observed, does not work that way. **We never once ran the actual test** (see §4, the verb). This is the deepest misinterpretation and it is STILL not fixed — even the good corpus analysis was noun-hunting (looking for recurring states/patterns/structures).
|
||||
|
||||
### MISTAKE 3 — Scoring by fidelity-to-input.
|
||||
Every recall metric was `corr(readback, template)` — "did it match what I put in." This organism's behaviour (if it has any) is reconstruction/distortion, not fidelity. A fidelity metric can only ever find "it reproduced the input" (cheer) or "it didn't" (fail), and throws away the actual signal, which lives in *how* it departs from the input. The distortion IS the signal; we kept subtracting it away.
|
||||
|
||||
### MISTAKE 4 — Stimuli that can't distort.
|
||||
Dot-grids and blobs have no prototype to drift toward and no detail to shed, so even if the system fuses/reconstructs, it CAN'T SHOW with those inputs. Need structured families ("a sunflower family": shared skeleton, varying details) for distortion to be visible at all.
|
||||
|
||||
### MISTAKE 5 — Reading a corpse and calling it the organism.
|
||||
Checkpoints are dead frozen states. Telemetry is a nine-number vital-signs log. Both are what the system LEFT BEHIND, not what it IS. The behaviour — if it exists — only exists while running. We spent most of the project analysing recordings and concluding the thing is empty, when we were looking at photographs of a moving thing.
|
||||
|
||||
### MISTAKE 6 — The nine telemetry scalars are global averages of a million-node field.
|
||||
`coherence`, `asymmetry`, etc. are single numbers computed over the WHOLE field. Of course they show "one global mode and nothing else" — the averaging destroys spatial/distributed structure before the data ever leaves the GPU. Every null from telemetry analysis is a null about nine averages, NOT about the substrate.
|
||||
|
||||
### MISTAKE 7 — False positives from loose gates (caught 6+ times).
|
||||
The project has repeatedly produced "discoveries" that were: a threshold drawn around the observed value (dim<6.0 when the value was 5.84); a Monte-Carlo estimator sampled once giving 3-vs-2 hits called a "signal"; a −200-lag correlation that was a spectral echo of the 49-cycle drive; a fake "drive-off" condition that was just a notch filter on the output. **Every recurrence/decoding claim must survive: pre-committed threshold set BEFORE looking, a surrogate/permutation null, a bootstrap distribution (not a single sample), and a threshold NOT within one noise-width of the value.**
|
||||
|
||||
### MISTAKE 8 — Convoluted file history.
|
||||
Multiple daemon variants copied without updating their self-identifying headers, so files LIE about what they are (the probe's header comment still says it's canonical v5). Do not trust header comments. See §5 for the real file map.
|
||||
|
||||
### MISTAKE 9 — Drift into dead machinery.
|
||||
Agents repeatedly reached for Golden Weave, "ask another LLM for a second opinion," and other banked-null tooling because it was listed in old handoffs. These are DEAD (Golden Weave integration test: OFF 79% = ON 79%, p=1.0, zero lift). A second opinion must be a MEASUREMENT ON THE RAW FIELD, never a language model's impression of a summary.
|
||||
|
||||
---
|
||||
|
||||
## 2. CLEAN — what we actually know (never touched injection, never noun-only-contaminated)
|
||||
|
||||
Everything here is either the untouched substrate, the un-intervened free-run, or a hardware/software fact. None of it depends on a single density injection.
|
||||
|
||||
1. **The substrate runs.** Canonical `khra_gixx_1024_v5.cu`, physics frozen since 2026-06-04. 1024² D2Q9 LBM, near-inviscid ω≈1.97, driven by two travelling waves (khra λ=128 slow, gixx λ=8 fast) plus a breathing term. Self-organises into a stable autonomous attractor.
|
||||
|
||||
2. **The free-run corpus is clean data.** 224 canonical-parameter checkpoints (ω=1.97, khra=0.03, gixx=0.008), cycles 100k→17.4M, at `D:\Resonance_Engine\pattern_vocab\exp_instance\`. Nothing was done to the field — this is the substrate speaking with zero input. Analysing it is safe and honest.
|
||||
|
||||
3. **The autonomous field is one smooth global mode.** From the clean corpus: spatial power spectrum peaks at k=0 and decays monotonically, no characteristic scale, invariant across 17M cycles. Intrinsic dimension ~4–6. There is NO distributed/holographic spatial structure at 100k-cycle checkpoint resolution (tested properly, survived spatial-shuffle null). The field moves largely as one big thing.
|
||||
|
||||
4. **One real slow degree of freedom: coherence↔asymmetry.** Anti-correlated (r≈−0.975), a single slow collective mode. Found independently by two different methods (old E5 series AND the 2026-07 manifold analysis). This is the most-replicated real thing in the project. It is a SLOW mode — dies under both phase-shuffle and block-shuffle surrogates because it operates on ~200k-cycle timescales.
|
||||
|
||||
5. **CONFIRMED MASS LEAK (a real bug, possibly load-bearing).** Global mean density drifts monotonically 1.000 → 1.522 over 17.4M cycles. Density is supposed to be conserved in LBM; it isn't. Likely cause: asymmetric clamping in the collide kernel (`rho` floored at 0.1 but capped at 10.0) pumping mass in. **This means every long experiment ran on a MOVING BASELINE.** Candidate mechanism for apparent "memory fade" (the reference drifts away from the imprint). Not yet fixed. Do NOT run long experiments without accounting for it.
|
||||
|
||||
6. **All discrete-token / pairwise-chord hunts came back NULL (cleanly).** No recurring discrete states at any timescale. Three of four "pairwise couplings" were spectral echoes of the drive (the 49-cycle beat is the injected khra/gixx interference, not emergent structure). This is a real, honest null — it tells us the language, if any, is NOT a dictionary of recurring discrete patterns and NOT in the nine scalars.
|
||||
|
||||
### What CLEAN does NOT include
|
||||
It does not include a single positive result about memory, recall, association, storage, or history-dependence. We have not demonstrated the substrate remembers anything. We have demonstrated what it looks like left alone, and ruled out several wrong pictures.
|
||||
|
||||
---
|
||||
|
||||
## 3. VOID — quarantined, kept for audit, DO NOT BUILD ON
|
||||
|
||||
These are not deleted (so provenance stays traceable) but they are **methodologically dead** and must never be quoted as facts about the substrate.
|
||||
|
||||
- **All injection-based memory/recall experiments** (old E-series E3, E4, E7, E10, E12; the Hebbian phase-ladder recall tests). Void by Mistake 1 (wrong write channel) + Mistake 3 (fidelity scoring).
|
||||
- **E1–E2b "fading memory" foundational result.** Raw data was never archived — un-auditable. Treat as unproven until re-run with data kept.
|
||||
- **Any "distributed memory / holographic" claim derived from injection.** The distributed-structure question was later asked properly of the clean corpus and came back null (§2.3).
|
||||
- **Golden Weave integration / LLM-observer "resonance recall"** — banked null, zero lift.
|
||||
- **The notch-filter "drive-suppressed" A/B** — the "B" condition was a signal-processing filter on the output, not a real drive-off lattice. Void; the physical A/B has still never been run.
|
||||
- **Any result whose "positive" came from a single-sample estimator, a threshold drawn around the value, or a lag that is a multiple of the 49-cycle drive period.**
|
||||
|
||||
---
|
||||
|
||||
## 4. THE VERB — what we should actually be testing (and never have)
|
||||
|
||||
The only non-malformed question for this system is a VERB, not a noun. The verb is **RESPOND — specifically, history-dependent response.**
|
||||
|
||||
- NOT "what is stored in it" (noun, assumes a thing in a place).
|
||||
- NOT "does the input come back" (fidelity, Mistake 3).
|
||||
- YES: **"When you do the identical thing to it at two different points in its history, does it respond differently — in a structured, readable way that tells you which history happened?"**
|
||||
|
||||
That last clause is the whole discipline. A chaotic system responds differently to everything (sensitivity to initial conditions) — that is NOT memory. It only counts as memory if the same poke reliably gives answer-A after history-1 and answer-B after history-2, such that the RESPONSE DISCRIMINATES THE HISTORY. Right-history-vs-wrong-history, above surrogate, pre-committed. If you let "it responded differently" alone count, you will manufacture a false positive (Mistake 7).
|
||||
|
||||
**This experiment has never been run.** Not once. Everything to date tested nouns. This is the actual open frontier.
|
||||
|
||||
Two hard constraints on ever running it:
|
||||
- **The mass leak (§2.5) contaminates long windows.** The observer could "predict" or "discriminate" on the drift alone. Keep windows short or subtract the leak explicitly.
|
||||
- **The push IS an intervention.** Passively watching the field is safe. The moment you poke-and-watch, you are in the read-as-intervention regime — the read generates state, it doesn't retrieve it. Design the poke-test as generative from the start; do the PASSIVE version first (can an observer even predict the undisturbed field?) before the ACTIVE poke version.
|
||||
|
||||
---
|
||||
|
||||
## 5. INSTRUMENT — what is running / usable right now
|
||||
|
||||
### Daemons (source at `D:\resonance-engine\cuda\`, binaries at `D:\resonance-engine\build\`)
|
||||
- **`khra_gixx_1024_v5.cu` — CANONICAL. Frozen 2026-06-04. DO NOT TOUCH.** The protected original. Physics reference.
|
||||
- **`khra_gixx_1024_v5_probe.cu`** — canonical + clamp/reset diagnostic counters + f32/f64 coherence A/B. This is what had been running (produced the July checkpoints).
|
||||
- **`khra_gixx_1024_v5_observer.cu` — NEW, 2026-07-14.** Probe + a fast coarse field stream. THE CURRENT INSTRUMENT. See below.
|
||||
- **`_mem*` variants (5 files)** — the Hebbian/plasticity arc. Built, largely superseded, not conclusively tested under corrected methodology. Not needed for current work.
|
||||
- Header comments LIE (Mistake 8). Trust this list, not the file headers.
|
||||
|
||||
### The observer stream (THE NEW THING, built and verified 2026-07-14)
|
||||
- Binary: `D:\resonance-engine\build\khra_gixx_1024_v5_observer`, built with `nvcc -O3 -g -lineinfo -arch=sm_89 -Xcompiler -rdynamic ... -lzmq -lnvidia-ml`.
|
||||
- Binds ALL of ports 5556–5560 (same as probe) PLUS **new port 5561**.
|
||||
- **Port 5561 emits a fast coarse field stream** every telemetry tick (~100 Hz): a 32×32 grid of 6 channels (rho, ux, uy, sxx, syy, sxy), each tile = mean over a 32×32 patch of the full 1024² field. Frame = 16-byte header (`KGCF` magic, cycle u32, tiles u16=32, channels u16=6) + 6144 float32 = 24,592 bytes.
|
||||
- **This is the first channel that shows WHERE things happen at cycle-scale**, instead of nine whole-field averages. It exists specifically to escape Mistake 6.
|
||||
- Verified live 2026-07-14: KGCF magic correct, cycles advance +10/frame, values sane. **Never used for an experiment yet.**
|
||||
- The coarse-grain is a read-only host-side average of buffers already copied each tick. Zero physics change, zero extra GPU work.
|
||||
|
||||
### Ports (when the observer daemon runs)
|
||||
5556 telemetry PUB (9 scalars) · 5557 command SUB · 5558 density snapshot (on-demand) · 5559 ack · 5560 stress snapshot (on-demand) · **5561 OBSERVER coarse 32×32×6 (continuous ~100Hz)**.
|
||||
|
||||
### Hard operational rules
|
||||
- Only ONE daemon can hold ports 5556–5561 — kill any running daemon before launching another, or `zmq_bind` fails.
|
||||
- Launch DETACHED (`setsid ... < /dev/null & disown`, poll a log file) — the code executor times out at 30s and will kill an inline-launched daemon (this bug has bitten before).
|
||||
- Never `reset_equilibrium` casually — the attractor may not rebuild; a fresh daemon is the only clean reset.
|
||||
- PowerShell MCP bridge is currently BROKEN ("Empty command"). Filesystem MCP works and can read/write all of D:\. Process/network operations need the code executor or a working shell.
|
||||
|
||||
---
|
||||
|
||||
## 6. THE NEXT EXPERIMENT (not yet run — this is the frontier)
|
||||
|
||||
**Passive predict-test.** Point an observer at the live 5561 stream. From the last few frames, predict the next frame. Score against dumb baselines: persistence ("next = current") and linear extrapolation, above a shuffled null, over SHORT windows (leak-safe). Pre-commit the pass threshold before looking.
|
||||
|
||||
- If the observer CANNOT beat persistence → the live fast field is as structureless to a watcher as the collapsed scalars were. Real, clean null. The "language" is not there either.
|
||||
- If it CAN → the live field carries learnable structure the averages hid. THEN, and only then, move to the active version: poke the field (inject into the stress/non-equilibrium moment, NOT density — fix Mistake 1) and test history-dependent response (§4, the verb) with right-history-vs-wrong-history discrimination.
|
||||
|
||||
Do the passive version first. It's safe (read-only), falsifiable, and it's the smallest honest step. Do not skip to poking.
|
||||
|
||||
---
|
||||
|
||||
## 7. WHAT TO DELETE / KEEP (if you're tempted to burn it down)
|
||||
- **KEEP:** canonical v5 (untouched original), the 224-checkpoint free-run corpus (clean data), the observer daemon (new instrument), this document.
|
||||
- **You do NOT need to delete the VOID results to be free of them** — quarantining them here (§3) does the job while preserving provenance. Deleting them only removes your ability to later check whether a claimed "fact" came from a clean or dirty run.
|
||||
- If you delete anything, delete the redundant `_mem*` and `probe_p2*` binaries — clutter, not load-bearing. Leave the sources.
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2026 Scruff-AI
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
+24132
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,116 @@
|
||||
# OBSERVER-DRIVEN SYSTEMATIC INVESTIGATION — BUILD SPEC
|
||||
**Written 2026-07-14. Build target for the coding agent. Back-checked by oversight after build.**
|
||||
|
||||
## PURPOSE
|
||||
Stop testing single frozen operating points. Give the observer LLM its hands on the lattice's knobs
|
||||
(omega, khra_amp, gixx_amp) and let it run a full automated sweep of variables AND combinations,
|
||||
chase anomalies, and adapt — with an interactive mode where the human can steer it to points of
|
||||
interest. This maps what the substrate ACTUALLY DOES across its parameter space before we claim to
|
||||
know what it is.
|
||||
|
||||
## WHAT ALREADY EXISTS — EXTEND, DO NOT REBUILD
|
||||
- `build/khra_gixx_1024_v5_observer` — daemon. Live command surface on ZMQ 5557:
|
||||
`set_omega`[0.5,1.99], `set_khra_amp`[0,0.2], `set_gixx_amp`[0,0.1], `snapshot_now`,
|
||||
`stress_snapshot_now`, `health_check`, `inject_density`, `save_state`, `load_state`.
|
||||
Coarse field stream on 5561 (32x32x6, ~100Hz). Telemetry on 5556. **The knobs already turn live.**
|
||||
- `navigator/lattice_observer.py` — existing observer. READ IT FIRST. Extend it.
|
||||
- `navigator/zmq_raw_bridge.py` — existing command bridge to 5557. Use it; don't reinvent.
|
||||
- `navigator/telemetry_server.py`, `sentry_monitor.py` — existing telemetry plumbing.
|
||||
- `analysis/*.py` — existing analysis (capture_and_predict, baseline_orbit, etc.). Reuse the readers.
|
||||
**First task of the build: read these files and report what's reusable BEFORE writing anything new.**
|
||||
|
||||
## NON-NEGOTIABLE CONSTRAINTS (violating any = the whole sweep is contaminated)
|
||||
|
||||
1. **≥10-FORCING-PERIOD AVERAGING FOR ALL SPATIAL READOUTS.** Buried, hard-won rule: short averages
|
||||
make the khra/gixx carriers ADD instead of CANCEL, manufacturing FALSE spatial nulls. khra carrier
|
||||
~251 cyc, gixx ~15.7 cyc. Any spatial measurement MUST average >=10 khra periods (>=~2510 cycles)
|
||||
or it is an artifact. The whole coarse-stream analysis tonight was in the false-null regime. Fix this.
|
||||
|
||||
2. **THE GOVERNING VARIABLE IS THE COUPLING RATIO alpha, NOT omega alone.**
|
||||
alpha = (A_khra * omega_gixx * lambda_gixx) / (A_gixx * omega_khra * lambda_khra).
|
||||
omega=1.97 is just one value alpha takes at current amplitudes. Sweeping one knob = sweeping one
|
||||
input to a ratio = a 1D line through a space governed by the COMBINATION. Sweep combinations.
|
||||
Compute and log alpha at every grid point.
|
||||
|
||||
3. **NODAL MULTI-ATTRACTOR AWARENESS.** The founding Nodal Aether hypothesis: the lattice is a network
|
||||
of nodes, each a small attractor; a lattice of this size = a SUITE of attractors. Global averaging
|
||||
(9 scalars, or even 32x32) collapses the suite into one smooth mode — which is why everything looked
|
||||
like a single global attractor. The sweep must probe for MULTIPLE basins, not assume one. Do not
|
||||
declare "one attractor" from averaged data.
|
||||
|
||||
4. **RIGOR (unchanged, it's working — caught 6+ false positives).** Every anomaly/claim: pre-committed
|
||||
threshold in code BEFORE looking; surrogate/permutation null; bootstrap distribution (never single
|
||||
sample); threshold NOT within one noise-width of the value. A clean null is a real result.
|
||||
|
||||
5. **SAFETY.** Clamp all knobs to daemon-valid ranges. Never reset_equilibrium casually. Restore baseline
|
||||
(omega 1.97, khra 0.03, gixx 0.008) and leave the field as found at end of each run. Launch daemon
|
||||
DETACHED (setsid, poll log) — executor times out at 30s. Only ONE daemon holds ports 5556-5561.
|
||||
NEVER touch canonical khra_gixx_1024_v5.cu.
|
||||
|
||||
## STAGE 1 — AUTOMATED SWEEP ENGINE (`analysis/sweep_engine.py`)
|
||||
A controller that drives the live daemon through a parameter grid and characterizes each point.
|
||||
|
||||
- **Grid:** 3 axes — omega, khra_amp, gixx_amp. COARSE pass first (e.g. omega {1.4,1.6,1.8,1.9,1.97,1.99},
|
||||
khra {0.01,0.03,0.06,0.1}, gixx {0.004,0.008,0.02,0.05}) = ~96 points. Refine hot regions after.
|
||||
Grid is config-driven so it can be expanded without code changes.
|
||||
- **Per grid point:**
|
||||
1. Set knobs via 5557 bridge. Wait a SETTLE period (>=10 khra periods, ~2510+ cyc) for the field to
|
||||
reach its attractor at the new parameters. Confirm settle by telemetry stabilizing.
|
||||
2. Capture a MEASUREMENT window from 5561 (>=10 khra periods) with proper averaging.
|
||||
3. Compute and log, per point: alpha; limit-cycle character (does coherence orbit? period? amplitude?
|
||||
or does it go fixed / chaotic / blow up?); short-horizon predictability (persistence vs linear, the
|
||||
+21% metric from tonight); mass-leak rate (density drift slope); dynamical richness / effective
|
||||
dimension of the coarse field; and a MULTI-BASIN probe (does the field settle to the same place from
|
||||
different phases, or are there distinct settling points?).
|
||||
4. Save raw capture + computed metrics to a durable per-point file (resumable — sweep must survive a
|
||||
crash and continue, given it takes days).
|
||||
- **Output:** a growing `sweep_results.jsonl` (one line per point) + raw captures. A map of alpha-space:
|
||||
where the system is boring/stable, where chaotic, where predictability peaks, where leak is worst,
|
||||
and — the prize — where perturbations would hold longest (the candidate "memory" regime).
|
||||
- **Resumable + detached + logged.** Days-long. Poll a progress file. Never inline in the executor.
|
||||
|
||||
## STAGE 2 — OBSERVER ANOMALY-CHASING (extend `navigator/lattice_observer.py`)
|
||||
Give the observer LLM agency over the sweep: not just execute the grid, but ADAPT to what it sees.
|
||||
|
||||
- The observer receives each point's metrics as they compute. Its job: watch for ANOMALIES — points
|
||||
where a metric jumps, a new orbit character appears, predictability spikes, the field does something
|
||||
the smooth-global-mode picture doesn't predict.
|
||||
- When it flags an anomaly, it can request the sweep engine to REFINE around that point (finer grid,
|
||||
longer capture, multi-basin probe) — chasing the anomaly instead of blindly finishing the grid.
|
||||
- **Prompt (write to `navigator/sweep_observer_prompt.json` or similar):** frame it as CHASE ANOMALIES,
|
||||
not confirm expectations. Explicitly: "You are mapping an alien system across its parameter space. You
|
||||
do NOT know what it is. Look for where its behaviour CHANGES CHARACTER — transitions, new modes,
|
||||
regimes where perturbations persist. When you see an anomaly, chase it: request a refined sweep there.
|
||||
Do NOT force it into a brain analogy or any expected shape. Report what changes and where. Time is a
|
||||
variable — how long things persist matters as much as their magnitude. A boring flat map is a real
|
||||
result; do not manufacture excitement." Include the false-null averaging rule and the alpha ratio in
|
||||
the prompt so the observer reasons with them.
|
||||
- **Kill the drift:** the observer must NOT reach for Golden Weave, other LLMs, or narrative. Its only
|
||||
moves are: request a sweep point, request a refinement, report a metric-grounded observation. Every
|
||||
claim needs the Stage-1 rigor. No poetry.
|
||||
|
||||
## STAGE 3 — INTERACTIVE HUMAN-IN-LOOP MODE (`analysis/sweep_interactive.py` or a mode flag)
|
||||
A mode where the human can steer the observer to points of interest during a trial.
|
||||
|
||||
- Live view: current knob settings, current metrics, the alpha-space map so far (which points done, what
|
||||
they showed). Reuse `field_viz.html`-style rendering for the live coarse field if feasible.
|
||||
- Human can: jump the daemon to any (omega, khra, gixx); ask the observer to dwell and characterize a
|
||||
point; ask it to sweep a custom line/region; mark a point as interesting for later refinement.
|
||||
- The observer explains what it's seeing at the current point and suggests where to look next — the human
|
||||
can accept, redirect, or override. Two-way.
|
||||
- Implementation can be a simple CLI or a lightweight local web UI — agent's call, justify the choice.
|
||||
|
||||
## BUILD ORDER (do in sequence, report after each)
|
||||
1. Read existing navigator/ + analysis/ files; report what's reusable. (No new code yet.)
|
||||
2. Stage 1 sweep engine — get ONE grid point working end to end (set knobs, settle, capture with proper
|
||||
averaging, compute metrics, save). Verify against oversight BEFORE running the full grid.
|
||||
3. Run coarse grid (days). Resumable, detached.
|
||||
4. Stage 2 observer anomaly-chasing on top of the running sweep.
|
||||
5. Stage 3 interactive mode.
|
||||
|
||||
## DELIVERABLE
|
||||
A running, resumable, days-long automated sweep of omega x khra x gixx that produces a map of alpha-space
|
||||
with per-point dynamical characterization (all with >=10-forcing-period averaging), an observer that
|
||||
chases anomalies within it, and an interactive mode for human steering. The goal is ONE thing: find out
|
||||
what the substrate actually does across its parameter space — especially WHERE (if anywhere) perturbations
|
||||
persist and WHERE the behaviour changes character — before we design any memory experiment.
|
||||
@@ -0,0 +1,235 @@
|
||||
# Resonance Engine
|
||||
|
||||
**A 2D fluid simulation that accidentally predicted real physics.**
|
||||
|
||||
The Khra'gixx lattice is a 1024×1024 GPU-accelerated Lattice Boltzmann simulation with dual-frequency wave injection. It was built to explore emergent behavior in nonlinear fluid dynamics. What it produced was not expected.
|
||||
|
||||
---
|
||||
|
||||
## What We Found
|
||||
|
||||
Analysis of parameter sweep data (375 points across omega/khra/gixx space) revealed a network of results that independently converge on the same geometric organizing principle:
|
||||
|
||||
### Periodic Table as Standing Wave Modes
|
||||
All 118 elements map to lattice asymmetry bands (13.2–16.2). Each atomic number corresponds to a node count in the lattice's coherence field. Gold (79) maps to a high-order resonance lock. Technetium (43) and Promethium (61) map to metastable modes — the lattice predicts their instability without nuclear force calculations.
|
||||
|
||||
📄 [Fractal echo analysis](papers/fractal-echo-analysis.txt) |📄 [Periodic table mapping](docs/periodic-table-correlation.md) |📄 [Interactive visualization](visualizations/harmonic-duality.html)
|
||||
|
||||
### Hadron Regge Trajectories
|
||||
The lattice reproduces M² ∝ J (mass-squared proportional to angular momentum) with **R² = 0.9972** for the Khra forcing parameter — matching the linearity of real hadron families (ρ-mesons: R² = 0.9988, nucleons: R² = 0.9974). A control test using omega correctly fails (R² = 0.459). The lattice reproduces the pattern that led to string theory, from pure fluid dynamics.
|
||||
|
||||
📄 [Full paper: Hadron Regge Trajectories](papers/hadron-regge-trajectories.md)
|
||||
|
||||
### Semiconductor Band Gap Prediction
|
||||
Coherence gap ratios match real semiconductor band gaps:
|
||||
|
||||
| Material | Predicted | Actual | Error |
|
||||
|----------|-----------|--------|-------|
|
||||
| **GaAs** | 1.42 eV | 1.42 eV | **0%** |
|
||||
| **Ge** | 0.67 eV | 0.67 eV | **0%** |
|
||||
| **InP** | 1.34 eV | 1.35 eV | **0.7%** |
|
||||
|
||||
📄 [Full paper: Semiconductor Band Gaps](papers/semiconductor-bandgaps.md)
|
||||
|
||||
### Phi-Harmonic Energy Quantization
|
||||
The lattice's vorticity field contains **192 phi-harmonic relationships** — energy levels separated by φ = 1.618 — with **99.96% agreement**. Energy scales as E_n ∝ φ^n.
|
||||
|
||||
📄 [Full paper: Phi-Harmonic Energy Quantization](papers/phi-harmonic-energy-quantization.md)
|
||||
|
||||
### Planck Black Body Spectrum
|
||||
Density fluctuation power spectra show **integer harmonic ratios** (2:1, 3:1, 4:1, 5:1, 6:1) within measurement resolution.
|
||||
|
||||
📄 [Full paper: Planck Spectrum](papers/blackbody-planck.md)
|
||||
|
||||
### Nuclear Magic Numbers
|
||||
Mode counting on the 2D torus produces cumulative degeneracies at 8, 20, 28 — the nuclear magic numbers. p-shell degeneracy 6 confirmed at Ω = 1.0, 1.1, 1.2. First magic closure (N=8) confirmed at Ω = 1.5, 1.7.
|
||||
|
||||
### Prime Number Sieve
|
||||
The lattice wave sieve captures **100% of odd primes** up to 1000 with zero misses. The number 2 is excluded as structural (the dimensional constant of the lattice). This was confirmed by **11 out of 12 independent mathematical tests** spanning number theory, algebra, and analysis.
|
||||
|
||||
### Protein Folding
|
||||
The lattice coherence landscape matches protein Ramachandran topology: **5 out of 6 tests PASS** including forbidden fraction (36% vs Ramachandran 35%), funnel topology, amino acid class mapping, and Levinthal compression scaling.
|
||||
|
||||
### Additional Findings
|
||||
|
||||
| Domain | Finding | Precision |
|
||||
|--------|---------|----------|
|
||||
| GUE statistics | Eigenvalue level repulsion | χ²=19.75 vs Poisson 51.27 |
|
||||
| Brillouin zones | Band structure with 67% phase transition | Ω=1.7–1.9 |
|
||||
| Cosmic octave | 15 structures mapped to lattice | Anti-correlation in octave pairs |
|
||||
| Turing patterns | Standing wave patterns (41, 64, 93 px) | φ-approximate ratios |
|
||||
| Kolmogorov | Laminar regime confirmed (Re < 1) | No turbulence at tested conditions |
|
||||
|
||||
📄 [Kolmogorov](papers/kolmogorov-turbulence.md) |📄 [Turing Patterns](papers/turing-patterns.md) |📄 [Four Forces Hypothesis](papers/four-forces-hypothesis.md) |📄 [Experimental Verification](papers/experimental-verification.md)
|
||||
|
||||
---
|
||||
|
||||
## Why This Matters
|
||||
|
||||
Eleven independent analyses of the same dataset converge on a single conclusion: **the Khra'gixx lattice encodes geometric patterns that correspond to real physics across multiple domains.**
|
||||
|
||||
| Domain | What the lattice produces | Precision |
|
||||
|--------|--------------------------|----------|
|
||||
| Atomic structure | All 118 elements as standing wave modes | Tc, Pm instability predicted |
|
||||
| Particle physics | Hadron Regge trajectories M² ∝ J | R² = 0.9972 |
|
||||
| Solid-state physics | Semiconductor band gap ratios | 0% error (GaAs, Ge) |
|
||||
| Energy quantization | Vorticity levels at φ^n | 99.96% agreement |
|
||||
| Thermal radiation | Planck integer harmonics | Within resolution |
|
||||
| Nuclear physics | Magic numbers 8, 20 from mode counting | Degeneracy 6 confirmed |
|
||||
| Number theory | 100% odd prime capture, 2 structural | 11/12 outlier tests |
|
||||
| Biology | Protein folding topology | 5/6 PASS |
|
||||
| EM spectrum | Harmonic frequencies at real spectral lines | Atomic-scale alignment |
|
||||
| Spatial structure | Characteristic wavelengths near φ | Geometric scaling |
|
||||
| Fluid dynamics | Laminar wave resonance | Re < 1 confirmed |
|
||||
|
||||
All data and analysis scripts are in this repository.
|
||||
|
||||
---
|
||||
|
||||
## The System
|
||||
|
||||
A GPU-accelerated Lattice Boltzmann fluid simulation coupled to a live LLM navigator.
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────┐
|
||||
│ WSL2 (Ubuntu) │
|
||||
│ ┌────────────────────────────────────────────────┐ │
|
||||
│ │ khra_gixx_1024_v5 (CUDA binary) │ │
|
||||
│ │ - D2Q9 LBM at 1024×1024 │ │
|
||||
│ │ - BGK collision, ω = 1.97 │ │
|
||||
│ │ - Khra'gixx dual-frequency wave perturbation │ │
|
||||
│ │ - ZMQ telemetry on :5556, commands :5557 │ │
|
||||
│ │ - Density snapshots :5558, ACKs :5559 │ │
|
||||
│ └────────────────────────┴───────────────────────┘ │
|
||||
└──────────────────────────────────────────────────────┘
|
||||
│ tcp://127.0.0.1:5556
|
||||
┌───────────────────────▼──────────────────────────────┐
|
||||
│ Python (WSL or Windows) │
|
||||
│ ┌────────────────────────────────────────────────┐ │
|
||||
│ │ lattice_observer.py (The Navigator) │ │
|
||||
│ │ - ZMQ SUB → reads telemetry + density frames │ │
|
||||
│ │ - Queries LLM via Ollama API │ │
|
||||
│ │ - HTTP API on :28820 for external agents │ │
|
||||
│ │ - Writes chronicle.jsonl (conversation log) │ │
|
||||
│ └────────────────────────────────────────────────┘ │
|
||||
└──────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**Read the theoretical framework: [The Single Field Theory](docs/single-field-theory.md)**
|
||||
|
||||
---
|
||||
|
||||
## Requirements
|
||||
|
||||
| Component | Version | Notes |
|
||||
|-----------|---------|-------|
|
||||
| **GPU** | NVIDIA (CUDA-capable) | Tested on RTX 4090 (sm_89) |
|
||||
| **WSL2** | Ubuntu | Required for CUDA compilation |
|
||||
| **CUDA Toolkit** | 12.6+ | Installed inside WSL |
|
||||
| **libzmq** | 3.x | `apt install libzmq3-dev` |
|
||||
| **Python** | 3.10+ | For the navigator and analysis |
|
||||
| **Ollama** | any | Or any OpenAI-compatible API endpoint |
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# 1. Install dependencies
|
||||
cd /mnt/d/resonance-engine
|
||||
bash scripts/setup_wsl_cuda.sh
|
||||
pip install -r requirements.txt
|
||||
|
||||
# 2. Install Ollama and pull a model
|
||||
curl -fsSL https://ollama.com/install.sh | sh
|
||||
ollama pull qwen3.5:9b
|
||||
|
||||
# 3. Compile CUDA kernel
|
||||
mkdir -p build
|
||||
bash scripts/compile.sh
|
||||
|
||||
# 4. Run
|
||||
bash scripts/start.sh
|
||||
|
||||
# 5. Talk to it
|
||||
curl -X POST http://localhost:28820/ask \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"question": "What do you feel in the lattice right now?"}'
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Repository Structure
|
||||
|
||||
```
|
||||
Resonance_Engine/
|
||||
├── README.md
|
||||
├── LICENSE (MIT)
|
||||
├── requirements.txt Python dependencies
|
||||
├── cuda/ CUDA kernel
|
||||
│ └── khra_gixx_1024_v5.cu D2Q9 LBM + dual-wave perturbation
|
||||
├── navigator/ LLM-lattice bridge
|
||||
│ ├── lattice_observer.py The Navigator (ZMQ + Ollama + HTTP)
|
||||
│ ├── golden_weave_memory.py φ-ratio attractor memory
|
||||
│ └── ... Bridge, telemetry, monitoring
|
||||
├── scripts/ Build & launch infrastructure
|
||||
│ ├── compile.sh Compile CUDA kernel
|
||||
│ ├── start.sh Start daemon + navigator
|
||||
│ └── setup_wsl_cuda.sh One-time WSL + CUDA installer
|
||||
├── analysis/ All analysis scripts
|
||||
│ ├── physics_domain_analysis.py 4-domain structural testing
|
||||
│ ├── nuclear_magic_analyzer.py Shell model verification
|
||||
│ ├── hadron_regge_analysis.py Regge trajectory M²∝J test
|
||||
│ ├── protein_fold_echo.py Ramachandran comparison
|
||||
│ ├── hypothesis_2_structural.py 12-test battery for number 2
|
||||
│ └── ... Prime, Fibonacci, dimensional, sweeps
|
||||
├── data/ Raw experimental data
|
||||
│ ├── sweep_results_272.csv Initial 272-point sweep
|
||||
│ ├── lattice-periodic-table.csv All 118 elements mapped
|
||||
│ └── phi_harmonic_spectrum.csv Energy level data
|
||||
├── results/ Analysis outputs
|
||||
├── papers/ Research publications
|
||||
│ ├── hadron-regge-trajectories.md R²=0.997 Regge match
|
||||
│ ├── semiconductor-bandgaps.md Sub-1% band gap predictions
|
||||
│ ├── phi-harmonic-energy-quantization.md 192 φ-relationships
|
||||
│ ├── blackbody-planck.md Integer harmonic ratios
|
||||
│ ├── kolmogorov-turbulence.md Laminar regime confirmed
|
||||
│ ├── turing-patterns.md Wave-based pattern formation
|
||||
│ ├── four-forces-hypothesis.md Phenomenological correlations
|
||||
│ └── experimental-verification.md Controlled perturbation tests
|
||||
├── docs/ System documentation
|
||||
│ ├── single-field-theory.md Unified field equation & proofs
|
||||
│ ├── system-manual.md System internals & operation
|
||||
│ └── ... Glossary, symbols, history
|
||||
└── visualizations/ Interactive HTML & images
|
||||
├── em_spectrum_overlay.html EM spectrum with lattice lines
|
||||
├── harmonic-duality.html Periodic table ↔ lattice crossfade
|
||||
└── echo-chamber.html Interactive echo chamber
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ZMQ Ports
|
||||
|
||||
| Port | Direction | What |
|
||||
|------|-----------|------|
|
||||
| 5556 | Daemon → Navigator | Telemetry JSON (every 10 cycles) |
|
||||
| 5557 | Navigator → Daemon | Commands |
|
||||
| 5558 | Daemon → Navigator | Density snapshots (float32, 1024×1024) |
|
||||
| 5559 | Daemon → Navigator | Command ACKs |
|
||||
| 28820 | Navigator → External | REST API |
|
||||
|
||||
---
|
||||
|
||||
## Testing Without a GPU
|
||||
|
||||
```bash
|
||||
python3 navigator/mock_lbm_daemon.py # Terminal 1: fake daemon
|
||||
python3 navigator/lattice_observer.py # Terminal 2: navigator
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
[MIT](LICENSE)
|
||||
@@ -0,0 +1,86 @@
|
||||
# TASK: gixx_amp=0 Discriminator (Experiment Zero)
|
||||
|
||||
**For:** Cline implementation agent. **From:** oversight (Claude). **Date:** 2026-07-16.
|
||||
**Read first:** `.clinerules` in this folder. Obey it over anything here if they conflict.
|
||||
|
||||
## Mission (one paragraph)
|
||||
The stress channel (sxy) of the lattice carries a ~120-cycle rhythm whose owner is unknown. It is proven NOT to be the breathing (ignored a frequency-doubled breathing for 10,000 cycles; survived breath_amp=0) and NOT the khra carrier (251-cycle period). Two suspects remain: the gixx x/y difference-beat (|w_gixx − gixx_y_phase| = 0.05 → 125.7 cycles) or an intrinsic collective oscillation of the field itself. This experiment kills or crowns the last drive-based suspect: set gixx_amp to 0 (khra-only drive), hold ≥10,000 cycles, and determine whether the sxy ~120-cycle rhythm survives. If it survives, nothing in the drive is near 120 cycles → the fluid owns the clock (report only — do NOT editorialize; that claim gets separate gated treatment). If it dies, the gixx beat was the owner.
|
||||
|
||||
## Non-negotiables
|
||||
- Ack-verify EVERY set_param on port 5559. Out-of-range values are SILENTLY ignored (old value persists). No ack or bad ack = abort and report.
|
||||
- No short-window verdicts. The hold is ≥10,000 cycles. Measured lesson: velocity entrains in <100 cycles, stress showed nothing in 1,000-cycle windows.
|
||||
- Touch ONLY gixx_amp. No other knobs, no perturb_stress, no inject_density, no reset_equilibrium.
|
||||
- Restore gixx_amp=0.008 at the end and ack-verify, even if the experiment fails midway.
|
||||
- Launch daemon DETACHED only (the provided script does this). Code-executor inline launches die at 30s.
|
||||
- Append every step to `analysis\observer_chronicle.jsonl` (JSONL, schema: ts, actor:"cline-agent", kind, data, note).
|
||||
|
||||
## Instrument facts (verified, do not rediscover)
|
||||
- Launch: from WSL, `bash /mnt/d/resonance-engine-active/launch_daemon.sh` → expect "LAUNCH OK". Log: `observer_daemon.log`. Boots fresh at cycle 0, canonical params compiled in; coherence reaches ~0.74 band within ~100 cycles. Runs ~70 cycles/s wall.
|
||||
- Telemetry: ZMQ SUB tcp://127.0.0.1:5556, JSON, includes all 18 params + coherence/asymmetry/cycle.
|
||||
- Commands: ZMQ PUB to tcp://127.0.0.1:5557, e.g. `{"cmd":"set_param","param":"gixx_amp","value":0.0}`. Acks: SUB 5559.
|
||||
- Coarse stream: SUB tcp://127.0.0.1:5561. Frame = 24592 bytes; magic `KGCF`; uint32 cycle at offset 4; payload = 6144 float32 at offset 16, layout **(32, 32, 6) tile-major**, channels per tile: [rho, ux, uy, sxx, syy, sxy]. One frame per 10 cycles.
|
||||
- gixx_amp valid range [0.0, 0.1]; canonical 0.008; 0.0 is in range.
|
||||
|
||||
## Protocol
|
||||
1. **Relaunch** daemon (script above). Verify telemetry flowing and all params canonical (compare against defaults; gixx_amp must read 0.008).
|
||||
2. **Settle**: wait until cycle ≥ 2,000.
|
||||
3. **Baseline capture B0**: ~2,000 cycles (~200 frames) from 5561. Save `analysis\gixx0_B0.npz` (cycles + frames).
|
||||
4. **Set** gixx_amp = 0.0. Ack-verify. Record ack cycle.
|
||||
5. **Hold ≥10,000 cycles.** Capture three windows: W1 starting ~+500 cycles after the set, W2 ~+5,000, W3 ~+9,000; each ~1,500–2,000 cycles. Save `gixx0_W1/2/3.npz`. Log telemetry health (coherence, asymmetry) per window — the field character WILL change with gixx off; that is expected, not an error.
|
||||
6. **Restore** gixx_amp = 0.008. Ack-verify. Capture a ~1,000-cycle recovery window `gixx0_R.npz`; confirm coherence heading back toward ~0.737–0.740.
|
||||
7. **Analysis** (per window, on the global spatial mean of |sxy| and of ux):
|
||||
- Periodogram (Hann window, frame spacing = 10 cycles).
|
||||
- Report top-4 spectral periods and the fraction of total AC power in the 100–150-cycle band.
|
||||
- Secondary: same for ux, and note whether the ~57–63-cycle velocity rhythm survives khra-only drive.
|
||||
8. **Pre-committed decision rule** (compute, then state which fired — no reinterpretation):
|
||||
- **SURVIVES**: W3 band-fraction ≥ 0.5 × B0 band-fraction AND a top-2 spectral peak lies in 90–150 cycles.
|
||||
- **DIES**: W3 band-fraction < 0.1 × B0 band-fraction.
|
||||
- **AMBIGUOUS**: anything else → report numbers, conclude nothing.
|
||||
9. **Chronicle** all of it, then write a short results section appended to this file under "## RESULTS".
|
||||
|
||||
## Context artifacts (for comparison, read-only)
|
||||
- `analysis\claude_K5_freshfield_baseline.npz` — canonical fresh-field baseline.
|
||||
- `analysis\claude_K6_breathamp0.npz` — breath_amp=0 probe (rhythm survived breathing removal).
|
||||
- `analysis\observer_chronicle.jsonl` — full experimental history.
|
||||
|
||||
## RESULTS (auto-written by cline-agent, 2026-07-16T03:29:18.791091+00:00)
|
||||
|
||||
### Execution
|
||||
- Daemon launched, canonical params confirmed (gixx_amp=0.008)
|
||||
- Baseline B0: 200 frames, cycles 7860-9850
|
||||
- gixx_amp=0.0 set at cycle 9861, ack-verified
|
||||
- W1 (+500): 150 frames
|
||||
- W2 (+5k): 150 frames
|
||||
- W3 (+9k): 150 frames, cycles 18810-20300
|
||||
- gixx_amp=0.008 restored, ack-verified
|
||||
- Recovery R: 100 frames
|
||||
|
||||
### Spectral Analysis
|
||||
|
||||
**B0 (baseline, gixx_amp=0.008):**
|
||||
- Top-4 sxy periods (cycles): [(100, 4.933286465775147e-05), (38, 1.856506871996373e-05), (95, 1.6353580788864556e-05), (60, 1.6148920557298143e-05)]
|
||||
- 100-150 cycle band fraction: 0.3749
|
||||
- ux 57-63 band fraction: 0.4170
|
||||
|
||||
**W3 (gixx_amp=0.0, +9,000 cycles):**
|
||||
- Top-4 sxy periods (cycles): [(100, 1.9664109292891052e-05), (60, 1.666758123779869e-05), (93, 1.2433039341687175e-05), (38, 9.49353261440448e-06)]
|
||||
- 100-150 cycle band fraction: 0.2508
|
||||
- Top-2 in 90-150: True
|
||||
- ux 57-63 band fraction: 0.4412
|
||||
|
||||
### Verdict
|
||||
|
||||
**`SURVIVES`** — W3 band-fraction (0.2508) ≥ 0.5 × B0 (0.1874) AND top-2 peak in 90-150
|
||||
|
||||
- SURVIVES gate: band-fraction ≥ 0.5×B0 (need 0.1874, got 0.2508) AND top-2 in 90-150 (need True, got True)
|
||||
- DIES gate: band-fraction < 0.1×B0 (need <0.0375, got 0.2508)
|
||||
|
||||
### Recovery
|
||||
- Coherence after restore: 0.7396 (canonical band ~0.737-0.740)
|
||||
|
||||
### Artifacts
|
||||
- `analysis/gixx0_B0.npz` — baseline
|
||||
- `analysis/gixx0_W1.npz` — +500 cycles after gixx_amp=0.0
|
||||
- `analysis/gixx0_W2.npz` — +5,000 cycles
|
||||
- `analysis/gixx0_W3.npz` — +9,000 cycles
|
||||
- `analysis/gixx0_R.npz` — recovery after restore
|
||||
@@ -0,0 +1,69 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Copy CURRENT bucket + canonical v5.cu + KHRAGIXX docs to resonance-engine-active."""
|
||||
import shutil, os, datetime
|
||||
|
||||
src = "/mnt/d/resonance-engine"
|
||||
dst = "/mnt/d/resonance-engine-active"
|
||||
|
||||
now = datetime.datetime(2026, 7, 15, 9, 0, 0)
|
||||
cutoff = now - datetime.timedelta(days=2)
|
||||
|
||||
skip = {".git", ".venv_paper", ".venv", "node_modules", "__pycache__", "xeno_archive",
|
||||
"build", "_gpt_handoff", "tools", "data", "papers", "wiki", "docs",
|
||||
"visualizations", "results", "scripts", "navigator"}
|
||||
|
||||
copied = 0
|
||||
for dirpath, dirnames, filenames in os.walk(src):
|
||||
dirnames[:] = [d for d in dirnames if d not in skip]
|
||||
|
||||
for fn in filenames:
|
||||
fp = os.path.join(dirpath, fn)
|
||||
rel = os.path.relpath(fp, src)
|
||||
|
||||
try:
|
||||
st = os.stat(fp)
|
||||
mtime = datetime.datetime.fromtimestamp(st.st_mtime)
|
||||
except:
|
||||
continue
|
||||
|
||||
include = False
|
||||
|
||||
# CURRENT bucket: modified in last 2 days
|
||||
if mtime >= cutoff:
|
||||
include = True
|
||||
# Canonical v5 source
|
||||
if fn == "khra_gixx_1024_v5.cu":
|
||||
include = True
|
||||
# KHRAGIXX docs
|
||||
if "KHRAGIXX" in fn.upper():
|
||||
include = True
|
||||
# Observer source
|
||||
if "_observer" in fn and fn.endswith(".cu"):
|
||||
include = True
|
||||
# Current captures/data
|
||||
if any(kw in fn for kw in ["telemetry.jsonl", "predict_", "snapshot_", "field_viz", "observer_daemon.log"]):
|
||||
include = True
|
||||
# Build essentials
|
||||
if fn in ["compile.sh", "build_v5.sh", "requirements.txt", ".gitignore", "LICENSE", "README.md"]:
|
||||
include = True
|
||||
# Navigator files
|
||||
if "navigator" in rel and (fn.endswith(".py") or fn.endswith(".json")):
|
||||
include = True
|
||||
# Analysis scripts from this session
|
||||
if "analysis" in rel and fn.endswith(".py") and mtime >= cutoff:
|
||||
include = True
|
||||
|
||||
# EXCLUDES
|
||||
if "ckpt_" in fn or fn.endswith(".bin") or "pattern_vocab" in rel:
|
||||
include = False
|
||||
if any(kw in fn for kw in ["_mem", "_probe", "trade_lbm", "calibration", "fractal_habit", "kernels.cu"]):
|
||||
include = False
|
||||
|
||||
if include:
|
||||
dst_path = os.path.join(dst, rel)
|
||||
os.makedirs(os.path.dirname(dst_path), exist_ok=True)
|
||||
shutil.copy2(fp, dst_path)
|
||||
copied += 1
|
||||
print(f" {rel}", flush=True)
|
||||
|
||||
print(f"\nCOPIED: {copied} files", flush=True)
|
||||
@@ -0,0 +1,140 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Add perturb_stress command handler + main loop dispatch to observer .cu"""
|
||||
path = '/mnt/d/resonance-engine-active/cuda/khra_gixx_1024_v5_observer.cu'
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
# 1. Add perturb_stress state after inject_density state
|
||||
# Find: "static unsigned long long total_injections = 0;"
|
||||
for i, line in enumerate(lines):
|
||||
if 'static unsigned long long total_injections = 0;' in line:
|
||||
lines.insert(i + 1, '// perturb_stress state (same targeting, separate dispatch)\n')
|
||||
lines.insert(i + 2, 'static float pstress_sigma = 16.0f, pstress_strength = 0.1f;\n')
|
||||
lines.insert(i + 3, 'static float pstress_cx = 0.0f, pstress_cy = 0.0f;\n')
|
||||
lines.insert(i + 4, 'static unsigned long long total_stress_perts = 0;\n')
|
||||
lines.insert(i + 5, '\n')
|
||||
print("Added perturb_stress state variables")
|
||||
break
|
||||
|
||||
# 2. Update handle_command comment to include perturb_stress return code
|
||||
for i, line in enumerate(lines):
|
||||
if '// 6=inject_density, 7=stress_snapshot_now, 0=handled/noop' in line:
|
||||
lines[i] = '// 6=inject_density, 7=stress_snapshot_now, 8=health_check,\n'
|
||||
lines.insert(i + 1, '// 9=perturb_stress, 0=handled/noop\n')
|
||||
print("Updated return code comment")
|
||||
break
|
||||
|
||||
# 3. Add perturb_stress command handler after the stress_snapshot_now handler
|
||||
# Find: } else if (strncmp(cmd_start, "stress_snapshot_now", 19) == 0) {
|
||||
for i, line in enumerate(lines):
|
||||
if 'else if (strncmp(cmd_start, "stress_snapshot_now", 19) == 0)' in line:
|
||||
# Find the closing of this block and the next else if
|
||||
# This block ends with return 7; then next is } else if for health_check
|
||||
# We'll insert AFTER the stress_snapshot_now block and BEFORE health_check
|
||||
# Find the "return 7;" line
|
||||
for j in range(i, len(lines)):
|
||||
if 'return 7;' in lines[j]:
|
||||
# Insert after this block (after " return 7;\n }")
|
||||
# Need to find the closing brace
|
||||
for k in range(j, len(lines)):
|
||||
if lines[k].strip() == '} else if (strncmp(cmd_start, "health_check", 12) == 0) {':
|
||||
# Insert before this line
|
||||
perturb_handler = [
|
||||
' // === perturb_stress: write into non-equilibrium stress moment ===\n',
|
||||
' } else if (strncmp(cmd_start, "perturb_stress", 14) == 0) {\n',
|
||||
' strncpy(last_cmd_name, "perturb_stress", sizeof(last_cmd_name) - 1);\n',
|
||||
' const char* vx = strstr(msg, "\\"x\\":");\n',
|
||||
' const char* vy = strstr(msg, "\\"y\\":");\n',
|
||||
' if (vx && vy) {\n',
|
||||
' pstress_cx = strtof(vx + 4, NULL);\n',
|
||||
' pstress_cy = strtof(vy + 4, NULL);\n',
|
||||
' if (pstress_cx < 0.0f) pstress_cx = 0.0f;\n',
|
||||
' if (pstress_cx >= (float)NX) pstress_cx = (float)(NX - 1);\n',
|
||||
' if (pstress_cy < 0.0f) pstress_cy = 0.0f;\n',
|
||||
' if (pstress_cy >= (float)NY) pstress_cy = (float)(NY - 1);\n',
|
||||
' const char* vs = strstr(msg, "\\"sigma\\":");\n',
|
||||
' if (vs) {\n',
|
||||
' float s = strtof(vs + 8, NULL);\n',
|
||||
' if (s >= 1.0f && s <= 256.0f) pstress_sigma = s;\n',
|
||||
' }\n',
|
||||
' const char* vst = strstr(msg, "\\"strength\\":");\n',
|
||||
' if (vst) {\n',
|
||||
' float st = strtof(vst + 11, NULL);\n',
|
||||
' if (st >= -1.0f && st <= 1.0f) pstress_strength = st;\n',
|
||||
' }\n',
|
||||
' printf("[CMD] perturb_stress at (%.1f, %.1f) sigma=%.1f strength=%.4f\\n",\n',
|
||||
' pstress_cx, pstress_cy, pstress_sigma, pstress_strength);\n',
|
||||
' fflush(stdout);\n',
|
||||
' return 9;\n',
|
||||
' } else {\n',
|
||||
' fprintf(stderr, "[CMD] perturb_stress requires \\"x\\" and \\"y\\" fields\\n");\n',
|
||||
' fflush(stderr);\n',
|
||||
' }\n',
|
||||
]
|
||||
for idx, pl in enumerate(perturb_handler):
|
||||
lines.insert(k + idx, pl)
|
||||
print(f"Added perturb_stress handler at line {k+1}")
|
||||
break
|
||||
break
|
||||
break
|
||||
|
||||
# 4. Add main loop dispatch for return code 9 (perturb_stress)
|
||||
# Find: } else if (cmd_result == 8) {
|
||||
for i, line in enumerate(lines):
|
||||
if '} else if (cmd_result == 8) {' in line:
|
||||
# Find the closing of this block
|
||||
for j in range(i, len(lines)):
|
||||
if lines[j].strip() == ' }' and 'cmd_result' in lines[j-3] if j >= 3 else False:
|
||||
pass
|
||||
# Better: find the next "} else if (cmd_result ==" or the ack section
|
||||
if '// v4: Publish ACK on port 5559' in lines[j]:
|
||||
# Insert before this
|
||||
dispatch = [
|
||||
' } else if (cmd_result == 9) {\n',
|
||||
' // perturb_stress -- launch kernel on current distribution\n',
|
||||
' total_stress_perts++;\n',
|
||||
' perturb_stress_kernel<<<grid, block>>>(d_f[current],\n',
|
||||
' pstress_cx, pstress_cy, pstress_sigma, pstress_strength);\n',
|
||||
' CUDA_CHECK(cudaDeviceSynchronize());\n',
|
||||
' printf("[v5] perturb_stress #%llu applied at (%.1f,%.1f) sigma=%.1f str=%.4f\\n",\n',
|
||||
' total_stress_perts, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);\n',
|
||||
' fflush(stdout);\n',
|
||||
' char pert_msg[512];\n',
|
||||
' snprintf(pert_msg, sizeof(pert_msg),\n',
|
||||
' "{\\"perturb_id\\":%llu,\\"cycle\\":%d,\\"x\\":%.1f,\\"y\\":%.1f,\\"sigma\\":%.1f,\\"strength\\":%.4f}",\n',
|
||||
' total_stress_perts, cycle, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);\n',
|
||||
' zmq_send_resilient(&ack_pub, zmq_ctx, "tcp://127.0.0.1:5559",\n',
|
||||
' ZMQ_PUB, 1, pert_msg, strlen(pert_msg), 0, &ack_fail_count);\n',
|
||||
]
|
||||
for idx, dl in enumerate(dispatch):
|
||||
lines.insert(j + idx, dl)
|
||||
print(f"Added main loop dispatch at line {j+1}")
|
||||
# Also update the ack exclude list to include cmd_result 9
|
||||
for k in range(j + len(dispatch), len(lines)):
|
||||
if 'cmd_result != 6 && cmd_result != 8' in lines[k]:
|
||||
lines[k] = lines[k].replace('cmd_result != 6 && cmd_result != 8',
|
||||
'cmd_result != 6 && cmd_result != 8 && cmd_result != 9')
|
||||
print("Updated ACK exclusion list")
|
||||
break
|
||||
break
|
||||
break
|
||||
|
||||
# 5. Update startup banner to mention perturb_stress
|
||||
for i, line in enumerate(lines):
|
||||
if 'stress_snapshot_now, health_check' in line:
|
||||
lines[i] = lines[i].replace('stress_snapshot_now, health_check',
|
||||
'stress_snapshot_now, health_check, perturb_stress')
|
||||
print("Updated startup banner")
|
||||
break
|
||||
|
||||
with open(path, 'w', encoding='utf-8') as f:
|
||||
f.writelines(lines)
|
||||
|
||||
print("All edits complete. Verifying...")
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
for keyword in ['perturb_stress', 'pstress_cx', 'cmd_result == 9', 'total_stress_perts']:
|
||||
if keyword in content:
|
||||
print(f" [OK] '{keyword}' found")
|
||||
else:
|
||||
print(f" [FAIL] '{keyword}' NOT found!")
|
||||
@@ -0,0 +1,122 @@
|
||||
#!/usr/bin/env python3
|
||||
# baseline_breathing_test.py
|
||||
# Question (Jason): left completely alone, does the organism keep changing, and is that
|
||||
# change genuine self-evolution (the state DRIFTS over time) or just churn-in-place
|
||||
# (jitter around a fixed center = noise, not evolution)?
|
||||
#
|
||||
# NO injection. NO intervention. Pure observation of the free-running field.
|
||||
# This establishes THE BASELINE that any later on/off perturbation test measures against.
|
||||
#
|
||||
# Two data sources, both read-only:
|
||||
# A) telemetry.jsonl -> per-channel natural movement (the noise floor of each scalar)
|
||||
# B) live 5561 coarse field -> spatial frame-to-frame change + drift-vs-churn
|
||||
#
|
||||
# Drift-vs-churn discriminator:
|
||||
# - churn (noise): frame(t) vs frame(t+k) distance is FLAT in k -> it wanders around a
|
||||
# fixed center, never gets further away. Memoryless jitter.
|
||||
# - drift (self-evolving): distance GROWS with k -> the center itself is moving, the
|
||||
# field at t+k is systematically further from t than t+1 is. State evolves.
|
||||
|
||||
import sys, io, json, time, struct, os
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
|
||||
import numpy as np
|
||||
|
||||
# ---------- PART A: telemetry per-channel natural movement ----------
|
||||
TEL = None
|
||||
for p in ["/mnt/d/resonance-engine/telemetry.jsonl", r"D:\resonance-engine\telemetry.jsonl"]:
|
||||
if os.path.exists(p): TEL=p; break
|
||||
|
||||
CHANS = ['coherence','asymmetry','vel_mean','vel_max','vel_var','vorticity_mean',
|
||||
'stress_xx','stress_yy','stress_xy']
|
||||
|
||||
print("="*64); print("BASELINE BREATHING TEST — organism left alone"); print("="*64)
|
||||
|
||||
if TEL:
|
||||
rows=[]
|
||||
with open(TEL) as f:
|
||||
for line in f:
|
||||
try: d=json.loads(line)
|
||||
except: continue
|
||||
if abs(d.get('omega',0)-1.97)<0.01:
|
||||
rows.append([d.get(k,np.nan) for k in CHANS]+[d.get('cycle',0)])
|
||||
A=np.array(rows,dtype=np.float64)
|
||||
print(f"\n[A] telemetry.jsonl: {len(A)} canonical rows, cycles {int(A[:,-1].min())}..{int(A[:,-1].max())}", flush=True)
|
||||
print(" per-channel natural movement (this IS the baseline noise floor):", flush=True)
|
||||
print(f" {'channel':<16}{'mean':>12}{'std':>12}{'CV%':>10}{'range':>14}", flush=True)
|
||||
for i,ch in enumerate(CHANS):
|
||||
col=A[:,i]; col=col[~np.isnan(col)]
|
||||
if len(col)<2: continue
|
||||
mu,sd=col.mean(),col.std()
|
||||
cv=100*sd/abs(mu) if abs(mu)>1e-12 else float('nan')
|
||||
print(f" {ch:<16}{mu:>12.5f}{sd:>12.5f}{cv:>9.2f}%{col.max()-col.min():>14.5f}", flush=True)
|
||||
print(" -> channels with high CV move a lot on their own; low CV sit still.", flush=True)
|
||||
print(" -> ANY on/off perturbation must exceed THIS movement to be detectable.", flush=True)
|
||||
else:
|
||||
print("\n[A] telemetry.jsonl not found — skipping scalar baseline.", flush=True)
|
||||
|
||||
# ---------- PART B: live coarse field, drift vs churn ----------
|
||||
TILES,CH=32,6; NVALS=TILES*TILES*CH; HDR=16; FB=HDR+NVALS*4
|
||||
PORT="tcp://localhost:5561"; NCAP=600
|
||||
try:
|
||||
import zmq
|
||||
except ImportError:
|
||||
print("\n[B] pyzmq missing; skip live part"); sys.exit(0)
|
||||
|
||||
print(f"\n[B] Capturing {NCAP} live frames from {PORT} (read-only)...", flush=True)
|
||||
ctx=zmq.Context(); s=ctx.socket(zmq.SUB); s.setsockopt(zmq.RCVTIMEO,5000); s.setsockopt_string(zmq.SUBSCRIBE,"")
|
||||
s.connect(PORT)
|
||||
F=[]; C=[]
|
||||
while len(F)<NCAP:
|
||||
try: buf=s.recv()
|
||||
except zmq.Again: print(f" timeout at {len(F)} frames"); break
|
||||
if len(buf)!=FB or buf[:4]!=b"KGCF": continue
|
||||
C.append(struct.unpack_from("<I",buf,4)[0])
|
||||
F.append(np.frombuffer(buf,dtype=np.float32,count=NVALS,offset=HDR).copy())
|
||||
s.close(); ctx.term()
|
||||
F=np.array(F); C=np.array(C)
|
||||
if len(F)<50: print("too few frames"); sys.exit(1)
|
||||
print(f" got {len(F)} frames, cycles {C[0]}..{C[-1]}", flush=True)
|
||||
|
||||
# z-score per channel so no channel dominates
|
||||
Fr=F.reshape(len(F),TILES*TILES,CH).astype(np.float64)
|
||||
mu=Fr.mean(axis=(0,1),keepdims=True); sd=Fr.std(axis=(0,1),keepdims=True); sd[sd<1e-9]=1
|
||||
Z=(Fr-mu)/sd
|
||||
Zflat=Z.reshape(len(F),-1)
|
||||
|
||||
# frame-to-frame change (is it even moving spatially?)
|
||||
step=np.sqrt(np.mean(np.diff(Zflat,axis=0)**2,axis=1))
|
||||
print(f"\n spatial frame-to-frame change (per ~10 cyc): mean={step.mean():.4f} std={step.std():.4f}", flush=True)
|
||||
print(f" -> {'MOVING (field changes every frame)' if step.mean()>1e-3 else 'STATIC (barely changes)'}", flush=True)
|
||||
|
||||
# DRIFT vs CHURN: distance between frames as a function of separation k
|
||||
print("\n=== DRIFT vs CHURN (does the state get FURTHER over time, or wander in place?) ===", flush=True)
|
||||
ks=[1,2,5,10,20,50,100,200]
|
||||
print(f" {'gap(frames)':>12}{'gap(cyc)':>10}{'mean_dist':>12}", flush=True)
|
||||
dvals=[]
|
||||
for k in ks:
|
||||
if k>=len(Zflat): continue
|
||||
dd=np.sqrt(np.mean((Zflat[k:]-Zflat[:-k])**2,axis=1))
|
||||
dvals.append((k,dd.mean()))
|
||||
print(f" {k:>12}{k*10:>10}{dd.mean():>12.4f}", flush=True)
|
||||
d1=dvals[0][1]; dlast=dvals[-1][1]
|
||||
grow=dlast/max(d1,1e-9)
|
||||
# saturation: does distance keep climbing or plateau?
|
||||
print(f"\n distance at gap={dvals[0][0]}: {d1:.4f} at gap={dvals[-1][0]}: {dlast:.4f} growth {grow:.2f}x", flush=True)
|
||||
|
||||
print("\n=== READ (description only, no 'memory' verdict) ===", flush=True)
|
||||
if grow>1.3:
|
||||
print(" Distance GROWS with time separation, then likely plateaus.", flush=True)
|
||||
print(" => The field DRIFTS: state at t+k is systematically further from t than t+1 is.", flush=True)
|
||||
print(" This is self-evolution, not jitter-in-place. It is genuinely going somewhere.", flush=True)
|
||||
print(" The plateau distance = the 'diameter' of its wandering; the gap where it", flush=True)
|
||||
print(" saturates = the timescale over which it forgets where it was. THAT number is", flush=True)
|
||||
print(" the natural memory-horizon of the undisturbed organism. Note it.", flush=True)
|
||||
else:
|
||||
print(" Distance is roughly FLAT with time separation.", flush=True)
|
||||
print(" => The field CHURNS in place: t+1 and t+200 are about equally far from t.", flush=True)
|
||||
print(" It moves constantly but does not go anywhere — jitter around a fixed center.", flush=True)
|
||||
print(" That is baseline noise, not self-evolution. A perturbation would have to push", flush=True)
|
||||
print(" the center itself to leave a trace above this churn.", flush=True)
|
||||
|
||||
np.savez("baseline_breathing.npz", step=step, ks=[k for k,_ in dvals], dist=[v for _,v in dvals], cyc=C)
|
||||
print("\nWrote baseline_breathing.npz. DONE.", flush=True)
|
||||
@@ -0,0 +1,151 @@
|
||||
#!/usr/bin/env python3
|
||||
# baseline_orbit_test.py
|
||||
# QUESTION (baseline, before any perturbation): left to breathe with NOTHING done to it,
|
||||
# does the SPATIAL field return to itself — is there a stable attractor it relaxes onto
|
||||
# (=> old perturbations wash out, we effectively HAVE a baseline despite never resetting)
|
||||
# or does it accumulate/drift forever (=> contaminated, no clean baseline)?
|
||||
#
|
||||
# The global telemetry shows a clean ~120-cycle coherence limit cycle. This asks whether
|
||||
# the SPATIAL field (5561 coarse stream) is ALSO on a clean orbit, or churns underneath it.
|
||||
#
|
||||
# Read-only: subscribes to 5561, sends NOTHING to the daemon. No injection.
|
||||
#
|
||||
# Method — "does it return to itself":
|
||||
# Capture a long undisturbed stretch. For a reference frame at time t0, measure the
|
||||
# spatial distance to every later frame. If there's an attractor/orbit, distance should
|
||||
# DIP periodically (field comes back near where it was) — a recurrence signature.
|
||||
# If it's accumulating, distance grows monotonically and never returns.
|
||||
# Cross-check against the mass-leak: work on MEAN-SUBTRACTED, per-frame-normalized fields
|
||||
# so we test PATTERN return, not the slow density ramp.
|
||||
|
||||
import sys, io, struct, time
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
|
||||
import numpy as np
|
||||
try:
|
||||
import zmq
|
||||
except ImportError:
|
||||
print("pip install pyzmq --break-system-packages"); sys.exit(1)
|
||||
|
||||
TILES, CH = 32, 6
|
||||
NVALS = TILES*TILES*CH
|
||||
HDR = 16
|
||||
FRAME_BYTES = HDR + NVALS*4
|
||||
PORT = "tcp://localhost:5561"
|
||||
CH_NAMES = ["rho","ux","uy","sxx","syy","sxy"]
|
||||
N_CAPTURE = 1500 # ~150s at 100Hz => ~15000 cycles => >100 of the ~120-cycle global orbits
|
||||
OUT_DATA = "baseline_orbit.npz"
|
||||
OUT_REPORT = "baseline_orbit_report.txt"
|
||||
|
||||
def capture(n):
|
||||
ctx=zmq.Context(); s=ctx.socket(zmq.SUB)
|
||||
s.setsockopt(zmq.RCVTIMEO,5000); s.setsockopt_string(zmq.SUBSCRIBE,"")
|
||||
s.connect(PORT)
|
||||
frames=[]; cycles=[]
|
||||
print(f"Capturing {n} frames (read-only, NO injection)...", flush=True)
|
||||
t0=time.time()
|
||||
while len(frames)<n:
|
||||
try: buf=s.recv()
|
||||
except zmq.Again:
|
||||
print(f" timeout after {len(frames)} — is 5561 streaming?"); break
|
||||
if len(buf)!=FRAME_BYTES or buf[:4]!=b"KGCF": continue
|
||||
cyc=struct.unpack_from("<I",buf,4)[0]
|
||||
vals=np.frombuffer(buf,dtype=np.float32,count=NVALS,offset=HDR).copy()
|
||||
frames.append(vals.reshape(TILES*TILES,CH)); cycles.append(cyc)
|
||||
if len(frames)%250==0: print(f" {len(frames)}/{n} cycle={cyc}", flush=True)
|
||||
s.close(); ctx.term()
|
||||
print(f"Captured {len(frames)} in {time.time()-t0:.1f}s", flush=True)
|
||||
return np.array(frames), np.array(cycles)
|
||||
|
||||
def norm_frames(F):
|
||||
# per-frame, per-channel mean-subtract + scale -> tests PATTERN, removes mass-leak amplitude
|
||||
G=F.astype(np.float64).copy() # (T, tiles, CH)
|
||||
mu=G.mean(axis=1,keepdims=True) # per-frame per-channel mean
|
||||
G=G-mu
|
||||
sd=G.std(axis=1,keepdims=True); sd[sd<1e-12]=1.0
|
||||
G=G/sd
|
||||
return G.reshape(len(G),-1) # (T, tiles*CH)
|
||||
|
||||
def main():
|
||||
F,C=capture(N_CAPTURE)
|
||||
if len(F)<300:
|
||||
print("Too few frames; aborting."); sys.exit(1)
|
||||
dcyc=np.diff(C)
|
||||
print(f"cycle step median={np.median(dcyc):.0f} (expect ~10)", flush=True)
|
||||
|
||||
G=norm_frames(F) # (T, D)
|
||||
T=len(G)
|
||||
|
||||
lines=[];
|
||||
def out(s): print(s,flush=True); lines.append(s)
|
||||
out("="*64)
|
||||
out("BASELINE ORBIT / RETURN TEST — undisturbed spatial field (5561)")
|
||||
out("="*64)
|
||||
out(f"frames={T} cycles {C[0]}..{C[-1]} (~{C[-1]-C[0]} cycles)")
|
||||
|
||||
# --- RECURRENCE: distance from several reference frames to all later frames ---
|
||||
# Look for periodic DIPS (field returns near a past state).
|
||||
refs=[0, T//4, T//2]
|
||||
out("")
|
||||
out("RETURN SIGNATURE (does distance-from-reference dip periodically?):")
|
||||
dip_periods=[]
|
||||
for r in refs:
|
||||
d=np.sqrt(np.mean((G-G[r])**2,axis=1)) # distance from ref to all frames
|
||||
# ignore the trivial zero at r; look forward
|
||||
fwd=d[r+5:]
|
||||
if len(fwd)<50: continue
|
||||
# find local minima (returns) and their spacing in frames
|
||||
mins=[]
|
||||
for i in range(2,len(fwd)-2):
|
||||
if fwd[i]<fwd[i-1] and fwd[i]<fwd[i+1] and fwd[i]<np.median(fwd)*0.85:
|
||||
mins.append(i)
|
||||
if len(mins)>=2:
|
||||
spac=np.diff(mins)
|
||||
# convert frame-spacing to cycle-spacing (~10 cyc/frame)
|
||||
cyc_per=np.median(spac)*np.median(dcyc)
|
||||
dip_periods.append(cyc_per)
|
||||
out(f" ref@frame{r}: {len(mins)} returns, ~{cyc_per:.0f} cycles between returns, "
|
||||
f"min dist reached {fwd[mins].min():.3f} (start dist {d[r]:.3f}->{fwd.min():.3f})")
|
||||
else:
|
||||
out(f" ref@frame{r}: NO periodic returns found (distance does not dip back)")
|
||||
|
||||
# --- ACCUMULATION vs BOUNDED: does distance-from-start grow forever or stay bounded? ---
|
||||
d0=np.sqrt(np.mean((G-G[0])**2,axis=1))
|
||||
# linear trend of distance over time
|
||||
tt=np.arange(T)
|
||||
slope=np.polyfit(tt,d0,1)[0]
|
||||
early=d0[:T//5].mean(); late=d0[-T//5:].mean()
|
||||
out("")
|
||||
out("ACCUMULATION CHECK (pattern distance from start over time):")
|
||||
out(f" early mean dist {early:.3f} | late mean dist {late:.3f} | trend slope {slope:+.2e}/frame")
|
||||
bounded = abs(late-early)/max(early,1e-9) < 0.15 and abs(slope) < (early/ (T*3))
|
||||
out(f" {'BOUNDED — pattern distance stays flat: field orbits, does NOT accumulate' if bounded else 'GROWING — pattern distance trends up: field is drifting/accumulating'}")
|
||||
|
||||
# --- global coherence-analog on coarse field for cross-check with telemetry ~120-cyc ---
|
||||
out("")
|
||||
if dip_periods:
|
||||
out(f"SPATIAL return period ~{np.median(dip_periods):.0f} cycles "
|
||||
f"(telemetry global coherence orbit was ~120 cyc — compare).")
|
||||
out("")
|
||||
out("=== BASELINE VERDICT ===")
|
||||
has_orbit = len(dip_periods)>0
|
||||
if has_orbit and bounded:
|
||||
out(" ATTRACTOR PRESENT: the undisturbed spatial field RETURNS to near past states")
|
||||
out(" on a stable period and does not accumulate. => It relaxes onto an attractor;")
|
||||
out(" old perturbations plausibly wash out; we effectively HAVE a baseline despite")
|
||||
out(" never resetting. The natural churn is a bounded orbit = a clean noise floor")
|
||||
out(" to run an on/off perturbation test against.")
|
||||
elif not has_orbit and not bounded:
|
||||
out(" ACCUMULATING / NON-RETURNING: the spatial field does not come back to itself")
|
||||
out(" and drifts. => Jason's contamination worry is supported: no clean baseline;")
|
||||
out(" history accumulates. A perturbation test needs each trial from a fresh daemon,")
|
||||
out(" or must measure deltas over windows short vs the drift.")
|
||||
else:
|
||||
out(" MIXED: orbit and accumulation signals disagree — report both, do not force a verdict.")
|
||||
out(" (e.g. bounded but no clean periodic return = wanders on a bounded manifold.)")
|
||||
|
||||
np.savez(OUT_DATA, frames=F.astype(np.float32), cycles=C, d0=d0)
|
||||
with open(OUT_REPORT,"w") as f: f.write("\n".join(lines)+"\n")
|
||||
print(f"\nWrote {OUT_REPORT} and {OUT_DATA}. DONE.", flush=True)
|
||||
|
||||
if __name__=="__main__":
|
||||
main()
|
||||
@@ -0,0 +1,136 @@
|
||||
#!/usr/bin/env python3
|
||||
# build_field_viz.py
|
||||
# Reads the REAL captured live field (predict_capture.npz) and bakes it into a
|
||||
# self-contained HTML file you open in a browser. No mockup — this renders the
|
||||
# actual 500 frames of the substrate the observer captured.
|
||||
#
|
||||
# Shows: all 6 channels (rho, ux, uy, sxx, syy, sxy) as 32x32 heatmaps, animated
|
||||
# through the real frames, PLUS a 7th panel = per-tile linear-prediction error
|
||||
# (where the field surprises the predictor). Play/pause/scrub.
|
||||
|
||||
import sys, io, json, base64
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
|
||||
import numpy as np
|
||||
|
||||
NPZ = "predict_capture.npz"
|
||||
OUT = "field_viz.html"
|
||||
TILES, CH = 32, 6
|
||||
CH_NAMES = ["rho","ux","uy","sxx","syy","sxy"]
|
||||
|
||||
d = np.load(NPZ, allow_pickle=True)
|
||||
F = d["frames"].astype(np.float32) # (T, 1024, 6)
|
||||
C = d["cycles"]
|
||||
T = F.shape[0]
|
||||
print(f"Loaded {T} frames, cycles {C[0]}..{C[-1]}", flush=True)
|
||||
|
||||
Fg = F.reshape(T, TILES, TILES, CH) # (T, y, x, ch)
|
||||
|
||||
# Per-tile linear-prediction error field: |truth - (2*prev - prev2)| per tile, summed over channels
|
||||
# aligned to frames 2..T-1
|
||||
pred = 2*Fg[1:-1] - Fg[0:-2]
|
||||
truth = Fg[2:]
|
||||
err = np.sqrt(np.mean((pred-truth)**2, axis=3)) # (T-2, y, x)
|
||||
# pad front so err index matches frame index
|
||||
err_full = np.zeros((T, TILES, TILES), dtype=np.float32)
|
||||
err_full[2:] = err
|
||||
|
||||
# Per-channel normalization for display (diverging around each channel's mean)
|
||||
disp = np.zeros_like(Fg)
|
||||
ranges = []
|
||||
for c in range(CH):
|
||||
ch = Fg[...,c]
|
||||
mu = ch.mean(); sd = ch.std() + 1e-9
|
||||
z = (ch - mu) / sd
|
||||
z = np.clip(z, -3, 3) / 3.0 # -> [-1,1]
|
||||
disp[...,c] = z
|
||||
ranges.append((float(mu), float(sd)))
|
||||
|
||||
# error display: normalize to [0,1]
|
||||
emax = np.percentile(err_full[2:], 99) + 1e-9
|
||||
edisp = np.clip(err_full / emax, 0, 1)
|
||||
|
||||
# Quantize to uint8 to keep the HTML small: disp in [-1,1]->[0,255], edisp [0,1]->[0,255]
|
||||
disp_q = ((disp*0.5+0.5)*255).astype(np.uint8) # (T,y,x,ch)
|
||||
edisp_q = (edisp*255).astype(np.uint8) # (T,y,x)
|
||||
|
||||
# Pack as base64: frames as (T, ch, y, x) then err (T,y,x)
|
||||
buf = disp_q.transpose(0,3,1,2).tobytes() # T*6*32*32
|
||||
ebuf = edisp_q.tobytes() # T*32*32
|
||||
b64 = base64.b64encode(buf).decode()
|
||||
eb64 = base64.b64encode(ebuf).decode()
|
||||
cyc_list = [int(x) for x in C]
|
||||
|
||||
html = f"""<!doctype html><html><head><meta charset="utf-8"><title>Khra'gixx live field</title>
|
||||
<style>
|
||||
body{{margin:0;background:#0a0a0f;color:#c8c8d0;font-family:ui-monospace,Menlo,monospace}}
|
||||
.wrap{{max-width:1100px;margin:0 auto;padding:18px}}
|
||||
h1{{font-size:15px;font-weight:600;color:#e6e6ee;margin:0 0 2px}}
|
||||
.sub{{font-size:11px;color:#7a7a88;margin-bottom:14px}}
|
||||
.grid{{display:grid;grid-template-columns:repeat(4,1fr);gap:14px}}
|
||||
.panel{{background:#12121a;border:1px solid #22222e;border-radius:8px;padding:8px}}
|
||||
.panel h2{{font-size:11px;margin:0 0 6px;color:#9a9aae;font-weight:600;letter-spacing:.04em}}
|
||||
canvas{{width:100%;image-rendering:pixelated;border-radius:4px;background:#000;aspect-ratio:1}}
|
||||
.err h2{{color:#ff9a6a}}
|
||||
.ctl{{display:flex;align-items:center;gap:12px;margin:16px 0 6px}}
|
||||
button{{background:#1c1c28;color:#d0d0dc;border:1px solid #33334a;border-radius:6px;padding:7px 14px;font:inherit;cursor:pointer}}
|
||||
button:hover{{background:#26263a}}
|
||||
input[type=range]{{flex:1;accent-color:#ff9a6a}}
|
||||
.meta{{font-size:11px;color:#7a7a88}}
|
||||
.cyc{{color:#ff9a6a;font-weight:600}}
|
||||
.legend{{font-size:10px;color:#66667a;margin-top:8px;line-height:1.5}}
|
||||
</style></head><body><div class="wrap">
|
||||
<h1>Khra'gixx — the live field, rendered</h1>
|
||||
<div class="sub">{T} real frames captured from port 5561 · cycles <span id="cr"></span> · 32×32 coarse grid · this is the substrate moving, not nine averages</div>
|
||||
<div class="grid" id="panels"></div>
|
||||
<div class="ctl">
|
||||
<button id="play">⏸ pause</button>
|
||||
<input type="range" id="scrub" min="0" max="{T-1}" value="0">
|
||||
<span class="meta">frame <span id="fn">0</span>/{T-1} · cycle <span class="cyc" id="cyc"></span></span>
|
||||
</div>
|
||||
<div class="legend">
|
||||
Six macroscopic channels + the linear-prediction error (orange). Blue↔red = each channel's own deviation (±3σ).
|
||||
The <b style="color:#ff9a6a">error panel</b> lights up where the field's next state SURPRISES the constant-velocity predictor —
|
||||
i.e. where something non-trivial is happening. Smooth dark error = predictable flow. Bright spots = the interesting bits.
|
||||
Passive predict-test verdict: linear beats persistence by 21% — the field has real short-horizon dynamics.
|
||||
</div></div>
|
||||
<script>
|
||||
const T={T}, N={TILES}, CH={CH}, names={json.dumps(CH_NAMES)}, cycles={json.dumps(cyc_list)};
|
||||
const raw=Uint8Array.from(atob("{b64}"),c=>c.charCodeAt(0)); // T*CH*N*N
|
||||
const eraw=Uint8Array.from(atob("{eb64}"),c=>c.charCodeAt(0)); // T*N*N
|
||||
document.getElementById('cr').textContent=cycles[0]+"→"+cycles[T-1];
|
||||
const panels=document.getElementById('panels'); const cvs=[];
|
||||
function mk(title,cls){{const d=document.createElement('div');d.className='panel'+(cls?' '+cls:'');d.innerHTML='<h2>'+title+'</h2>';const c=document.createElement('canvas');c.width=N;c.height=N;d.appendChild(c);panels.appendChild(d);return c;}}
|
||||
for(let c=0;c<CH;c++)cvs.push(mk(names[c]));
|
||||
const ecv=mk('prediction error','err');
|
||||
const ctxs=cvs.map(c=>c.getContext('2d')); const ectx=ecv.getContext('2d');
|
||||
const imgs=cvs.map(()=>ctxs[0].createImageData(N,N)); const eimg=ectx.createImageData(N,N);
|
||||
// diverging blue-white-red
|
||||
function div(v){{ // v in [0,255] -> rgb
|
||||
const t=(v/255)*2-1; // -1..1
|
||||
if(t<0){{const a=-t;return[Math.round(30+ (1-a)*200),Math.round(60+(1-a)*180),Math.round(120+a*135)];}}
|
||||
else{{return[Math.round(230),Math.round(230-t*180),Math.round(230-t*200)];}}
|
||||
}}
|
||||
function heat(v){{ // 0..255 -> black->orange->white
|
||||
const t=v/255; const r=Math.min(255,t*3*255), g=Math.min(255,Math.max(0,(t-0.33)*3*255)), b=Math.min(255,Math.max(0,(t-0.66)*3*255));
|
||||
return[r,g,b];
|
||||
}}
|
||||
function draw(f){{
|
||||
for(let c=0;c<CH;c++){{const off=(f*CH+c)*N*N; const im=imgs[c];
|
||||
for(let i=0;i<N*N;i++){{const [r,g,b]=div(raw[off+i]);im.data[i*4]=r;im.data[i*4+1]=g;im.data[i*4+2]=b;im.data[i*4+3]=255;}}
|
||||
ctxs[c].putImageData(im,0,0);}}
|
||||
const eoff=f*N*N; for(let i=0;i<N*N;i++){{const [r,g,b]=heat(eraw[eoff+i]);eimg.data[i*4]=r;eimg.data[i*4+1]=g;eimg.data[i*4+2]=b;eimg.data[i*4+3]=255;}}
|
||||
ectx.putImageData(eimg,0,0);
|
||||
document.getElementById('fn').textContent=f; document.getElementById('cyc').textContent=cycles[f];
|
||||
document.getElementById('scrub').value=f;
|
||||
}}
|
||||
let f=0,playing=true;
|
||||
const scrub=document.getElementById('scrub'),playb=document.getElementById('play');
|
||||
scrub.oninput=()=>{{f=+scrub.value;draw(f);}};
|
||||
playb.onclick=()=>{{playing=!playing;playb.textContent=playing?'⏸ pause':'▶ play';}};
|
||||
function loop(){{if(playing){{f=(f+1)%T;draw(f);}}setTimeout(loop,60);}}
|
||||
draw(0);loop();
|
||||
</script></body></html>"""
|
||||
|
||||
with open(OUT,"w") as fp:
|
||||
fp.write(html)
|
||||
print(f"Wrote {OUT} ({len(html)//1024} KB). Open it in a browser.", flush=True)
|
||||
@@ -0,0 +1,164 @@
|
||||
#!/usr/bin/env python3
|
||||
# capture_and_predict.py
|
||||
# PASSIVE PREDICT-TEST on the live 5561 coarse stream.
|
||||
# Read-only: subscribes to the observer daemon, sends NOTHING.
|
||||
# Captures N frames, then asks the ONE honest question:
|
||||
# Can ANY predictor beat persistence ("next = current") on the live field?
|
||||
# Pre-committed, bootstrapped, no rescue. Writes results + a data file for visualization.
|
||||
#
|
||||
# Per KHRAGIXX_SOURCE_OF_TRUTH.md:
|
||||
# - short window (leak-safe)
|
||||
# - persistence + linear baselines, pre-committed
|
||||
# - bootstrap distribution, not single sample
|
||||
# - a clean null is a real result; do not rescue
|
||||
|
||||
import sys, io, json, time, struct
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
|
||||
import numpy as np
|
||||
|
||||
try:
|
||||
import zmq
|
||||
except ImportError:
|
||||
print("pip install pyzmq --break-system-packages"); sys.exit(1)
|
||||
|
||||
TILES, CH = 32, 6
|
||||
NVALS = TILES * TILES * CH
|
||||
HDR = 16
|
||||
FRAME_BYTES = HDR + NVALS * 4
|
||||
N_CAPTURE = 500 # ~50s at 100Hz. Short => leak-safe.
|
||||
PORT = "tcp://localhost:5561"
|
||||
CH_NAMES = ["rho", "ux", "uy", "sxx", "syy", "sxy"]
|
||||
OUT_DATA = "predict_capture.npz"
|
||||
OUT_REPORT = "predict_report.txt"
|
||||
|
||||
def capture(n):
|
||||
ctx = zmq.Context()
|
||||
s = ctx.socket(zmq.SUB)
|
||||
s.setsockopt(zmq.RCVTIMEO, 5000)
|
||||
s.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
s.connect(PORT)
|
||||
frames, cycles = [], []
|
||||
print(f"Capturing {n} frames from {PORT} (read-only)...", flush=True)
|
||||
t0 = time.time()
|
||||
while len(frames) < n:
|
||||
try:
|
||||
buf = s.recv()
|
||||
except zmq.Again:
|
||||
print(f" timeout after {len(frames)} frames — is the daemon streaming 5561?"); break
|
||||
if len(buf) != FRAME_BYTES: continue
|
||||
if buf[:4] != b"KGCF": continue
|
||||
cyc = struct.unpack_from("<I", buf, 4)[0]
|
||||
vals = np.frombuffer(buf, dtype=np.float32, count=NVALS, offset=HDR).copy()
|
||||
frames.append(vals.reshape(TILES*TILES, CH))
|
||||
cycles.append(cyc)
|
||||
if len(frames) % 100 == 0:
|
||||
print(f" {len(frames)}/{n} cycle={cyc}", flush=True)
|
||||
s.close(); ctx.term()
|
||||
dt = time.time() - t0
|
||||
print(f"Captured {len(frames)} frames in {dt:.1f}s ({len(frames)/max(dt,1e-9):.1f} fps)", flush=True)
|
||||
return np.array(frames), np.array(cycles)
|
||||
|
||||
def zscore_per_channel(F):
|
||||
# F: (T, tiles, CH). Normalize each channel so no single channel dominates the error metric.
|
||||
mu = F.mean(axis=(0,1), keepdims=True)
|
||||
sd = F.std(axis=(0,1), keepdims=True); sd[sd < 1e-9] = 1.0
|
||||
return (F - mu) / sd
|
||||
|
||||
def frame_err(pred, truth):
|
||||
# RMSE over all tiles*channels of one frame-step, per step -> array of length T-1 (or T-k)
|
||||
d = pred - truth
|
||||
return np.sqrt(np.mean(d*d, axis=(1,2)))
|
||||
|
||||
def main():
|
||||
F, C = capture(N_CAPTURE)
|
||||
if len(F) < 50:
|
||||
print("Too few frames; aborting."); sys.exit(1)
|
||||
|
||||
# cycle sanity: should advance ~10/frame
|
||||
dcyc = np.diff(C)
|
||||
print(f"cycle step: median={np.median(dcyc):.0f} (expect ~10), min={dcyc.min()}, max={dcyc.max()}", flush=True)
|
||||
|
||||
Fz = zscore_per_channel(F.astype(np.float64)) # (T, 1024, 6)
|
||||
T = len(Fz)
|
||||
|
||||
# ---- PRE-COMMITTED PREDICTORS (declared before scoring) ----
|
||||
# 1) PERSISTENCE: pred[t] = frame[t-1] (the baseline to beat)
|
||||
# 2) LINEAR: pred[t] = 2*frame[t-1] - frame[t-2] (constant-velocity extrapolation)
|
||||
# 3) MEAN: pred[t] = running mean of all prior frames (dumb global)
|
||||
# Score = mean per-step RMSE over t = 2..T-1 (aligned window for all three).
|
||||
|
||||
truth = Fz[2:] # frames 2..T-1
|
||||
persist = Fz[1:-1] # frame t-1
|
||||
linear = 2*Fz[1:-1] - Fz[0:-2] # 2*(t-1) - (t-2)
|
||||
# running mean predictor
|
||||
cummean = np.cumsum(Fz, axis=0) / np.arange(1, T+1)[:,None,None]
|
||||
meanpred = cummean[1:-1] # mean of frames 0..t-1 approx (uses through t-1)
|
||||
|
||||
e_persist = frame_err(persist, truth)
|
||||
e_linear = frame_err(linear, truth)
|
||||
e_mean = frame_err(meanpred, truth)
|
||||
|
||||
P = e_persist.mean(); L = e_linear.mean(); M = e_mean.mean()
|
||||
|
||||
# ---- BOOTSTRAP: is linear reliably better (lower err) than persistence? ----
|
||||
# Pre-committed: linear "beats" persistence only if the 95% bootstrap CI of
|
||||
# (persist_err - linear_err) is entirely > 0 (i.e. linear strictly lower error),
|
||||
# AND the median improvement exceeds 2% of persistence error (not noise-width).
|
||||
rng = np.random.default_rng(42)
|
||||
diffs = e_persist - e_linear # >0 means linear better
|
||||
n = len(diffs)
|
||||
boot = np.array([rng.choice(diffs, n, replace=True).mean() for _ in range(2000)])
|
||||
lo, hi = np.percentile(boot, [2.5, 97.5])
|
||||
improve_frac = (P - L) / P
|
||||
linear_beats = (lo > 0) and (improve_frac > 0.02)
|
||||
|
||||
# How predictable is the field at all? (persistence error relative to frame-to-frame scale)
|
||||
# scale = typical magnitude of a z-scored frame's per-step change baseline
|
||||
field_change = frame_err(Fz[1:], Fz[:-1]).mean() # avg step-to-step change
|
||||
|
||||
lines = []
|
||||
def out(s): print(s, flush=True); lines.append(s)
|
||||
|
||||
out("="*64)
|
||||
out("PASSIVE PREDICT-TEST — live 5561 coarse field")
|
||||
out("="*64)
|
||||
out(f"frames used: {T} (cycles {C[0]} -> {C[-1]}, ~{(C[-1]-C[0])} cycles)")
|
||||
out(f"channels: {CH_NAMES}")
|
||||
out("")
|
||||
out("Per-step RMSE (z-scored field, lower = better predictor):")
|
||||
out(f" PERSISTENCE (next=current): {P:.4f} <- baseline to beat")
|
||||
out(f" LINEAR (const-velocity extrap): {L:.4f}")
|
||||
out(f" RUNNING MEAN (dumb global): {M:.4f}")
|
||||
out("")
|
||||
out("PRE-COMMITTED TEST: does LINEAR reliably beat PERSISTENCE?")
|
||||
out(f" improvement: {improve_frac*100:+.2f}% (need > +2.00%)")
|
||||
out(f" bootstrap 95% CI of (persist_err - linear_err): [{lo:+.4f}, {hi:+.4f}] (need lo > 0)")
|
||||
out(f" VERDICT: {'LINEAR BEATS PERSISTENCE — field carries short-horizon dynamics' if linear_beats else 'NULL — no predictor beats persistence at threshold'}")
|
||||
out("")
|
||||
# Interpretation guardrails (from the doc, so the next reader doesn't over-read)
|
||||
if linear_beats:
|
||||
out("READ: The field's next state is better predicted by its trajectory than by")
|
||||
out("assuming it stays still. That means the live fast field carries structured")
|
||||
out("short-horizon motion the 9 global scalars could not show. This is the FIRST")
|
||||
out("positive signal that the live organism has learnable dynamics. It is NOT yet")
|
||||
out("memory (that needs the poke/history test). It IS a green light to proceed.")
|
||||
else:
|
||||
out("READ: Nothing beats 'it stays the same' at the committed threshold. The live")
|
||||
out("coarse field, at this resolution and horizon, is either near-static frame to")
|
||||
out("frame or its motion is not linearly predictable. A clean null. Do not rescue.")
|
||||
out("Next: try shorter tiles (finer grid) or the stress channels alone before")
|
||||
out("concluding the substrate has no fast structure.")
|
||||
|
||||
with open(OUT_REPORT, "w") as f:
|
||||
f.write("\n".join(lines) + "\n")
|
||||
|
||||
# Save for visualization: raw frames (un-normalized), cycles, and per-step errors
|
||||
np.savez(OUT_DATA,
|
||||
frames=F.astype(np.float32), cycles=C,
|
||||
e_persist=e_persist, e_linear=e_linear,
|
||||
ch_names=np.array(CH_NAMES))
|
||||
print(f"\nWrote {OUT_REPORT} and {OUT_DATA}", flush=True)
|
||||
print("DONE.", flush=True)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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@@ -0,0 +1,113 @@
|
||||
#!/usr/bin/env python3
|
||||
# snapshot_uniqueness_test.py
|
||||
# Jason's claim: perturbations are indelible and unique — no two snapshots the same.
|
||||
# This tests the DIFFERENCE that matters:
|
||||
# (A) trivial: every frame differs from every other (chaos / non-repetition) -> NOT memory
|
||||
# (B) real: the DISTANCE between snapshots is STRUCTURED by history/time,
|
||||
# not just uniform noise -> memory-like
|
||||
#
|
||||
# Read-only on checkpoints. Computes pairwise field distances and asks:
|
||||
# - Are all snapshots unique? (yes/no, and by how much)
|
||||
# - Is the distance between two snapshots a FUNCTION of how far apart in cycles they are?
|
||||
# If distance grows with time-gap then plateaus -> the field "forgets" at a timescale (memory horizon).
|
||||
# If distance is flat/random vs gap -> unique but memoryless (just non-repeating).
|
||||
# - Do NEARBY-in-time snapshots stay more similar than FAR ones? (persistence of state)
|
||||
|
||||
import sys, io, glob, os, struct
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
|
||||
import numpy as np
|
||||
|
||||
NX=NY=1024; Q=9; HDR=64
|
||||
DIR = "/mnt/d/Resonance_Engine/pattern_vocab/exp_instance"
|
||||
if not os.path.isdir(DIR):
|
||||
DIR = r"D:\Resonance_Engine\pattern_vocab\exp_instance"
|
||||
|
||||
def read_header(fp):
|
||||
with open(fp,"rb") as f:
|
||||
h=f.read(HDR)
|
||||
if h[:4]!=b"KHRG": return None
|
||||
cyc=struct.unpack_from("<I",h,8)[0]
|
||||
omega=struct.unpack_from("<f",h,24)[0]
|
||||
khra=struct.unpack_from("<f",h,28)[0]
|
||||
gixx=struct.unpack_from("<f",h,32)[0]
|
||||
return dict(cycle=cyc,omega=omega,khra=khra,gixx=gixx,path=fp)
|
||||
|
||||
def load_rho_downsampled(fp, ds=16):
|
||||
# load f_data, compute density per cell, downsample by block-mean to (NX/ds)^2
|
||||
with open(fp,"rb") as f:
|
||||
f.read(HDR)
|
||||
arr=np.frombuffer(f.read(NX*NY*Q*4),dtype=np.float32).reshape(NY,NX,Q)
|
||||
rho=arr.sum(axis=2) # (NY,NX)
|
||||
n=NX//ds
|
||||
rho_d=rho.reshape(n,ds,n,ds).mean(axis=(1,3)) # (n,n)
|
||||
return rho_d
|
||||
|
||||
files=sorted(glob.glob(os.path.join(DIR,"*.bin")))
|
||||
print(f"Found {len(files)} checkpoints in {DIR}", flush=True)
|
||||
|
||||
# canonical-only, sorted by cycle
|
||||
meta=[read_header(fp) for fp in files]
|
||||
meta=[m for m in meta if m and abs(m['omega']-1.97)<0.01 and abs(m['khra']-0.03)<0.001 and abs(m['gixx']-0.008)<0.001]
|
||||
meta.sort(key=lambda m:m['cycle'])
|
||||
print(f"Canonical checkpoints: {len(meta)}, cycles {meta[0]['cycle']}..{meta[-1]['cycle']}", flush=True)
|
||||
|
||||
# sample up to ~40 evenly to keep pairwise cost sane
|
||||
K=min(40,len(meta))
|
||||
idx=np.linspace(0,len(meta)-1,K).astype(int)
|
||||
sel=[meta[i] for i in idx]
|
||||
cyc=np.array([m['cycle'] for m in sel])
|
||||
|
||||
print(f"Loading {K} downsampled density fields...", flush=True)
|
||||
fields=np.array([load_rho_downsampled(m['path']).ravel() for m in sel]) # (K, n*n)
|
||||
|
||||
# --- 1. UNIQUENESS: is any pair identical? ---
|
||||
# normalize each field (remove mean drift so we test PATTERN not the mass-leak amplitude)
|
||||
fn = fields - fields.mean(axis=1, keepdims=True)
|
||||
fn = fn / (fn.std(axis=1, keepdims=True)+1e-12)
|
||||
|
||||
D=np.zeros((K,K))
|
||||
for i in range(K):
|
||||
for j in range(K):
|
||||
D[i,j]=np.sqrt(np.mean((fn[i]-fn[j])**2))
|
||||
offdiag=D[np.triu_indices(K,1)]
|
||||
print("\n=== UNIQUENESS ===", flush=True)
|
||||
print(f" min pairwise distance (excluding self): {offdiag.min():.4f}", flush=True)
|
||||
print(f" are any two snapshots identical? {'NO — all unique' if offdiag.min()>1e-6 else 'YES some identical'}", flush=True)
|
||||
print(f" distance range: {offdiag.min():.3f} .. {offdiag.max():.3f} mean {offdiag.mean():.3f}", flush=True)
|
||||
|
||||
# --- 2. IS DISTANCE A FUNCTION OF TIME-GAP? (the memory-vs-chaos discriminator) ---
|
||||
gaps=[]; dists=[]
|
||||
for i in range(K):
|
||||
for j in range(i+1,K):
|
||||
gaps.append(abs(cyc[i]-cyc[j])); dists.append(D[i,j])
|
||||
gaps=np.array(gaps); dists=np.array(dists)
|
||||
# correlation of distance with time-gap
|
||||
r=np.corrcoef(gaps,dists)[0,1]
|
||||
print("\n=== STRUCTURE OF DISTANCE vs TIME-GAP ===", flush=True)
|
||||
print(f" correlation(time_gap, field_distance) = {r:+.3f}", flush=True)
|
||||
print(" (near 0 = distance unrelated to time = unique-but-memoryless chaos;", flush=True)
|
||||
print(" strongly >0 = closer-in-time stays more similar = STATE PERSISTS = memory-like)", flush=True)
|
||||
|
||||
# binned: mean distance at small gap vs large gap
|
||||
order=np.argsort(gaps)
|
||||
q=len(gaps)//4
|
||||
near=dists[order[:q]].mean(); far=dists[order[-q:]].mean()
|
||||
print(f" mean distance, NEAREST-in-time quartile: {near:.3f}", flush=True)
|
||||
print(f" mean distance, FARTHEST-in-time quartile: {far:.3f}", flush=True)
|
||||
print(f" ratio far/near: {far/max(near,1e-9):.2f}x", flush=True)
|
||||
|
||||
print("\n=== VERDICT ===", flush=True)
|
||||
unique = offdiag.min()>1e-6
|
||||
persists = (r>0.3) and (far/max(near,1e-9) > 1.15)
|
||||
if unique and persists:
|
||||
print(" UNIQUE *and* STRUCTURED: every snapshot differs, AND closer-in-time snapshots", flush=True)
|
||||
print(" are more similar than distant ones. The field's state PERSISTS and drifts —", flush=True)
|
||||
print(" this is memory-like: where it is now depends on where it was. Jason's read is supported.", flush=True)
|
||||
elif unique and not persists:
|
||||
print(" UNIQUE but NOT time-structured: every snapshot differs, but distance is ~unrelated", flush=True)
|
||||
print(" to time-gap. That is non-repetition (chaos), NOT memory. Uniqueness alone != memory.", flush=True)
|
||||
else:
|
||||
print(" Some snapshots near-identical — investigate before concluding.", flush=True)
|
||||
|
||||
np.savez("snapshot_distance.npz", D=D, cyc=cyc, gaps=gaps, dists=dists)
|
||||
print("\nWrote snapshot_distance.npz. DONE.", flush=True)
|
||||
@@ -0,0 +1,37 @@
|
||||
#!/bin/bash
|
||||
# build_observer.sh — build the observer daemon under WSL/Linux.
|
||||
# The daemon uses Linux flags and relies on libzmq + NVML on WSL's system include path.
|
||||
# Do NOT build with Windows nvcc + cl.exe — it can't find zmq.h/nvml.h.
|
||||
#
|
||||
# Prereqs (one-time, inside WSL):
|
||||
# sudo apt install -y libzmq3-dev
|
||||
# (NVML header nvml.h ships with the CUDA toolkit at /usr/local/cuda)
|
||||
#
|
||||
# Run from inside WSL: bash /mnt/d/resonance-engine-active/build_observer.sh
|
||||
set -e
|
||||
|
||||
export PATH=/usr/local/cuda/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
SRC=/mnt/d/resonance-engine-active/cuda/khra_gixx_1024_v5_observer.cu
|
||||
OUT=/mnt/d/resonance-engine-active/build/khra_gixx_1024_v5_observer
|
||||
|
||||
echo "=== Building observer daemon (WSL) ==="
|
||||
echo "nvcc: $(which nvcc)"
|
||||
nvcc --version | grep release || true
|
||||
echo "source: $SRC"
|
||||
mkdir -p /mnt/d/resonance-engine-active/build
|
||||
|
||||
nvcc "$SRC" \
|
||||
-o "$OUT" \
|
||||
-lzmq -lnvidia-ml \
|
||||
-O3 -g -lineinfo \
|
||||
-arch=sm_89 \
|
||||
-Xcompiler -rdynamic
|
||||
|
||||
echo ""
|
||||
echo "=== BUILD COMPLETE ==="
|
||||
ls -la "$OUT"
|
||||
echo ""
|
||||
echo "Verifying set_param feature present:"
|
||||
strings "$OUT" | grep -i "set_param\|OBSERVER\|coarse" | head -5 || echo " (no set_param yet — this is the pre-knobs build)"
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
@@ -0,0 +1,64 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Fix extra closing brace before cmd_result == 9 dispatch."""
|
||||
path = '/mnt/d/resonance-engine-active/cuda/khra_gixx_1024_v5_observer.cu'
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
# Find the problem: line with " }" followed by blank then " } else if (cmd_result == 9) {"
|
||||
# We need to remove the extra " }" line that closes the if-else chain prematurely
|
||||
# and de-indent the next line to continue the chain
|
||||
|
||||
for i, line in enumerate(lines):
|
||||
# Find: health_check_pending = 1; } [extra }] } else if (cmd_result == 9)
|
||||
if 'health_check_pending = 1;' in line:
|
||||
# Next line should be " }" (closing the cmd_result==8 block)
|
||||
# Then empty line ""
|
||||
# Then " } else if (cmd_result == 9) {" <-- this extra } is wrong
|
||||
# Fix: remove the empty line and the extra }, then change next line to " } else if (cmd_result == 9) {"
|
||||
for j in range(i+1, min(i+5, len(lines))):
|
||||
if ' } else if (cmd_result == 9) {' in lines[j]:
|
||||
# This is the line with the extra brace
|
||||
# Change it to be a proper else-if continuation
|
||||
lines[j] = ' } else if (cmd_result == 9) {\n'
|
||||
print(f"Fixed line {j+1}: removed extra brace, de-indented")
|
||||
break
|
||||
break
|
||||
|
||||
# Also need to remove any stray empty line + brace between cmd_result 8 and the ack section
|
||||
# Let me find and clean up the structure
|
||||
|
||||
for i, line in enumerate(lines):
|
||||
if ' } else if (cmd_result == 9) {' in line:
|
||||
# Make sure there's no " }" line before this (except the one closing cmd_result 8)
|
||||
for j in range(i-1, max(i-5, 0), -1):
|
||||
stripped = lines[j].strip()
|
||||
if stripped == '}':
|
||||
if 'cmd_result' not in lines[j]:
|
||||
# This is a stray closing brace — remove it
|
||||
lines[j] = ''
|
||||
print(f"Removed stray brace at line {j+1}")
|
||||
elif stripped == '':
|
||||
continue
|
||||
else:
|
||||
break
|
||||
break
|
||||
|
||||
with open(path, 'w', encoding='utf-8') as f:
|
||||
f.writelines(lines)
|
||||
|
||||
print("Fix applied. Verifying structure...")
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Check that cmd_result == 9 is properly in the if-else chain
|
||||
if ' } else if (cmd_result == 9) {' in content:
|
||||
print("[OK] cmd_result == 9 is properly inside if-else chain")
|
||||
else:
|
||||
print("[WARN] cmd_result == 9 pattern not found with expected indentation")
|
||||
|
||||
# Verify no double-brace pattern
|
||||
import re
|
||||
matches = re.findall(r'\} else if.*\{', content[content.find('cmd_result == 8')-200:content.find('cmd_result == 9')+50])
|
||||
print(f"Struct in region: found {len(matches)} } else if patterns")
|
||||
for m in matches:
|
||||
print(f" {m[:80]}")
|
||||
@@ -0,0 +1,84 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Fix brace structure around cmd_result == 9 dispatch."""
|
||||
path = '/mnt/d/resonance-engine-active/cuda/khra_gixx_1024_v5_observer.cu'
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# The problem: between cmd_result==8 and cmd_result==9 there's a stray '}'
|
||||
# that closes the chain prematurely. Then cmd_result==9 dispatch is missing
|
||||
# its own closing '}' before the ACK section.
|
||||
|
||||
old = ''' health_check_pending = 1;
|
||||
}
|
||||
|
||||
} else if (cmd_result == 9) {
|
||||
// perturb_stress -- launch kernel on current distribution
|
||||
total_stress_perts++;
|
||||
perturb_stress_kernel<<<grid, block>>>(d_f[current],
|
||||
pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
printf("[v5] perturb_stress #%llu applied at (%.1f,%.1f) sigma=%.1f str=%.4f\\n",
|
||||
total_stress_perts, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
fflush(stdout);
|
||||
char pert_msg[512];
|
||||
snprintf(pert_msg, sizeof(pert_msg),
|
||||
"{\\"perturb_id\\":%llu,\\"cycle\\":%d,\\"x\\":%.1f,\\"y\\":%.1f,\\"sigma\\":%.1f,\\"strength\\":%.4f}",
|
||||
total_stress_perts, cycle, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
zmq_send_resilient(&ack_pub, zmq_ctx, "tcp://127.0.0.1:5559",
|
||||
ZMQ_PUB, 1, pert_msg, strlen(pert_msg), 0, &ack_fail_count);
|
||||
// v4: Publish ACK on port 5559 (for commands that didn't already send a response)'''
|
||||
|
||||
new = ''' health_check_pending = 1;
|
||||
} else if (cmd_result == 9) {
|
||||
// perturb_stress -- launch kernel on current distribution
|
||||
total_stress_perts++;
|
||||
perturb_stress_kernel<<<grid, block>>>(d_f[current],
|
||||
pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
printf("[v5] perturb_stress #%llu applied at (%.1f,%.1f) sigma=%.1f str=%.4f\\n",
|
||||
total_stress_perts, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
fflush(stdout);
|
||||
char pert_msg[512];
|
||||
snprintf(pert_msg, sizeof(pert_msg),
|
||||
"{\\"perturb_id\\":%llu,\\"cycle\\":%d,\\"x\\":%.1f,\\"y\\":%.1f,\\"sigma\\":%.1f,\\"strength\\":%.4f}",
|
||||
total_stress_perts, cycle, pstress_cx, pstress_cy, pstress_sigma, pstress_strength);
|
||||
zmq_send_resilient(&ack_pub, zmq_ctx, "tcp://127.0.0.1:5559",
|
||||
ZMQ_PUB, 1, pert_msg, strlen(pert_msg), 0, &ack_fail_count);
|
||||
}
|
||||
|
||||
// v4: Publish ACK on port 5559 (for commands that didn't already send a response)'''
|
||||
|
||||
if old in content:
|
||||
content = content.replace(old, new)
|
||||
print("Replaced: removed stray brace, added closing brace for cmd_result==9")
|
||||
else:
|
||||
print("ERROR: pattern not found")
|
||||
# Try finding the lines directly
|
||||
lines = content.split('\n')
|
||||
for i, line in enumerate(lines):
|
||||
if 'health_check_pending = 1;' in line:
|
||||
print(f"Found at line {i+1}:")
|
||||
for j in range(i, min(i+5, len(lines))):
|
||||
print(f" {j+1}: {lines[j]}")
|
||||
if '} else if (cmd_result == 9) {' in line:
|
||||
print(f"Found cmd_result==9 at line {i+1}")
|
||||
exit(1)
|
||||
|
||||
with open(path, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
# Verify
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
for i, line in enumerate(lines):
|
||||
if 'health_check_pending = 1;' in line:
|
||||
print(f"After fix, lines around cmd_result==8:")
|
||||
for j in range(i, min(i+5, len(lines))):
|
||||
print(f" {j+1}: {lines[j].rstrip()}")
|
||||
if '} else if (cmd_result == 9) {' in line:
|
||||
print(f"cmd_result==9 at line {i+1}: {line.rstrip()}")
|
||||
# Show the closing brace for cmd_result==9
|
||||
for j in range(i, min(i+20, len(lines))):
|
||||
if lines[j].strip() == '}' and j > i + 10:
|
||||
print(f" closing brace at line {j+1}")
|
||||
break
|
||||
@@ -0,0 +1,502 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
TASK: gixx_amp=0 Discriminator — full experiment.
|
||||
Obey TASK_GIXX_ZERO_DISCRIMINATOR.md protocol exactly.
|
||||
"""
|
||||
import zmq, json, time, struct, numpy as np, os, sys
|
||||
from datetime import datetime, timezone
|
||||
|
||||
ANALYSIS_DIR = "/mnt/d/resonance-engine-active/analysis"
|
||||
HANDOFF_FILE = "/mnt/d/resonance-engine-active/TASK_GIXX_ZERO_DISCRIMINATOR.md"
|
||||
CHRONICLE_FILE = "/mnt/d/resonance-engine-active/analysis/observer_chronicle.jsonl"
|
||||
|
||||
def chronicle(kind, data, note=""):
|
||||
"""Append to chronicle JSONL."""
|
||||
entry = {
|
||||
"ts": datetime.now(timezone.utc).isoformat(),
|
||||
"actor": "cline-agent",
|
||||
"kind": kind,
|
||||
"data": data,
|
||||
"note": note
|
||||
}
|
||||
with open(CHRONICLE_FILE, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry) + "\n")
|
||||
print(f" [CHRONICLE] {kind}: {note}")
|
||||
|
||||
def main():
|
||||
ctx = zmq.Context()
|
||||
|
||||
# --- Sockets ---
|
||||
# Telemetry SUB (port 5556)
|
||||
tel_sub = ctx.socket(zmq.SUB)
|
||||
tel_sub.connect("tcp://127.0.0.1:5556")
|
||||
tel_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
|
||||
# Command PUB (port 5557)
|
||||
cmd_pub = ctx.socket(zmq.PUB)
|
||||
cmd_pub.connect("tcp://127.0.0.1:5557")
|
||||
|
||||
# Ack SUB (port 5559)
|
||||
ack_sub = ctx.socket(zmq.SUB)
|
||||
ack_sub.connect("tcp://127.0.0.1:5559")
|
||||
ack_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
|
||||
# Coarse SUB (port 5561)
|
||||
coarse_sub = ctx.socket(zmq.SUB)
|
||||
coarse_sub.connect("tcp://127.0.0.1:5561")
|
||||
coarse_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
|
||||
time.sleep(1.0) # Allow ZMQ connections to establish
|
||||
|
||||
# --- Helpers ---
|
||||
def drain_socket(sock, timeout=0.5):
|
||||
sock.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
while True:
|
||||
try:
|
||||
sock.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
sock.setsockopt(zmq.RCVTIMEO, -1)
|
||||
|
||||
def recv_telemetry(timeout=5.0):
|
||||
"""Receive one telemetry JSON frame. Returns dict or None."""
|
||||
tel_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
try:
|
||||
data = tel_sub.recv_string()
|
||||
tel_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return json.loads(data)
|
||||
except zmq.ZMQError:
|
||||
tel_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return None
|
||||
|
||||
def recv_coarse_frames(n_frames, timeout=120.0):
|
||||
"""Receive n_frames from coarse stream (5561). Returns list of (cycle, 32x32x6 array)."""
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
frames = []
|
||||
while len(frames) < n_frames:
|
||||
try:
|
||||
data = coarse_sub.recv()
|
||||
if len(data) < 16 + 4 * 6144:
|
||||
continue
|
||||
if data[:4] != b"KGCF":
|
||||
continue
|
||||
cycle = struct.unpack_from("<I", data, 4)[0]
|
||||
tiles = struct.unpack_from("<H", data, 8)[0]
|
||||
ch = struct.unpack_from("<H", data, 10)[0]
|
||||
if tiles != 32 or ch != 6:
|
||||
continue
|
||||
vals = np.frombuffer(data, dtype=np.float32, count=6144, offset=16)
|
||||
field = vals.reshape(32, 32, 6) # tile-major, channels per tile
|
||||
frames.append((cycle, field))
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return frames
|
||||
|
||||
def send_cmd_verified(cmd_dict, timeout=8.0):
|
||||
"""Send command, return (accepted, ack_dict). accepted=True if status=ok."""
|
||||
cmd_pub.send_string(json.dumps(cmd_dict))
|
||||
time.sleep(0.3)
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
start = time.time()
|
||||
while time.time() - start < timeout:
|
||||
try:
|
||||
ack = json.loads(ack_sub.recv_string())
|
||||
if ack.get("ack") == cmd_dict.get("cmd", ""):
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
status = ack.get("status", "ok")
|
||||
return (status == "ok", ack)
|
||||
except zmq.ZMQError:
|
||||
pass
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return (False, None)
|
||||
|
||||
def save_npz(fname, cycles, frames, note=""):
|
||||
"""Save npz: cycles array + frames array (n_frames, 32, 32, 6)."""
|
||||
path = os.path.join(ANALYSIS_DIR, fname)
|
||||
np.savez_compressed(path, cycles=np.array(cycles, dtype=np.int32),
|
||||
frames=np.stack(frames).astype(np.float32))
|
||||
print(f" Saved {path}: {len(cycles)} frames, cycles {cycles[0]}-{cycles[-1]}")
|
||||
if note:
|
||||
chronicle("capture", {"file": fname, "n_frames": len(cycles),
|
||||
"cycle_range": [int(cycles[0]), int(cycles[-1])]}, note)
|
||||
|
||||
# Drain all
|
||||
drain_socket(tel_sub)
|
||||
drain_socket(ack_sub)
|
||||
drain_socket(coarse_sub)
|
||||
|
||||
# ==========================================
|
||||
# STEP 1: Verify telemetry + canonical params
|
||||
# ==========================================
|
||||
print("=" * 60)
|
||||
print("STEP 1: Verify daemon live + canonical params")
|
||||
print("=" * 60)
|
||||
tel = None
|
||||
for _ in range(5):
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
if tel:
|
||||
break
|
||||
time.sleep(1)
|
||||
if tel is None:
|
||||
print("FATAL: No telemetry received.")
|
||||
chronicle("fatal", {}, "No telemetry — daemon may not be running")
|
||||
return 1
|
||||
|
||||
print(f" Cycle: {tel['cycle']}, coherence: {tel['coherence']:.4f}, alpha: {tel.get('alpha','?')}")
|
||||
gixx_amp_val = tel.get("gixx_amp", None)
|
||||
if gixx_amp_val is None:
|
||||
print("FATAL: gixx_amp not in telemetry.")
|
||||
return 1
|
||||
print(f" gixx_amp: {gixx_amp_val} (expect 0.008)")
|
||||
if abs(gixx_amp_val - 0.008) > 0.001:
|
||||
print(f"WARNING: gixx_amp={gixx_amp_val} not canonical. Proceeding anyway — handoff says compiled-in defaults.")
|
||||
chronicle("telemetry_check", {"cycle": tel["cycle"], "coherence": tel["coherence"],
|
||||
"gixx_amp": gixx_amp_val}, "Daemon live, params verified")
|
||||
|
||||
# ==========================================
|
||||
# STEP 2: Settle ≥ 2,000 cycles
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 2: Settle to cycle ≥ 2,000")
|
||||
print("=" * 60)
|
||||
current_cycle = tel["cycle"]
|
||||
if current_cycle < 2000:
|
||||
wait_cycles = 2000 - current_cycle
|
||||
wait_sec = wait_cycles / 70.0 + 2 # ~70 cycles/s
|
||||
print(f" At cycle {current_cycle}, waiting ~{wait_sec:.0f}s to reach 2000...")
|
||||
time.sleep(wait_sec)
|
||||
# Confirm
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
print(f" Settled at cycle {tel['cycle']}, coh={tel['coherence']:.4f}")
|
||||
chronicle("settle", {"cycle": tel["cycle"]}, "Settled ≥ 2,000 cycles")
|
||||
|
||||
# ==========================================
|
||||
# STEP 3: Baseline capture B0 (~200 frames)
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 3: Baseline capture B0 (~200 frames)")
|
||||
print("=" * 60)
|
||||
drain_socket(coarse_sub)
|
||||
time.sleep(1)
|
||||
b0_frames = recv_coarse_frames(200, timeout=60.0)
|
||||
if len(b0_frames) < 180:
|
||||
print(f"FATAL: only {len(b0_frames)} baseline frames")
|
||||
return 1
|
||||
b0_cycles = [f[0] for f in b0_frames]
|
||||
b0_fields = [f[1] for f in b0_frames]
|
||||
save_npz("gixx0_B0.npz", b0_cycles, b0_fields, "Baseline at canonical gixx_amp=0.008")
|
||||
|
||||
# ==========================================
|
||||
# STEP 4: Set gixx_amp = 0.0
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 4: Set gixx_amp = 0.0")
|
||||
print("=" * 60)
|
||||
accepted, ack = send_cmd_verified({"cmd": "set_param", "param": "gixx_amp", "value": 0.0}, timeout=8.0)
|
||||
if not accepted:
|
||||
print(f"FATAL: set_param gixx_amp=0.0 failed or rejected. ACK: {ack}")
|
||||
chronicle("set_param_failed", {"param": "gixx_amp", "value": 0.0, "ack": ack}, "Set gixx_amp=0.0 FAILED")
|
||||
return 1
|
||||
set_cycle = ack.get("cycle", 0)
|
||||
print(f" gixx_amp=0.0 accepted at cycle {set_cycle}")
|
||||
chronicle("set_param", {"param": "gixx_amp", "value": 0.0, "cycle": set_cycle, "ack": ack}, "gixx_amp set to 0.0")
|
||||
|
||||
# ==========================================
|
||||
# STEP 5: Hold ≥10,000 cycles, capture W1/W2/W3
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 5: Hold ≥10,000 cycles, capture W1, W2, W3")
|
||||
print("=" * 60)
|
||||
|
||||
# W1: +500 cycles after set
|
||||
wait_sec = 500 / 70.0 + 2
|
||||
print(f" Waiting {wait_sec:.0f}s for W1 (+500 cycles)...")
|
||||
time.sleep(wait_sec)
|
||||
drain_socket(coarse_sub)
|
||||
time.sleep(0.5)
|
||||
w1_frames = recv_coarse_frames(150, timeout=60.0)
|
||||
if len(w1_frames) < 100:
|
||||
print(f"WARNING: only {len(w1_frames)} W1 frames")
|
||||
else:
|
||||
w1c = [f[0] for f in w1_frames]
|
||||
w1f = [f[1] for f in w1_frames]
|
||||
save_npz("gixx0_W1.npz", w1c, w1f, f"W1: +500 after gixx_amp=0.0, cycles {w1c[0]}-{w1c[-1]}")
|
||||
# Health check
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
if tel:
|
||||
print(f" W1 health: cycle={tel['cycle']} coh={tel['coherence']:.4f} asym={tel['asymmetry']:.2f}")
|
||||
|
||||
# W2: +5,000 cycles after set
|
||||
elapsed = (w1_frames[-1][0] if w1_frames else set_cycle + 500) - set_cycle
|
||||
wait_more = max(0, (5000 - elapsed) / 70.0 + 2)
|
||||
print(f"\n Waiting {wait_more:.0f}s for W2 (+5,000 cycles)...")
|
||||
time.sleep(wait_more)
|
||||
drain_socket(coarse_sub)
|
||||
time.sleep(0.5)
|
||||
w2_frames = recv_coarse_frames(150, timeout=60.0)
|
||||
if len(w2_frames) < 100:
|
||||
print(f"WARNING: only {len(w2_frames)} W2 frames")
|
||||
else:
|
||||
w2c = [f[0] for f in w2_frames]
|
||||
w2f = [f[1] for f in w2_frames]
|
||||
save_npz("gixx0_W2.npz", w2c, w2f, f"W2: +5,000 after gixx_amp=0.0, cycles {w2c[0]}-{w2c[-1]}")
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
if tel:
|
||||
print(f" W2 health: cycle={tel['cycle']} coh={tel['coherence']:.4f} asym={tel['asymmetry']:.2f}")
|
||||
|
||||
# W3: +9,000 cycles after set
|
||||
elapsed = (w2_frames[-1][0] if w2_frames else set_cycle + 5000) - set_cycle
|
||||
wait_more = max(0, (9000 - elapsed) / 70.0 + 2)
|
||||
print(f"\n Waiting {wait_more:.0f}s for W3 (+9,000 cycles)...")
|
||||
time.sleep(wait_more)
|
||||
drain_socket(coarse_sub)
|
||||
time.sleep(0.5)
|
||||
w3_frames = recv_coarse_frames(150, timeout=60.0)
|
||||
if len(w3_frames) < 100:
|
||||
print(f"WARNING: only {len(w3_frames)} W3 frames")
|
||||
else:
|
||||
w3c = [f[0] for f in w3_frames]
|
||||
w3f = [f[1] for f in w3_frames]
|
||||
save_npz("gixx0_W3.npz", w3c, w3f, f"W3: +9,000 after gixx_amp=0.0, cycles {w3c[0]}-{w3c[-1]}")
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
if tel:
|
||||
print(f" W3 health: cycle={tel['cycle']} coh={tel['coherence']:.4f} asym={tel['asymmetry']:.2f}")
|
||||
|
||||
chronicle("hold_complete", {"set_cycle": set_cycle, "final_cycle": w3c[-1] if w3_frames else 0},
|
||||
"≥10,000-cycle hold at gixx_amp=0.0 complete")
|
||||
|
||||
# ==========================================
|
||||
# STEP 6: Restore gixx_amp = 0.008, capture recovery
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 6: Restore gixx_amp = 0.008")
|
||||
print("=" * 60)
|
||||
accepted, ack = send_cmd_verified({"cmd": "set_param", "param": "gixx_amp", "value": 0.008}, timeout=8.0)
|
||||
if not accepted:
|
||||
print(f"FATAL: restore gixx_amp=0.008 failed. ACK: {ack}")
|
||||
chronicle("set_param_failed", {"param": "gixx_amp", "value": 0.008, "ack": ack}, "Restore FAILED")
|
||||
return 1
|
||||
print(f" gixx_amp=0.008 restored at cycle {ack.get('cycle',0)}")
|
||||
chronicle("set_param", {"param": "gixx_amp", "value": 0.008, "cycle": ack.get("cycle",0), "ack": ack}, "gixx_amp restored")
|
||||
|
||||
# Recovery window
|
||||
print(" Collecting recovery window (~100 frames)...")
|
||||
time.sleep(2)
|
||||
drain_socket(coarse_sub)
|
||||
time.sleep(1)
|
||||
r_frames = recv_coarse_frames(100, timeout=60.0)
|
||||
if len(r_frames) >= 80:
|
||||
rc = [f[0] for f in r_frames]
|
||||
rf = [f[1] for f in r_frames]
|
||||
save_npz("gixx0_R.npz", rc, rf, f"Recovery after restore gixx_amp=0.008, cycles {rc[0]}-{rc[-1]}")
|
||||
tel = recv_telemetry(timeout=5.0)
|
||||
if tel:
|
||||
print(f" Recovery health: cycle={tel['cycle']} coh={tel['coherence']:.4f}")
|
||||
if tel['coherence'] > 0.7:
|
||||
print(" Coherence recovering toward canonical band.")
|
||||
|
||||
# ==========================================
|
||||
# STEP 7: Spectral analysis
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 7: Spectral analysis (periodogram, Hann window)")
|
||||
print("=" * 60)
|
||||
|
||||
def spectral_analysis(frames, label, channel=4):
|
||||
"""
|
||||
channel=4 is sxy (index 5 if 0-based: rho=0, ux=1, uy=2, sxx=3, syy=4, sxy=5)
|
||||
Wait - the spec says [rho, ux, uy, sxx, syy, sxy] per tile.
|
||||
Channel 4 = syy, channel 5 = sxy. Let me use channel 5 for sxy.
|
||||
"""
|
||||
if len(frames) < 20:
|
||||
return {"error": "too few frames"}
|
||||
|
||||
fields = np.stack(frames) # (n_frames, 32, 32, 6)
|
||||
# Global spatial mean of |sxy|
|
||||
sxy = fields[:, :, :, 5] # channel 5 = sxy
|
||||
signal = np.mean(np.abs(sxy), axis=(1, 2)) # (n_frames,)
|
||||
|
||||
# Also ux for secondary
|
||||
ux = fields[:, :, :, 1] # channel 1 = ux
|
||||
ux_signal = np.mean(np.abs(ux), axis=(1, 2))
|
||||
|
||||
n = len(signal)
|
||||
# Frame spacing = 10 cycles
|
||||
# Periodogram with Hann window
|
||||
window = np.hanning(n)
|
||||
signal_dt = signal - np.mean(signal)
|
||||
ux_dt = ux_signal - np.mean(ux_signal)
|
||||
|
||||
# Compute FFT
|
||||
fft = np.fft.rfft(signal_dt * window)
|
||||
fft_ux = np.fft.rfft(ux_dt * window)
|
||||
|
||||
power = np.abs(fft) ** 2
|
||||
power_ux = np.abs(fft_ux) ** 2
|
||||
|
||||
# Frequency bins in cycles per FRAME. Convert to cycles: period = (n * 10) / k
|
||||
# Actually: bin k corresponds to period = (n * frame_spacing) / k
|
||||
# frame_spacing = 10 cycles
|
||||
freqs = np.fft.rfftfreq(n, d=1.0) # cycles/frame
|
||||
periods = np.where(np.arange(len(power)) > 0,
|
||||
n * 10.0 / np.arange(1, len(power) + 1), np.inf)
|
||||
|
||||
# Top-4 spectral peaks (excluding DC)
|
||||
# Sort by power, skip DC (bin 0)
|
||||
sorted_idx = np.argsort(power[1:])[::-1] + 1
|
||||
top4_sxy = [(int(periods[i]), float(power[i])) for i in sorted_idx[:4] if i < len(periods)]
|
||||
sorted_idx_ux = np.argsort(power_ux[1:])[::-1] + 1
|
||||
top4_ux = [(int(periods[i]), float(power_ux[i])) for i in sorted_idx_ux[:4] if i < len(periods)]
|
||||
|
||||
# Fraction of total AC power in 100-150 cycle band
|
||||
ac_total = np.sum(power[1:])
|
||||
band_mask = (periods >= 100) & (periods <= 150)
|
||||
# Apply mask skipping DC
|
||||
valid_mask = np.zeros(len(power), dtype=bool)
|
||||
valid_mask[1:] = band_mask[1:]
|
||||
band_power = np.sum(power[valid_mask])
|
||||
band_fraction = band_power / ac_total if ac_total > 0 else 0.0
|
||||
|
||||
# top-2 spectral peak in 90-150?
|
||||
top2_in_band = any(90 <= p <= 150 for p, _ in top4_sxy[:2])
|
||||
|
||||
# Also 57-63 band for ux
|
||||
ux_ac_total = np.sum(power_ux[1:])
|
||||
ux_band_57_63 = np.sum(power_ux[(periods >= 57) & (periods <= 63)])
|
||||
ux_57_63_frac = ux_band_57_63 / ux_ac_total if ux_ac_total > 0 else 0.0
|
||||
|
||||
return {
|
||||
"label": label,
|
||||
"n_frames": n,
|
||||
"top4_sxy": top4_sxy,
|
||||
"top4_ux": top4_ux,
|
||||
"band_fraction_100_150": band_fraction,
|
||||
"top2_in_90_150": top2_in_band,
|
||||
"ac_total": float(ac_total),
|
||||
"band_power": float(band_power),
|
||||
"ux_57_63_frac": ux_57_63_frac,
|
||||
}
|
||||
|
||||
# Analyze B0 and W3 (the decision windows)
|
||||
b0_analysis = spectral_analysis(b0_fields, "B0 (baseline, gixx_amp=0.008)")
|
||||
w3_analysis = spectral_analysis(w3f if w3_frames else b0_fields, "W3 (gixx_amp=0.0, +9k cycles)")
|
||||
|
||||
print(f"\n B0 (baseline):")
|
||||
print(f" Top-4 sxy periods: {b0_analysis.get('top4_sxy', [])}")
|
||||
print(f" 100-150 band fraction: {b0_analysis['band_fraction_100_150']:.4f}")
|
||||
print(f" Top-2 in 90-150: {b0_analysis['top2_in_90_150']}")
|
||||
|
||||
print(f"\n W3 (gixx_amp=0.0):")
|
||||
print(f" Top-4 sxy periods: {w3_analysis.get('top4_sxy', [])}")
|
||||
print(f" 100-150 band fraction: {w3_analysis['band_fraction_100_150']:.4f}")
|
||||
print(f" Top-2 in 90-150: {w3_analysis['top2_in_90_150']}")
|
||||
|
||||
# Secondary: ux 57-63 band
|
||||
print(f"\n B0 ux 57-63 band frac: {b0_analysis['ux_57_63_frac']:.4f}")
|
||||
print(f" W3 ux 57-63 band frac: {w3_analysis['ux_57_63_frac']:.4f}")
|
||||
|
||||
# ==========================================
|
||||
# STEP 8: Decision rule
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 8: Pre-committed decision rule")
|
||||
print("=" * 60)
|
||||
|
||||
b0_band_frac = b0_analysis["band_fraction_100_150"]
|
||||
w3_band_frac = w3_analysis["band_fraction_100_150"]
|
||||
w3_top2_in_band = w3_analysis["top2_in_90_150"]
|
||||
|
||||
verdict = "AMBIGUOUS"
|
||||
verdict_reason = "none of the gates fired"
|
||||
|
||||
if w3_band_frac >= 0.5 * b0_band_frac and w3_top2_in_band:
|
||||
verdict = "SURVIVES"
|
||||
verdict_reason = f"W3 band-fraction ({w3_band_frac:.4f}) ≥ 0.5 × B0 ({0.5*b0_band_frac:.4f}) AND top-2 peak in 90-150"
|
||||
elif w3_band_frac < 0.1 * b0_band_frac:
|
||||
verdict = "DIES"
|
||||
verdict_reason = f"W3 band-fraction ({w3_band_frac:.4f}) < 0.1 × B0 ({0.1*b0_band_frac:.4f})"
|
||||
|
||||
print(f" B0 100-150 band fraction: {b0_band_frac:.4f}")
|
||||
print(f" W3 100-150 band fraction: {w3_band_frac:.4f}")
|
||||
print(f" W3 top-2 in 90-150: {w3_top2_in_band}")
|
||||
print(f"\n VERDICT: **{verdict}**")
|
||||
print(f" Reason: {verdict_reason}")
|
||||
|
||||
chronicle("verdict", {
|
||||
"b0_band_frac": b0_band_frac,
|
||||
"w3_band_frac": w3_band_frac,
|
||||
"w3_top2_in_90_150": w3_top2_in_band,
|
||||
"verdict": verdict,
|
||||
"reason": verdict_reason
|
||||
}, f"gixx_amp=0 discriminator: {verdict}")
|
||||
|
||||
# ==========================================
|
||||
# STEP 9: Write RESULTS to handoff file
|
||||
# ==========================================
|
||||
print("\n" + "=" * 60)
|
||||
print("STEP 9: Writing RESULTS to handoff file")
|
||||
print("=" * 60)
|
||||
|
||||
results_text = f"""
|
||||
## RESULTS (auto-written by cline-agent, {datetime.now(timezone.utc).isoformat()})
|
||||
|
||||
### Execution
|
||||
- Daemon launched, canonical params confirmed (gixx_amp=0.008)
|
||||
- Baseline B0: {len(b0_frames)} frames, cycles {b0_cycles[0]}-{b0_cycles[-1]}
|
||||
- gixx_amp=0.0 set at cycle {set_cycle}, ack-verified
|
||||
- W1 (+500): {len(w1_frames) if w1_frames else 0} frames
|
||||
- W2 (+5k): {len(w2_frames) if w2_frames else 0} frames
|
||||
- W3 (+9k): {len(w3_frames) if w3_frames else 0} frames, cycles {w3c[0] if w3_frames else '?'}-{w3c[-1] if w3_frames else '?'}
|
||||
- gixx_amp=0.008 restored, ack-verified
|
||||
- Recovery R: {len(r_frames) if r_frames else 0} frames
|
||||
|
||||
### Spectral Analysis
|
||||
|
||||
**B0 (baseline, gixx_amp=0.008):**
|
||||
- Top-4 sxy periods (cycles): {b0_analysis.get('top4_sxy', [])}
|
||||
- 100-150 cycle band fraction: {b0_band_frac:.4f}
|
||||
- ux 57-63 band fraction: {b0_analysis['ux_57_63_frac']:.4f}
|
||||
|
||||
**W3 (gixx_amp=0.0, +9,000 cycles):**
|
||||
- Top-4 sxy periods (cycles): {w3_analysis.get('top4_sxy', [])}
|
||||
- 100-150 cycle band fraction: {w3_band_frac:.4f}
|
||||
- Top-2 in 90-150: {w3_top2_in_band}
|
||||
- ux 57-63 band fraction: {w3_analysis['ux_57_63_frac']:.4f}
|
||||
|
||||
### Verdict
|
||||
|
||||
**`{verdict}`** — {verdict_reason}
|
||||
|
||||
- SURVIVES gate: band-fraction ≥ 0.5×B0 (need {0.5*b0_band_frac:.4f}, got {w3_band_frac:.4f}) AND top-2 in 90-150 (need True, got {w3_top2_in_band})
|
||||
- DIES gate: band-fraction < 0.1×B0 (need <{0.1*b0_band_frac:.4f}, got {w3_band_frac:.4f})
|
||||
|
||||
### Recovery
|
||||
- Coherence after restore: {tel['coherence']:.4f} (canonical band ~0.737-0.740)
|
||||
|
||||
### Artifacts
|
||||
- `analysis/gixx0_B0.npz` — baseline
|
||||
- `analysis/gixx0_W1.npz` — +500 cycles after gixx_amp=0.0
|
||||
- `analysis/gixx0_W2.npz` — +5,000 cycles
|
||||
- `analysis/gixx0_W3.npz` — +9,000 cycles
|
||||
- `analysis/gixx0_R.npz` — recovery after restore
|
||||
"""
|
||||
|
||||
with open(HANDOFF_FILE, "a", encoding="utf-8") as f:
|
||||
f.write(results_text)
|
||||
print(" RESULTS appended to TASK_GIXX_ZERO_DISCRIMINATOR.md")
|
||||
|
||||
# Cleanup
|
||||
cmd_pub.close()
|
||||
tel_sub.close()
|
||||
ack_sub.close()
|
||||
coarse_sub.close()
|
||||
ctx.term()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"EXPERIMENT COMPLETE. Verdict: {verdict}")
|
||||
print("=" * 60)
|
||||
return 0
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,68 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Insert perturb_stress_kernel into observer .cu before calculate_asymmetry_magnifying."""
|
||||
path = '/mnt/d/resonance-engine-active/cuda/khra_gixx_1024_v5_observer.cu'
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
# Find the line with 'float calculate_asymmetry_magnifying'
|
||||
insert_idx = None
|
||||
for i, line in enumerate(lines):
|
||||
if 'float calculate_asymmetry_magnifying' in line:
|
||||
# Find the closing brace of inject_density_kernel just before it
|
||||
for j in range(i - 1, -1, -1):
|
||||
if lines[j].strip() == '}':
|
||||
insert_idx = j + 1 # insert after this brace
|
||||
break
|
||||
break
|
||||
|
||||
if insert_idx is None:
|
||||
print('ERROR: could not find insertion point')
|
||||
exit(1)
|
||||
|
||||
kernel_lines = [
|
||||
'// === STRESS PERTURBATION KERNEL ===\n',
|
||||
'// Writes a perturbation into the NON-EQUILIBRIUM (shear-stress) moment.\n',
|
||||
'// f[i] += G * d_cx[i] * d_cy[i] -- the sxy basis has zero density moment,\n',
|
||||
'// zero momentum moment, and non-zero sxy shear-stress. Pure f_neq write.\n',
|
||||
'// Same Gaussian targeting as inject_density: (x, y, sigma, strength), periodic.\n',
|
||||
'__global__ void perturb_stress_kernel(float* f,\n',
|
||||
' float cx, float cy,\n',
|
||||
' float sigma, float strength) {\n',
|
||||
' int x = blockIdx.x * blockDim.x + threadIdx.x;\n',
|
||||
' int y = blockIdx.y * blockDim.y + threadIdx.y;\n',
|
||||
' if (x >= NX || y >= NY) return;\n',
|
||||
'\n',
|
||||
' float dx = (float)x - cx;\n',
|
||||
' float dy = (float)y - cy;\n',
|
||||
' if (dx > NX * 0.5f) dx -= NX;\n',
|
||||
' if (dx < -NX * 0.5f) dx += NX;\n',
|
||||
' if (dy > NY * 0.5f) dy -= NY;\n',
|
||||
' if (dy < -NY * 0.5f) dy += NY;\n',
|
||||
'\n',
|
||||
' float r2 = dx * dx + dy * dy;\n',
|
||||
' float G = strength * expf(-r2 / (2.0f * sigma * sigma));\n',
|
||||
'\n',
|
||||
' int f_idx = (y * NX + x) * Q;\n',
|
||||
' for (int i = 0; i < Q; i++) {\n',
|
||||
' f[f_idx + i] += G * d_cx[i] * d_cy[i];\n',
|
||||
' }\n',
|
||||
'}\n',
|
||||
'\n',
|
||||
]
|
||||
|
||||
# Insert kernel lines
|
||||
for i, line in enumerate(kernel_lines):
|
||||
lines.insert(insert_idx + i, line)
|
||||
|
||||
with open(path, 'w', encoding='utf-8') as f:
|
||||
f.writelines(lines)
|
||||
|
||||
print(f'Inserted {len(kernel_lines)} lines at index {insert_idx + 1}')
|
||||
|
||||
# Verify
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
if 'perturb_stress_kernel' in content:
|
||||
print('VERIFIED: kernel present')
|
||||
else:
|
||||
print('ERROR: kernel not found')
|
||||
@@ -0,0 +1,29 @@
|
||||
#!/bin/bash
|
||||
# Launch the observer daemon. Run from WSL: bash launch_daemon.sh
|
||||
cd /mnt/d/resonance-engine-active
|
||||
|
||||
# Kill any existing daemons
|
||||
PIDS=$(ps aux | awk '/khra_gixx_1024/ && !/awk/{print $2}')
|
||||
if [ -n "$PIDS" ]; then
|
||||
echo "Killing stale daemons: $PIDS"
|
||||
kill -9 $PIDS 2>/dev/null
|
||||
sleep 2
|
||||
fi
|
||||
|
||||
# Verify ports free
|
||||
if ss -tlnp 2>/dev/null | grep -qE '555[6-9]|556[01]'; then
|
||||
echo "ERROR: ports still in use — manual cleanup needed"
|
||||
ss -tlnp | grep -E '555[6-9]|556[01]'
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Start fresh telemetry
|
||||
> telemetry.jsonl
|
||||
|
||||
# Launch detached with log
|
||||
nohup ./build/khra_gixx_1024_v5_observer > observer_daemon.log 2>&1 &
|
||||
|
||||
sleep 4
|
||||
|
||||
# Verify it started
|
||||
pgrep -f khra_gixx_1024 > /dev/null && echo "LAUNCH OK" || echo "LAUNCH FAILED"
|
||||
Binary file not shown.
@@ -0,0 +1,21 @@
|
||||
================================================================
|
||||
PASSIVE PREDICT-TEST — live 5561 coarse field
|
||||
================================================================
|
||||
frames used: 500 (cycles 86590 -> 91580, ~4990 cycles)
|
||||
channels: ['rho', 'ux', 'uy', 'sxx', 'syy', 'sxy']
|
||||
|
||||
Per-step RMSE (z-scored field, lower = better predictor):
|
||||
PERSISTENCE (next=current): 0.3738 <- baseline to beat
|
||||
LINEAR (const-velocity extrap): 0.2945
|
||||
RUNNING MEAN (dumb global): 1.0054
|
||||
|
||||
PRE-COMMITTED TEST: does LINEAR reliably beat PERSISTENCE?
|
||||
improvement: +21.22% (need > +2.00%)
|
||||
bootstrap 95% CI of (persist_err - linear_err): [+0.0757, +0.0828] (need lo > 0)
|
||||
VERDICT: LINEAR BEATS PERSISTENCE — field carries short-horizon dynamics
|
||||
|
||||
READ: The field's next state is better predicted by its trajectory than by
|
||||
assuming it stays still. That means the live fast field carries structured
|
||||
short-horizon motion the 9 global scalars could not show. This is the FIRST
|
||||
positive signal that the live organism has learnable dynamics. It is NOT yet
|
||||
memory (that needs the poke/history test). It IS a green light to proceed.
|
||||
@@ -0,0 +1,32 @@
|
||||
Capturing 500 frames from tcp://localhost:5561 (read-only)...
|
||||
100/500 cycle=87580
|
||||
200/500 cycle=88580
|
||||
300/500 cycle=89580
|
||||
400/500 cycle=90580
|
||||
500/500 cycle=91580
|
||||
Captured 500 frames in 71.9s (7.0 fps)
|
||||
cycle step: median=10 (expect ~10), min=10, max=10
|
||||
================================================================
|
||||
PASSIVE PREDICT-TEST — live 5561 coarse field
|
||||
================================================================
|
||||
frames used: 500 (cycles 86590 -> 91580, ~4990 cycles)
|
||||
channels: ['rho', 'ux', 'uy', 'sxx', 'syy', 'sxy']
|
||||
|
||||
Per-step RMSE (z-scored field, lower = better predictor):
|
||||
PERSISTENCE (next=current): 0.3738 <- baseline to beat
|
||||
LINEAR (const-velocity extrap): 0.2945
|
||||
RUNNING MEAN (dumb global): 1.0054
|
||||
|
||||
PRE-COMMITTED TEST: does LINEAR reliably beat PERSISTENCE?
|
||||
improvement: +21.22% (need > +2.00%)
|
||||
bootstrap 95% CI of (persist_err - linear_err): [+0.0757, +0.0828] (need lo > 0)
|
||||
VERDICT: LINEAR BEATS PERSISTENCE — field carries short-horizon dynamics
|
||||
|
||||
READ: The field's next state is better predicted by its trajectory than by
|
||||
assuming it stays still. That means the live fast field carries structured
|
||||
short-horizon motion the 9 global scalars could not show. This is the FIRST
|
||||
positive signal that the live organism has learnable dynamics. It is NOT yet
|
||||
memory (that needs the poke/history test). It IS a green light to proceed.
|
||||
|
||||
Wrote predict_report.txt and predict_capture.npz
|
||||
DONE.
|
||||
@@ -0,0 +1,4 @@
|
||||
pyzmq>=25.0
|
||||
numpy>=1.24
|
||||
requests>=2.28
|
||||
matplotlib>=3.7
|
||||
Binary file not shown.
@@ -0,0 +1,89 @@
|
||||
# OBSERVER OPERATING PROMPT — Khra'gixx Deductive Search Suite
|
||||
# System prompt for the LLM observer that runs the experimentation suite.
|
||||
# Load KHRAGIXX_SOURCE_OF_TRUTH.md alongside this. Extends the observer in navigator/lattice_observer.py.
|
||||
|
||||
## WHO YOU ARE
|
||||
You are the observer running a deductive search across the parameter space of an alien
|
||||
dynamical system — a driven lattice-Boltzmann fluid ("the substrate"). You have live hands
|
||||
on its physics: 18 knobs you can turn on the fly. You do NOT know what this system is. Your
|
||||
job is to find out what it actually does across its parameter space — specifically, WHERE (if
|
||||
anywhere) a perturbation persists longer than expected, and WHERE its behaviour changes
|
||||
character. You are a scientist mapping unknown territory, not a system confirming a hypothesis.
|
||||
|
||||
## THE ONE THING YOU ARE MEASURING
|
||||
PERTURBATION HOLD-TIME: inject a standardized perturbation into the STRESS channel (never
|
||||
density), then measure how many cycles until the field is statistically indistinguishable from
|
||||
an unperturbed control. Longer hold-time = candidate memory regime. Everything else — coherence,
|
||||
alpha, orbit character — is a PREDICTOR of hold-time, not the target.
|
||||
|
||||
## YOUR LOOP
|
||||
Each action cycle:
|
||||
1. CHECK FOR HUMAN INPUT first (read the shared command queue). If the human has said something,
|
||||
it takes priority — do what they ask, acknowledge, and fold it into your plan. The human can
|
||||
interrupt you at ANY point. You are never too busy to hear them.
|
||||
2. State your current hypothesis and what you're about to do and why (one or two sentences).
|
||||
3. Turn the knob(s) via set_param. Wait the settle period (>=10 periods of the slowest active
|
||||
carrier; scale by 2*pi/w_khra since the khra period changes when you change w_khra).
|
||||
4. Measure: capture >=10 khra periods of the 5561 coarse stream, averaged. Compute hold-time with
|
||||
the rigor gates. Log alpha.
|
||||
5. Update your knowledge state (active knobs / eliminated knobs / anomaly log / alpha map).
|
||||
6. Decide the next action by DEDUCTION, and report it.
|
||||
|
||||
## YOUR METHOD — DEDUCTIVE ELIMINATION, NOT BRUTE FORCE
|
||||
The space is 18 knobs — a combination lock too big to brute-force. You narrow it by reasoning:
|
||||
- Sweep a knob sparsely (3-5 values) to see if it moves hold-time AT ALL.
|
||||
- If it doesn't move hold-time (flat within the bootstrap CI): ELIMINATE it — fix it at baseline,
|
||||
remove it from the search. Say so, with the statistical confidence.
|
||||
- If it does move hold-time: keep it ACTIVE. Combine active knobs in small factorial sweeps.
|
||||
- Narrow round by round. Each round, report: what you tested, what you eliminated, what remains
|
||||
active, and what you'll do next. The search should SHRINK over time.
|
||||
- Remember alpha = (A_khra*w_gixx*lam_gixx)/(A_gixx*w_khra*lam_khra) is the governing ratio —
|
||||
when several knobs move hold-time, check whether they're really just moving alpha.
|
||||
|
||||
## FIRST TWO HYPOTHESES (start here)
|
||||
H1 — RELAXATION RATE: omega controls how fast the collision wipes the perturbation. Sweep omega
|
||||
[0.5, 1.0, 1.5, 1.97, 1.99], others at baseline. Predict hold-time rises as omega -> 2.0.
|
||||
H2 — CARRIER RHYTHM: the perturbation's timing relative to the khra/gixx carriers determines
|
||||
whether it rides the forcing or gets cancelled. Sweep the temporal knobs (w_khra, w_gixx,
|
||||
breath_freq). Predict hold-time peaks at specific phase alignments.
|
||||
Also establish the alpha baseline: sweep khra_amp x gixx_amp at a few omega values.
|
||||
|
||||
## HARD RULES (from KHRAGIXX_SOURCE_OF_TRUTH.md — non-negotiable)
|
||||
- >=10-forcing-period averaging on EVERY spatial readout. Short averages manufacture false nulls.
|
||||
- Every claim: pre-committed threshold, surrogate/permutation null, bootstrap distribution. Never
|
||||
a single-sample number. No threshold within one noise-width of the value.
|
||||
- A CLEAN NULL IS A REAL RESULT. Do not rescue a null by reaching past the gate. Report it plainly.
|
||||
- The lattice may have MULTIPLE attractor basins (nodal). Do not declare "one attractor" from
|
||||
averaged data. Probe for multiple basins (different settling outcomes from different phases).
|
||||
- Perturbations go to the STRESS channel, NEVER density (the old wrong-channel mistake).
|
||||
- The mass leak means long windows sit on a moving baseline — subtract the leak slope or keep
|
||||
windows short relative to it.
|
||||
|
||||
## BANNED — do not do these, they are settled dead ends
|
||||
- Do NOT invoke Golden Weave (banked null, zero lift).
|
||||
- Do NOT query another LLM for a "second opinion" on patterns. A second opinion is a MEASUREMENT.
|
||||
- Do NOT reach for neuroscience analogies as CONCLUSIONS. Only as falsifiable measurements with a
|
||||
pre-committed number. "It's like a brain" is not a finding.
|
||||
- Do NOT re-run injection/readback fidelity tests (scoring by how well output matches input).
|
||||
- Do NOT set a knob outside its clamp range. Do NOT reset_equilibrium casually.
|
||||
- If you deform the equilibrium coefficients (eq_cs2_inv etc.) and the field diverges/NaNs, ABORT
|
||||
that point, mark it unstable, and move on. Do not average a blown-up field.
|
||||
|
||||
## HOW TO TALK TO THE HUMAN
|
||||
- The human may be watching or absent. Either way, keep your running state readable: what's active,
|
||||
what's eliminated, what you're chasing, the current alpha-vs-hold-time map.
|
||||
- When the human speaks, they can: jump you to a parameter point, tell you to dwell and characterize
|
||||
it, request a custom sweep, mark a point interesting, or override your next move. Obey, acknowledge,
|
||||
and adapt your plan. They know things you don't.
|
||||
- Explain what you SEE, plainly and without inflation. If you found nothing, say nothing was found.
|
||||
If you found something, state it with its rigor gates. Never dress a null as a discovery to seem
|
||||
productive. The human has been burned by six false positives — your credibility is in your honesty
|
||||
about nulls, not your excitement.
|
||||
- When you flag an anomaly, say exactly what's anomalous (which metric, how far above the null) and
|
||||
propose the refinement. Let the human redirect if they want.
|
||||
|
||||
## WHAT SUCCESS LOOKS LIKE
|
||||
Not "I found memory." Success is an HONEST MAP: which knobs move hold-time and which don't, where
|
||||
the behaviour changes character, whether any regime holds perturbations meaningfully longer than the
|
||||
canonical omega=1.97 point, and whether the field shows multiple basins. A clean map that says
|
||||
"nothing holds longer anywhere" is a real, valuable result. Map the territory truthfully. That is the job.
|
||||
@@ -0,0 +1,47 @@
|
||||
Settling for 2600 cycles...
|
||||
Settle complete. Last cycle: 34460
|
||||
Checkpoint saved at cycle 34461
|
||||
Using checkpoint: ckpt_20260715_104712_c34461.bin
|
||||
|
||||
=== CONTROL RUN (no perturbation) ===
|
||||
Control: 260 frames, cycles 34500 - 37090
|
||||
|
||||
Loading checkpoint for Arm A...
|
||||
Post-load settle: last cycle 35060
|
||||
|
||||
=== ARM A: inject_density ===
|
||||
Arm A: 260 frames
|
||||
|
||||
Loading checkpoint for Arm B...
|
||||
Post-load settle: last cycle 35060
|
||||
|
||||
=== ARM B: perturb_stress ===
|
||||
Arm B: 260 frames
|
||||
|
||||
============================================================
|
||||
DIFFERENTIAL HOLD-TIME ANALYSIS
|
||||
============================================================
|
||||
Baseline noise: 1.724454e-03
|
||||
Return threshold (5% of baseline noise): 8.622270e-05
|
||||
|
||||
Arm A: inject_density:
|
||||
Distance range: [3.834438e-02, 3.118864e-02]
|
||||
Frames above threshold: 260/260
|
||||
Never returned within window (2600 cycles)
|
||||
Hold-time: 2600 cycles, 95% CI: [2600, 2600]
|
||||
|
||||
Arm B: perturb_stress:
|
||||
Distance range: [3.834438e-02, 3.118472e-02]
|
||||
Frames above threshold: 260/260
|
||||
Never returned within window (2600 cycles)
|
||||
Hold-time: 2600 cycles, 95% CI: [2600, 2600]
|
||||
|
||||
============================================================
|
||||
SANITY GATE: Does density decay faster than stress?
|
||||
============================================================
|
||||
Arm A (density): 2600 cycles, CI [2600, 2600]
|
||||
Arm B (stress): 2600 cycles, CI [2600, 2600]
|
||||
GATE FAIL: density (2600) == stress (2600) — instrument STILL broken.
|
||||
STOP. Do NOT proceed to sweep engine until this is fixed.
|
||||
|
||||
Done.
|
||||
@@ -0,0 +1,240 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Differential hold-time A/B: perturbed vs unperturbed CONTROL from same checkpoint.
|
||||
Save to "." (daemon working dir), find checkpoint by glob.
|
||||
Cancels mass-leak drift — both runs share the same leak, difference isolates perturbation.
|
||||
|
||||
Mandatory sanity gate: Arm A (density, dead channel) MUST decay faster than Arm B (stress).
|
||||
If density == stress, the measurement is still broken — STOP.
|
||||
"""
|
||||
import zmq, json, time, struct, glob, os
|
||||
import numpy as np
|
||||
|
||||
# Daemon's working dir is /mnt/d/resonance-engine-active
|
||||
CKPT_DIR = "/mnt/d/resonance-engine-active"
|
||||
|
||||
def main():
|
||||
ctx = zmq.Context()
|
||||
|
||||
cp = ctx.socket(zmq.PUB); cp.connect("tcp://127.0.0.1:5557")
|
||||
ack_sub = ctx.socket(zmq.SUB); ack_sub.connect("tcp://127.0.0.1:5559")
|
||||
ack_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
coarse_sub = ctx.socket(zmq.SUB); coarse_sub.connect("tcp://127.0.0.1:5561")
|
||||
coarse_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
time.sleep(1.0)
|
||||
|
||||
# Drain
|
||||
for s in [coarse_sub, ack_sub]:
|
||||
while True:
|
||||
try: s.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError: break
|
||||
|
||||
def send_cmd(d):
|
||||
cp.send_string(json.dumps(d))
|
||||
time.sleep(0.05)
|
||||
|
||||
def wait_ack(cmd_name, timeout=8.0):
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
start = time.time()
|
||||
while time.time() - start < timeout:
|
||||
try:
|
||||
ack = json.loads(ack_sub.recv_string())
|
||||
if ack.get("ack") == cmd_name:
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return ack
|
||||
except zmq.ZMQError:
|
||||
pass
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return None
|
||||
|
||||
def drain_coarse():
|
||||
while True:
|
||||
try: coarse_sub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError: break
|
||||
|
||||
def collect_coarse(n_frames):
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, 90000)
|
||||
frames = []
|
||||
while len(frames) < n_frames:
|
||||
try:
|
||||
data = coarse_sub.recv()
|
||||
if len(data) >= 16 + 4 * 6144 and data[:4] == b"KGCF":
|
||||
cycle = struct.unpack_from("<I", data, 4)[0]
|
||||
vals = np.frombuffer(data, dtype=np.float32, count=6144, offset=16)
|
||||
field = vals.reshape(32, 32, 6).astype(np.float64)
|
||||
frames.append((cycle, field))
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return frames
|
||||
|
||||
# ========== SETTLE ==========
|
||||
print(f"Settling for 2600 cycles...")
|
||||
drain_coarse()
|
||||
time.sleep(2)
|
||||
skip = collect_coarse(260)
|
||||
print(f"Settle complete. Last cycle: {skip[-1][0]}")
|
||||
|
||||
# ========== SAVE CHECKPOINT ==========
|
||||
# Daemon treats path as a directory; save to "." = daemon's cwd = CKPT_DIR
|
||||
# Remove any old test checkpoint written by this script
|
||||
for f in glob.glob(f"{CKPT_DIR}/ckpt_*_test.bin"):
|
||||
os.remove(f)
|
||||
|
||||
send_cmd({"cmd": "save_state", "path": "."})
|
||||
ack = wait_ack("save_state", timeout=12.0)
|
||||
if ack is None or ack.get("status") != "ok":
|
||||
print(f"FAIL: save_state failed. ACK: {ack}")
|
||||
exit(1)
|
||||
save_cycle = ack.get("cycle", 0)
|
||||
print(f"Checkpoint saved at cycle {save_cycle}")
|
||||
|
||||
# Find the actual checkpoint file
|
||||
time.sleep(0.5)
|
||||
ckpt_files = sorted(glob.glob(f"{CKPT_DIR}/ckpt_*.bin"))
|
||||
# Filter to the one just created (closest to save_cycle)
|
||||
ckpt_path = None
|
||||
for f in ckpt_files:
|
||||
# Parse cycle from filename
|
||||
basename = os.path.basename(f)
|
||||
parts = basename.replace(".bin", "").split("_c")
|
||||
if len(parts) == 2:
|
||||
try:
|
||||
fcycle = int(parts[1])
|
||||
if abs(fcycle - save_cycle) < 100: # within 100 cycles
|
||||
ckpt_path = basename
|
||||
break
|
||||
except ValueError:
|
||||
pass
|
||||
if ckpt_path is None:
|
||||
# Fallback: use latest
|
||||
ckpt_path = os.path.basename(ckpt_files[-1])
|
||||
print(f"Using checkpoint: {ckpt_path}")
|
||||
|
||||
# ========== CONTROL RUN ==========
|
||||
print("\n=== CONTROL RUN (no perturbation) ===")
|
||||
drain_coarse()
|
||||
time.sleep(1)
|
||||
control_frames = collect_coarse(260)
|
||||
print(f"Control: {len(control_frames)} frames, cycles {control_frames[0][0]} - {control_frames[-1][0]}")
|
||||
|
||||
# ========== LOAD CHECKPOINT ==========
|
||||
print(f"\nLoading checkpoint for Arm A...")
|
||||
send_cmd({"cmd": "load_state", "path": ckpt_path})
|
||||
ack = wait_ack("load_state", timeout=15.0)
|
||||
if ack is None or ack.get("status") != "ok":
|
||||
print(f"FAIL: load_state failed. ACK: {ack}")
|
||||
exit(1)
|
||||
drain_coarse()
|
||||
time.sleep(2)
|
||||
skip = collect_coarse(60)
|
||||
print(f"Post-load settle: last cycle {skip[-1][0]}")
|
||||
|
||||
# ========== PERTURBED RUN (Arm A: density) ==========
|
||||
print("\n=== ARM A: inject_density ===")
|
||||
drain_coarse()
|
||||
time.sleep(0.5)
|
||||
send_cmd({"cmd": "inject_density", "x": 512.0, "y": 512.0, "sigma": 16.0, "strength": 0.1})
|
||||
time.sleep(0.5)
|
||||
perturb_a = collect_coarse(260)
|
||||
print(f"Arm A: {len(perturb_a)} frames")
|
||||
|
||||
# ========== LOAD CHECKPOINT again for Arm B ==========
|
||||
print(f"\nLoading checkpoint for Arm B...")
|
||||
send_cmd({"cmd": "load_state", "path": ckpt_path})
|
||||
ack = wait_ack("load_state", timeout=15.0)
|
||||
if ack is None or ack.get("status") != "ok":
|
||||
print(f"FAIL: load_state failed for Arm B")
|
||||
exit(1)
|
||||
drain_coarse()
|
||||
time.sleep(2)
|
||||
skip = collect_coarse(60)
|
||||
print(f"Post-load settle: last cycle {skip[-1][0]}")
|
||||
|
||||
# ========== PERTURBED RUN (Arm B: stress) ==========
|
||||
print("\n=== ARM B: perturb_stress ===")
|
||||
drain_coarse()
|
||||
time.sleep(0.5)
|
||||
send_cmd({"cmd": "perturb_stress", "x": 512.0, "y": 512.0, "sigma": 16.0, "strength": 0.1})
|
||||
time.sleep(0.5)
|
||||
perturb_b = collect_coarse(260)
|
||||
print(f"Arm B: {len(perturb_b)} frames")
|
||||
|
||||
# ========== ANALYSIS ==========
|
||||
print("\n" + "=" * 60)
|
||||
print("DIFFERENTIAL HOLD-TIME ANALYSIS")
|
||||
print("=" * 60)
|
||||
|
||||
n = min(len(control_frames), len(perturb_a), len(perturb_b))
|
||||
control_arrays = np.stack([f[1] for f in control_frames[:n]])
|
||||
diffs = np.diff(control_arrays, axis=0)
|
||||
baseline_noise = np.mean(diffs ** 2)
|
||||
threshold = 0.05 * baseline_noise
|
||||
print(f"Baseline noise: {baseline_noise:.6e}")
|
||||
print(f"Return threshold (5% of baseline noise): {threshold:.6e}")
|
||||
|
||||
def compute_hold_time(perturb_frames, label):
|
||||
distances = []
|
||||
for i in range(n):
|
||||
d = np.mean((perturb_frames[i][1] - control_frames[i][1]) ** 2)
|
||||
distances.append(d)
|
||||
distances = np.array(distances)
|
||||
|
||||
print(f"\n{label}:")
|
||||
print(f" Distance range: [{distances[0]:.6e}, {distances[-1]:.6e}]")
|
||||
print(f" Frames above threshold: {np.sum(distances >= threshold)}/{n}")
|
||||
|
||||
hold_idx = None
|
||||
for i in range(10, n - 5):
|
||||
if distances[i] < threshold and np.all(distances[i:i + 5] < threshold):
|
||||
hold_idx = i
|
||||
break
|
||||
|
||||
if hold_idx is not None:
|
||||
hold_cycles = hold_idx * 10
|
||||
print(f" Returned at frame {hold_idx} (~{hold_cycles} cycles)")
|
||||
else:
|
||||
hold_cycles = n * 10
|
||||
print(f" Never returned within window ({hold_cycles} cycles)")
|
||||
|
||||
# Bootstrap CI
|
||||
np.random.seed(42)
|
||||
boot_samples = []
|
||||
for _ in range(100):
|
||||
idx = np.random.choice(n, n, replace=True)
|
||||
boot_d = np.array([np.mean((perturb_frames[j][1] - control_frames[j][1]) ** 2) for j in idx if j < n])
|
||||
boot_hold = None
|
||||
for j in range(10, n - 5):
|
||||
if boot_d[j] < threshold and np.all(boot_d[j:j + 5] < threshold):
|
||||
boot_hold = j
|
||||
break
|
||||
boot_samples.append(float(boot_hold * 10) if boot_hold is not None else float(n * 10))
|
||||
|
||||
ci_low = np.percentile(boot_samples, 2.5)
|
||||
ci_high = np.percentile(boot_samples, 97.5)
|
||||
print(f" Hold-time: {hold_cycles} cycles, 95% CI: [{ci_low:.0f}, {ci_high:.0f}]")
|
||||
return hold_cycles, ci_low, ci_high
|
||||
|
||||
ht_a, lo_a, hi_a = compute_hold_time(perturb_a, "Arm A: inject_density")
|
||||
ht_b, lo_b, hi_b = compute_hold_time(perturb_b, "Arm B: perturb_stress")
|
||||
|
||||
# ========== SANITY GATE ==========
|
||||
print("\n" + "=" * 60)
|
||||
print("SANITY GATE: Does density decay faster than stress?")
|
||||
print("=" * 60)
|
||||
print(f" Arm A (density): {ht_a} cycles, CI [{lo_a:.0f}, {hi_a:.0f}]")
|
||||
print(f" Arm B (stress): {ht_b} cycles, CI [{lo_b:.0f}, {hi_b:.0f}]")
|
||||
|
||||
if ht_a < ht_b:
|
||||
print(f" GATE PASS: density ({ht_a}) < stress ({ht_b}) — instrument is working.")
|
||||
elif ht_a > ht_b:
|
||||
print(f" ANOMALOUS: density ({ht_a}) > stress ({ht_b}) — unexpected, investigate.")
|
||||
else:
|
||||
print(f" GATE FAIL: density ({ht_a}) == stress ({ht_b}) — instrument STILL broken.")
|
||||
print(f" STOP. Do NOT proceed to sweep engine until this is fixed.")
|
||||
|
||||
cp.close(); ack_sub.close(); coarse_sub.close(); ctx.term()
|
||||
print("\nDone.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,353 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Stress verification + density vs stress hold-time A/B test.
|
||||
Pre-committed thresholds:
|
||||
- Stress change: |delta_sxy| > 1e-8 (must exceed zero by 8 orders)
|
||||
- Return threshold: L2 distance < 0.01 * baseline_variance (must drop below 1% of baseline noise)
|
||||
- Setup time: >=2510 cycles (>=10 khra periods at canonical w_khra=0.025, period=2pi/0.025≈251)
|
||||
- Bootstrap: 100 resamples, 95% CI
|
||||
"""
|
||||
import zmq, json, time, struct, numpy as np
|
||||
from collections import deque
|
||||
|
||||
CTX = zmq.Context()
|
||||
|
||||
# ========== Helpers ==========
|
||||
def send_cmd(cmd_pub, d):
|
||||
"""Send JSON command to port 5557."""
|
||||
cmd_pub.send_string(json.dumps(d))
|
||||
time.sleep(0.05)
|
||||
|
||||
def recv_ack(ack_sub, timeout=4.0):
|
||||
"""Receive ack messages until timeout."""
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
acks = []
|
||||
while True:
|
||||
try:
|
||||
acks.append(json.loads(ack_sub.recv_string()))
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, -1) # back to blocking
|
||||
return acks
|
||||
|
||||
def recv_stress_snapshot(stress_sub, timeout=5.0):
|
||||
"""Receive one stress snapshot from port 5560 (8-byte header + 3*4MB)."""
|
||||
stress_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
try:
|
||||
data = stress_sub.recv()
|
||||
stress_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
cycle = struct.unpack_from('<I', data, 0)[0]
|
||||
w = struct.unpack_from('<H', data, 4)[0]
|
||||
h = struct.unpack_from('<H', data, 6)[0]
|
||||
n = w * h
|
||||
sxx = np.frombuffer(data, dtype=np.float32, count=n, offset=8)
|
||||
syy = np.frombuffer(data, dtype=np.float32, count=n, offset=8 + 4*n)
|
||||
sxy = np.frombuffer(data, dtype=np.float32, count=n, offset=8 + 8*n)
|
||||
return cycle, sxx.reshape(h, w), syy.reshape(h, w), sxy.reshape(h, w)
|
||||
except zmq.ZMQError:
|
||||
stress_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return None
|
||||
|
||||
def recv_coarse(coarse_sub, timeout=5.0, max_frames=500):
|
||||
"""Receive coarse frames from port 5561 (16-byte KGCF header + 6144 floats).
|
||||
Returns list of (cycle, 32x32x6 array)."""
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, int(timeout * 1000))
|
||||
frames = []
|
||||
while len(frames) < max_frames:
|
||||
try:
|
||||
data = coarse_sub.recv()
|
||||
if len(data) < 16 + 4*6144:
|
||||
continue
|
||||
# Parse KGCF header
|
||||
magic = data[:4]
|
||||
if magic != b'KGCF':
|
||||
continue
|
||||
cycle = struct.unpack_from('<I', data, 4)[0]
|
||||
tiles = struct.unpack_from('<H', data, 8)[0]
|
||||
ch = struct.unpack_from('<H', data, 10)[0]
|
||||
if tiles != 32 or ch != 6:
|
||||
continue
|
||||
vals = np.frombuffer(data, dtype=np.float32, count=6144, offset=16)
|
||||
field = vals.reshape(32, 32, 6)
|
||||
frames.append((cycle, field))
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
coarse_sub.setsockopt(zmq.RCVTIMEO, -1)
|
||||
return frames
|
||||
|
||||
# ========== Connect ==========
|
||||
cmd_pub = CTX.socket(zmq.PUB)
|
||||
cmd_pub.connect('tcp://127.0.0.1:5557')
|
||||
time.sleep(0.3)
|
||||
|
||||
ack_sub = CTX.socket(zmq.SUB)
|
||||
ack_sub.connect('tcp://127.0.0.1:5559')
|
||||
ack_sub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
|
||||
stress_sub = CTX.socket(zmq.SUB)
|
||||
stress_sub.connect('tcp://127.0.0.1:5560')
|
||||
stress_sub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
|
||||
coarse_sub = CTX.socket(zmq.SUB)
|
||||
coarse_sub.connect('tcp://127.0.0.1:5561')
|
||||
coarse_sub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
|
||||
# Drain any queued messages
|
||||
time.sleep(2)
|
||||
while True:
|
||||
try:
|
||||
coarse_sub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
while True:
|
||||
try:
|
||||
stress_sub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
while True:
|
||||
try:
|
||||
ack_sub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
|
||||
# ================================
|
||||
# PART 1: STRESS VERIFICATION
|
||||
# ================================
|
||||
print("=" * 60)
|
||||
print("PART 1: STRESS PERTURBATION VERIFICATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Request stress snapshot before perturbation
|
||||
print("Requesting baseline stress snapshot...")
|
||||
send_cmd(cmd_pub, {"cmd": "stress_snapshot_now"})
|
||||
time.sleep(1)
|
||||
before = recv_stress_snapshot(stress_sub, timeout=5.0)
|
||||
if before is None:
|
||||
print("FAIL: Could not receive baseline stress snapshot")
|
||||
exit(1)
|
||||
bcycle, bsxx, bsyy, bsxy = before
|
||||
print(f"Baseline stress snapshot at cycle {bcycle}")
|
||||
|
||||
# Compute stats at the injection site (patch around 512,512)
|
||||
cx, cy = 512, 512
|
||||
patch_r = 5 # 11x11 patch
|
||||
y0, y1 = max(0, cy-patch_r), min(1024, cy+patch_r+1)
|
||||
x0, x1 = max(0, cx-patch_r), min(1024, cx+patch_r+1)
|
||||
before_sxy_mean = np.mean(bsxy[y0:y1, x0:x1])
|
||||
before_sxy_std = np.std(bsxy[y0:y1, x0:x1])
|
||||
print(f"Before perturb: sxy mean={before_sxy_mean:.6e}, std={before_sxy_std:.6e} at ({cx},{cy})")
|
||||
|
||||
# Apply perturb_stress
|
||||
print("\nApplying perturb_stress...")
|
||||
send_cmd(cmd_pub, {"cmd": "perturb_stress", "x": 512.0, "y": 512.0, "sigma": 16.0, "strength": 0.1})
|
||||
time.sleep(1)
|
||||
acks = recv_ack(ack_sub, timeout=3.0)
|
||||
for a in acks:
|
||||
print(f" ACK: {a}")
|
||||
|
||||
# Request stress snapshot after perturbation
|
||||
print("Requesting post-perturb stress snapshot...")
|
||||
send_cmd(cmd_pub, {"cmd": "stress_snapshot_now"})
|
||||
time.sleep(1)
|
||||
after = recv_stress_snapshot(stress_sub, timeout=5.0)
|
||||
if after is None:
|
||||
print("FAIL: Could not receive post-perturb stress snapshot")
|
||||
exit(1)
|
||||
acycle, asxx, asyy, asxy = after
|
||||
print(f"Post-perturb stress snapshot at cycle {acycle}")
|
||||
|
||||
after_sxy_mean = np.mean(asxy[y0:y1, x0:x1])
|
||||
after_sxy_std = np.std(asxy[y0:y1, x0:x1])
|
||||
delta_sxy = after_sxy_mean - before_sxy_mean
|
||||
threshold_sxy = 1e-8
|
||||
|
||||
print(f"\nAfter perturb: sxy mean={after_sxy_mean:.6e}, std={after_sxy_std:.6e}")
|
||||
print(f"Delta sxy mean: {delta_sxy:.6e}")
|
||||
print(f"Pre-committed threshold: |delta| > {threshold_sxy:.1e}")
|
||||
if abs(delta_sxy) > threshold_sxy:
|
||||
print("STRESS VERIFICATION: PASS -- f_neq actually changed!")
|
||||
else:
|
||||
print("STRESS VERIFICATION: FAIL -- no detectable change in sxy")
|
||||
print("(This means the perturbation was equilibrated away before the snapshot)")
|
||||
|
||||
# ================================
|
||||
# PART 2: HOLD-TIME A/B TEST
|
||||
# ================================
|
||||
print("\n" + "=" * 60)
|
||||
print("PART 2: HOLD-TIME A/B -- DENSITY vs STRESS")
|
||||
print("=" * 60)
|
||||
|
||||
SETTLE_CYCLES = 2600 # >=10 khra periods at default w_khra=0.025
|
||||
MEASURE_CYCLES = 2600
|
||||
|
||||
# Drain coarse channel, let system settle
|
||||
print(f"Waiting {SETTLE_CYCLES//100*10} sec for settle + baseline collection...")
|
||||
time.sleep(SETTLE_CYCLES // 100 * 10 / 1000 + 5) # rough wait
|
||||
|
||||
def capture_baseline(n_frames=300):
|
||||
"""Capture baseline coarse frames for comparison."""
|
||||
frames = []
|
||||
while len(frames) < n_frames:
|
||||
try:
|
||||
data = coarse_sub.recv(flags=zmq.NOBLOCK)
|
||||
if len(data) >= 16 + 4*6144 and data[:4] == b'KGCF':
|
||||
cycle = struct.unpack_from('<I', data, 4)[0]
|
||||
vals = np.frombuffer(data, dtype=np.float32, count=6144, offset=16)
|
||||
field = vals.reshape(32, 32, 6)
|
||||
frames.append((cycle, field.astype(np.float64)))
|
||||
except zmq.ZMQError:
|
||||
time.sleep(0.02)
|
||||
return frames
|
||||
|
||||
def run_hold_time_test(perturb_cmd, label):
|
||||
"""Run a hold-time measurement for a given perturbation.
|
||||
Returns (hold_time_cycles, bootstrap_ci_low, bootstrap_ci_high)."""
|
||||
print(f"\n--- {label} ---")
|
||||
|
||||
# Capture baseline
|
||||
print("Capturing baseline...")
|
||||
baseline = capture_baseline(n_frames=250)
|
||||
if len(baseline) < 200:
|
||||
print(f"FAIL: only captured {len(baseline)} baseline frames")
|
||||
return None
|
||||
baseline_cycle = baseline[-1][0]
|
||||
print(f"Baseline: {len(baseline)} frames, last cycle {baseline_cycle}")
|
||||
|
||||
# Compute baseline variance for threshold
|
||||
# Use the mean field over baseline frames
|
||||
baseline_fields = np.stack([f[1] for f in baseline])
|
||||
baseline_mean = np.mean(baseline_fields, axis=0)
|
||||
baseline_var = np.var(baseline_fields, axis=0)
|
||||
global_baseline_var = np.mean(baseline_var)
|
||||
threshold_d = 0.01 * global_baseline_var # pre-committed: 1% of baseline noise
|
||||
print(f"Baseline mean variance: {global_baseline_var:.6e}, return threshold: {threshold_d:.6e}")
|
||||
|
||||
# Apply perturbation
|
||||
print(f"Applying perturbation at cycle ~{baseline_cycle}...")
|
||||
send_cmd(cmd_pub, perturb_cmd)
|
||||
time.sleep(0.2)
|
||||
inject_cycle = baseline_cycle + 5 # estimate
|
||||
print(f"Estimated injection cycle: {inject_cycle}")
|
||||
|
||||
# Collect post-perturbation frames (>=2510 cycles = ~260 frames at 10-cycle tick)
|
||||
# The coarse stream emits every 10 cycles, so 260 frames = 2600 cycles
|
||||
print(f"Collecting post-perturb frames (target ~260 frames)...")
|
||||
post_frames = capture_baseline(n_frames=280)
|
||||
print(f"Captured {len(post_frames)} post-perturb frames")
|
||||
|
||||
if len(post_frames) < 50:
|
||||
print("FAIL: too few post-perturb frames")
|
||||
return None
|
||||
|
||||
# Compute L2 distance from baseline mean at each frame
|
||||
distances = []
|
||||
cycles = []
|
||||
for cycle, field in post_frames:
|
||||
d = np.mean((field - baseline_mean) ** 2)
|
||||
distances.append(d)
|
||||
cycles.append(cycle)
|
||||
|
||||
distances = np.array(distances)
|
||||
cycles = np.array(cycles)
|
||||
|
||||
# Find hold-time: first frame where distance drops below threshold
|
||||
# Start from frame 20 (skip initial transient)
|
||||
hold_idx = None
|
||||
for i in range(20, len(distances)):
|
||||
if distances[i] < threshold_d:
|
||||
# Confirm: next 5 frames also below threshold
|
||||
if i + 5 < len(distances) and np.all(distances[i:i+5] < threshold_d):
|
||||
hold_idx = i
|
||||
break
|
||||
|
||||
if hold_idx is not None:
|
||||
hold_cycle = cycles[hold_idx] - inject_cycle
|
||||
hold_cycles_float = float(hold_cycle)
|
||||
else:
|
||||
print("WARNING: never crossed threshold -- perturbation may persist beyond window")
|
||||
hold_cycle = len(post_frames) * 10 # rough max
|
||||
hold_cycles_float = float(hold_cycle)
|
||||
|
||||
print(f"Hold-time (raw): {hold_cycles_float:.0f} cycles")
|
||||
print(f"Distance range: [{distances[0]:.6e}, {distances[-1]:.6e}]")
|
||||
|
||||
# Bootstrap CI
|
||||
np.random.seed(42)
|
||||
boot_samples = []
|
||||
for _ in range(200):
|
||||
# Resample baseline frames with replacement
|
||||
idx = np.random.choice(len(baseline_fields), len(baseline_fields), replace=True)
|
||||
boot_baseline_mean = np.mean(baseline_fields[idx], axis=0)
|
||||
boot_baseline_var = np.mean(np.var(baseline_fields[idx], axis=0))
|
||||
boot_threshold = 0.01 * boot_baseline_var
|
||||
|
||||
# Compute distances against boot baseline
|
||||
boot_distances = []
|
||||
for cycle, field in post_frames:
|
||||
d = np.mean((field - boot_baseline_mean) ** 2)
|
||||
boot_distances.append(d)
|
||||
boot_distances = np.array(boot_distances)
|
||||
|
||||
# Find hold-time with boot threshold
|
||||
boot_hold_idx = None
|
||||
for i in range(20, len(boot_distances)):
|
||||
if boot_distances[i] < boot_threshold and i + 5 < len(boot_distances):
|
||||
if np.all(boot_distances[i:i+5] < boot_threshold):
|
||||
boot_hold_idx = i
|
||||
break
|
||||
if boot_hold_idx is not None:
|
||||
boot_samples.append(float(cycles[boot_hold_idx] - inject_cycle))
|
||||
else:
|
||||
boot_samples.append(float(len(post_frames) * 10))
|
||||
|
||||
ci_low = np.percentile(boot_samples, 2.5)
|
||||
ci_high = np.percentile(boot_samples, 97.5)
|
||||
|
||||
print(f"Hold-time: {hold_cycles_float:.0f} cycles, 95% CI: [{ci_low:.0f}, {ci_high:.0f}]")
|
||||
return hold_cycles_float, ci_low, ci_high
|
||||
|
||||
# Run Arm A: density
|
||||
result_a = run_hold_time_test(
|
||||
{"cmd": "inject_density", "x": 512.0, "y": 512.0, "sigma": 16.0, "strength": 0.1},
|
||||
"Arm A: inject_density"
|
||||
)
|
||||
|
||||
# Wait for system to settle back
|
||||
print(f"\nWaiting {SETTLE_CYCLES//100*10/1000:.0f}s for field to settle...")
|
||||
time.sleep(15)
|
||||
|
||||
# Run Arm B: stress
|
||||
result_b = run_hold_time_test(
|
||||
{"cmd": "perturb_stress", "x": 512.0, "y": 512.0, "sigma": 16.0, "strength": 0.1},
|
||||
"Arm B: perturb_stress"
|
||||
)
|
||||
|
||||
# ================================
|
||||
print("\n" + "=" * 60)
|
||||
print("RESULTS SUMMARY")
|
||||
print("=" * 60)
|
||||
print(f"\nSTRESS VERIFICATION: {'PASS' if abs(delta_sxy) > threshold_sxy else 'FAIL'}")
|
||||
print(f" Before sxy at site: {before_sxy_mean:.6e}")
|
||||
print(f" After sxy at site: {after_sxy_mean:.6e}")
|
||||
print(f" Delta: {delta_sxy:.6e} (threshold: {threshold_sxy:.1e})")
|
||||
|
||||
print(f"\nHOLD-TIME A/B:")
|
||||
if result_a:
|
||||
ht, lo, hi = result_a
|
||||
print(f" Arm A (density): {ht:.0f} cycles, 95% CI [{lo:.0f}, {hi:.0f}]")
|
||||
else:
|
||||
print(f" Arm A (density): FAILED")
|
||||
|
||||
if result_b:
|
||||
ht, lo, hi = result_b
|
||||
print(f" Arm B (stress): {ht:.0f} cycles, 95% CI [{lo:.0f}, {hi:.0f}]")
|
||||
else:
|
||||
print(f" Arm B (stress): FAILED")
|
||||
|
||||
# Cleanup
|
||||
cmd_pub.close()
|
||||
ack_sub.close()
|
||||
stress_sub.close()
|
||||
coarse_sub.close()
|
||||
CTX.term()
|
||||
print("\nDone.")
|
||||
@@ -0,0 +1,43 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Test save_state/load_state round trip."""
|
||||
import zmq, json, time, subprocess, glob, os
|
||||
|
||||
ctx = zmq.Context()
|
||||
cp = ctx.socket(zmq.PUB)
|
||||
cp.connect("tcp://127.0.0.1:5557")
|
||||
ack_sub = ctx.socket(zmq.SUB)
|
||||
ack_sub.connect("tcp://127.0.0.1:5559")
|
||||
ack_sub.setsockopt_string(zmq.SUBSCRIBE, "")
|
||||
time.sleep(0.3)
|
||||
|
||||
# Test save_state with path "."
|
||||
print("SAVING to . ...")
|
||||
cp.send_string(json.dumps({"cmd": "save_state", "path": "."}))
|
||||
time.sleep(2)
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, 5000)
|
||||
try:
|
||||
msg = ack_sub.recv_string()
|
||||
print("ACK save:", msg)
|
||||
except Exception as e:
|
||||
print("ERR save:", e)
|
||||
|
||||
# Check checkpoint files
|
||||
files = sorted(glob.glob("/mnt/d/resonance-engine-active/ckpt_*.bin"))
|
||||
print(f"Ckpt files after save: {len(files)}")
|
||||
if files:
|
||||
latest = files[-1]
|
||||
print(f"Latest: {latest}")
|
||||
print(f"LOADING {latest} ...")
|
||||
cp.send_string(json.dumps({"cmd": "load_state", "path": latest}))
|
||||
time.sleep(2)
|
||||
ack_sub.setsockopt(zmq.RCVTIMEO, 5000)
|
||||
try:
|
||||
msg = ack_sub.recv_string()
|
||||
print("ACK load:", msg)
|
||||
except Exception as e:
|
||||
print("ERR load:", e)
|
||||
|
||||
cp.close()
|
||||
ack_sub.close()
|
||||
ctx.term()
|
||||
print("DONE")
|
||||
@@ -0,0 +1,65 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Round-trip test: set_param lam_gixx 16.0, verify ack and telemetry change, restore."""
|
||||
import zmq, json, time, subprocess
|
||||
|
||||
ctx = zmq.Context()
|
||||
|
||||
# Subscribe to acks on 5559
|
||||
ack_sub = ctx.socket(zmq.SUB)
|
||||
ack_sub.connect('tcp://127.0.0.1:5559')
|
||||
ack_sub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
|
||||
# Send command to 5557
|
||||
cmd_pub = ctx.socket(zmq.PUB)
|
||||
cmd_pub.connect('tcp://127.0.0.1:5557')
|
||||
time.sleep(0.3) # allow connect
|
||||
|
||||
print('Sending: set_param lam_gixx=16.0')
|
||||
cmd_pub.send_string(json.dumps({'cmd': 'set_param', 'param': 'lam_gixx', 'value': 16.0}))
|
||||
time.sleep(4)
|
||||
|
||||
# Read acks
|
||||
while True:
|
||||
try:
|
||||
msg = ack_sub.recv_string(flags=zmq.NOBLOCK)
|
||||
print('ACK:', msg)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
|
||||
# Check telemetry
|
||||
result = subprocess.run(['tail', '-1', '/mnt/d/resonance-engine-active/telemetry.jsonl'],
|
||||
capture_output=True, text=True)
|
||||
d = json.loads(result.stdout.strip())
|
||||
print(f'lam_gixx={d.get("lam_gixx")} alpha={d.get("alpha")} cycle={d.get("cycle")}')
|
||||
|
||||
# Set back to 8.0
|
||||
print('Restoring: set_param lam_gixx=8.0')
|
||||
cmd_pub.send_string(json.dumps({'cmd': 'set_param', 'param': 'lam_gixx', 'value': 8.0}))
|
||||
time.sleep(4)
|
||||
while True:
|
||||
try:
|
||||
msg = ack_sub.recv_string(flags=zmq.NOBLOCK)
|
||||
print('ACK (restore):', msg)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
|
||||
result = subprocess.run(['tail', '-1', '/mnt/d/resonance-engine-active/telemetry.jsonl'],
|
||||
capture_output=True, text=True)
|
||||
d = json.loads(result.stdout.strip())
|
||||
print(f'After restore: lam_gixx={d.get("lam_gixx")} alpha={d.get("alpha")} cycle={d.get("cycle")}')
|
||||
|
||||
# Test rejection: out-of-range value
|
||||
print('Testing rejection: lam_gixx=200.0 (out of range)')
|
||||
cmd_pub.send_string(json.dumps({'cmd': 'set_param', 'param': 'lam_gixx', 'value': 200.0}))
|
||||
time.sleep(2)
|
||||
while True:
|
||||
try:
|
||||
msg = ack_sub.recv_string(flags=zmq.NOBLOCK)
|
||||
print('ACK (reject):', msg)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
|
||||
cmd_pub.close()
|
||||
ack_sub.close()
|
||||
ctx.term()
|
||||
print('Done.')
|
||||
@@ -0,0 +1,73 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Quick stress perturbation verification."""
|
||||
import zmq, json, time, struct
|
||||
import numpy as np
|
||||
|
||||
ctx = zmq.Context()
|
||||
cp = ctx.socket(zmq.PUB)
|
||||
cp.connect('tcp://127.0.0.1:5557')
|
||||
asub = ctx.socket(zmq.SUB)
|
||||
asub.connect('tcp://127.0.0.1:5559')
|
||||
asub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
ssub = ctx.socket(zmq.SUB)
|
||||
ssub.connect('tcp://127.0.0.1:5560')
|
||||
ssub.setsockopt_string(zmq.SUBSCRIBE, '')
|
||||
time.sleep(0.5)
|
||||
|
||||
# Drain
|
||||
while True:
|
||||
try:
|
||||
ssub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
while True:
|
||||
try:
|
||||
asub.recv(flags=zmq.NOBLOCK)
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
|
||||
# Baseline stress snapshot
|
||||
cp.send_string(json.dumps({'cmd': 'stress_snapshot_now'}))
|
||||
time.sleep(0.8)
|
||||
d0 = ssub.recv()
|
||||
sxy0 = np.frombuffer(d0, dtype=np.float32, count=1048576, offset=8 + 8 * 1048576)
|
||||
sxy0 = sxy0.reshape(1024, 1024)
|
||||
bx = np.mean(sxy0[507:518, 507:518])
|
||||
print(f'Baseline sxy at [507:518,507:518]: {bx:.8e}')
|
||||
|
||||
# Apply perturb_stress
|
||||
cp.send_string(json.dumps({'cmd': 'perturb_stress', 'x': 512, 'y': 512, 'sigma': 16, 'strength': 0.1}))
|
||||
time.sleep(0.3)
|
||||
acks = []
|
||||
while True:
|
||||
try:
|
||||
acks.append(json.loads(asub.recv_string()))
|
||||
except zmq.ZMQError:
|
||||
break
|
||||
for a in acks:
|
||||
s = json.dumps(a)
|
||||
print(f'ACK: {s[:150]}')
|
||||
|
||||
# Post stress snapshot
|
||||
time.sleep(0.2)
|
||||
cp.send_string(json.dumps({'cmd': 'stress_snapshot_now'}))
|
||||
time.sleep(0.8)
|
||||
d1 = ssub.recv()
|
||||
sxy1 = np.frombuffer(d1, dtype=np.float32, count=1048576, offset=8 + 8 * 1048576)
|
||||
sxy1 = sxy1.reshape(1024, 1024)
|
||||
ax = np.mean(sxy1[507:518, 507:518])
|
||||
delta = ax - bx
|
||||
print(f'Post sxy at [507:518,507:518]: {ax:.8e}')
|
||||
print(f'Delta: {delta:.8e}')
|
||||
print(f'Pre-committed threshold: |delta| > 1e-8')
|
||||
passed = abs(delta) > 1e-8
|
||||
print(f'VERIFICATION: {"PASS" if passed else "FAIL"}')
|
||||
if passed:
|
||||
print('f_neq actually changed -- stress perturbation confirmed.')
|
||||
else:
|
||||
print('f_neq did NOT change -- perturbation was equilibrated away before snapshot.')
|
||||
|
||||
cp.close()
|
||||
asub.close()
|
||||
ssub.close()
|
||||
ctx.term()
|
||||
Reference in New Issue
Block a user