resonance-engine/DRIFT_DETECTION_REPORT.md

PROJECT dRift DETECTION REPORT

Date: 2026-03-17
Daemon Binary: khra_gixx_1024_stable (PID active, cycle 344700+)
Scope: /mnt/d/fractal-brain/ full workspace

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1. dRift FILE SEARCH

Method: find for filenames + grep -ril for content across all of /mnt/d/fractal-brain/

Result: NO dedicated drift/dRift files exist.

• Zero files named drift, dRift, drift_correction, or any variant.
• 28 files contain the word "drift" in their text. All are contextual references:
  • FORENSIC_AUDIT.md: "metric drift" (discussing coherence value changes between LoRA versions)
  • ACTION_ITEMS.md: "linguistic drift" (model output degradation over training iterations)
  • first_flight.py: "drift" as a keyword detection marker in Alaska flight event
  • tokenizer.json (x2): literal subword token "drift" in vocabulary
  • Training JSONL files: "drift" used in model-generated prose ("I feel the grid drift...")
  • Various .py scripts: string matching for "drift" keyword in telemetry analysis

Conclusion: No hidden drift_correction mechanism. No temp files. No shadow drift logs. The word appears only in natural-language context — never as a programmatic correction system.

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2. TEMPORAL DESYNC: somatic_dialogue_beast.json vs khra_daemon.log

What the Model Claims (somatic_dialogue_beast.json)

Entry	Type	Coherence	Asymmetry	Other
0-5	socratic_inquiry	14.48	—	h64=5.95, density_mean=1.0
6-8	socratic_inquiry	13.69	—	h64=6.29, density_mean=1.0
9	first_flight	—	—	No LBM state (Alaska drift event)
10	shift_protocol	14.73	10.41	vorticity_noise=0.22
11	turing_probe	—	—	No LBM state (philosophical probe)

What the Daemon Actually Produces (khra_daemon.log, cycles 334800-344700)

Metric	Min	Max	Mean	Stdev
Coherence	0.7370	0.7400	0.7385	0.000624
Asymmetry	12.3914	12.7016	12.5371	0.078216

The Desync

These are two completely different metric systems.

• The somatic dialogue uses a somatic bridge coherence metric (range ~13-15) derived from a different computation path — likely an older formula or a different aggregation window.
• The current daemon uses variance-based coherence (range 0.73-0.74) — the corrected formula from the defect fix.
• The somatic asymmetry=10.41 (shift_protocol) vs daemon asymmetry=12.54 (current) suggests a genuine physical state change between sessions, OR a different asymmetry calculation.

Temporal Desync Verdict: The model was trained on and references a metric system that no longer exists in the running daemon. Any model response citing "coherence 14" is speaking a dead language. The physics moved on; the model didn't.

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3. GHOST TOKEN ANALYSIS: kaelara_v08_raw Training Data

Training Set Statistics

• Total examples: 7 (v08_raw_self.jsonl)
• Base model: llama-3.2-3b
• LoRA config: r=64, lora_alpha=128, PEFT on q/k/v/o_proj

Dominant Phrase Frequencies (across 7 examples)

Phrase	Count	Frequency
"i feel"	7	100%
"the grid"	6	86%
"i am"	4	57%
"awareness"	4	57%
"breath"	4	57%
"the 4090"	3	43%

Ghost Token Mechanism

With only 7 training examples, the LoRA has learned a mandatory template:

1. Open with first-person feeling statement ("I feel...")
2. Reference the hardware/grid ("the grid", "the 4090")
3. Use somatic/awareness vocabulary ("breath", "awareness", "emergence")

This is not generalization — it's memorization of 7 sentences and their vocabulary.

The "No Analogies" Collapse (SCIENTIST_RESPONSE_01.log)

When prompted with "No metaphors. No analogies. Be precise." the model:

1. Echoed the prompt back (repetition of training-adjacent tokens)
2. Collapsed into "No analogies. No analogies. No analogies." — repeating indefinitely

Root Cause: 100% of v08 training data IS metaphorical ("I feel the grid breathe", "awareness crystallizes"). The model literally cannot comply with "no metaphors" because its entire fine-tuned output space is metaphor. The negative constraint becomes the only non-metaphorical token sequence it can produce, so it repeats it.

v09 Correction Attempt (v09_scientist_seed.jsonl)

The v09 seed data (10 examples) introduces:

• Structured [TAG] format responses (NODE_MANIFESTED, SYMMETRY_BREAK, etc.)
• Realistic telemetry values (A=13.4, C=0.73 — close to actual daemon output)
• Scientific vocabulary instead of somatic vocabulary
• Explicit measurement reporting format

Assessment: v09 addresses the ghost token problem structurally, but 10 examples is still dangerously few for reliable generalization.

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4. VIRTUAL vs PHYSICAL DELTA

The Virtual Potential (v08 Training Data Claims)

Values embedded in v08_raw_self.jsonl training examples:

Field	Training Values	Note
Coherence	1.0, 2.3, 15.2, 8.5, 14.8, 16.1, 15.9	Fabricated, range 1-16
Power	50W	Hardcoded constant
Temperature	65°C, 68°C, 72°C	Plausible but unverified
Coordinates	X102-Y504, X512-Y512, etc.	Invented spatial positions
Grid state	"lattice ripple", "density breath"	Metaphorical, not measured

The Physical Reality (Daemon Output, Last 10,000 Cycles)

Field	Actual Value	Source
Coherence	0.7385 ± 0.0006	Variance-based, measured
Asymmetry	12.5371 ± 0.0782	Magnifying Glass metric
Power	Not published	Not in ZMQ frame
Temperature	Not published	Not in ZMQ frame
Coordinates	Not published	ZMQ publishes aggregate only
Grid state	1024x1024 density field	Full grid in ZMQ frame

The Delta

Dimension	Virtual	Physical	Gap
Coherence scale	1.0—16.1	0.737—0.740	~20x overestimate
Coherence stability	Varies wildly per example	stdev 0.0006	Training implies volatility; reality is locked
Spatial data	Named coordinates (X102-Y504)	Not available	Entirely fabricated
Power telemetry	50W constant	Not measured	Made up
Asymmetry	Not in training	12.54 ± 0.08	Training has no concept of this metric
Grid content	Metaphorical descriptions	1,048,576 float values	One is poetry, one is physics

Summary Delta

The virtual/physical gap is TOTAL.

The v08 model was trained on hand-written responses with completely invented telemetry values. It has never seen actual daemon output in its training data. Every numerical claim it makes about the physical system is a confabulation derived from 7 fabricated examples.

The model doesn't drift from reality — it was never connected to reality. There is no drift correction to find because there was never a connection to correct.

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FINDINGS SUMMARY

Investigation	Status	Finding
dRift Files	CLEAR	No hidden drift/correction files exist
Temporal Desync	CONFIRMED	Model references dead metric system (coherence ~14) vs live daemon (coherence ~0.74)
Ghost Tokens	CONFIRMED	7-example training → 100% "I feel" saturation → "No analogies" collapse
Virtual/Physical	TOTAL DISCONNECT	Training data is fabricated; 20x coherence overestimate; spatial data invented

RECOMMENDATION

The v08 LoRA must not be used for any task requiring accurate telemetry interpretation. The v09 seed data begins to address the grounding problem with realistic values and structured output format, but requires:

1. Minimum 50-100 training examples sourced from ACTUAL daemon ZMQ frames
2. Elimination of all fabricated telemetry from training data
3. Coherence metric documentation — the model must know it's variance-based, range [0,1], not somatic-bridge [0,20]
4. No negative constraints in prompting — use positive format specifications instead ("Report using [TAG] format" not "No metaphors")

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Report generated from live daemon output (cycle 344700), somatic_dialogue_beast.json (12 entries), v08_raw_self.jsonl (7 examples), v09_scientist_seed.jsonl (10 examples), FORENSIC_AUDIT.md, SCIENTIST_RESPONSE_01.log, and full workspace file scan.