119 lines
7.7 KiB
Markdown
119 lines
7.7 KiB
Markdown
# Olfactory conditioning lab — unregistered preparation
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This directory is an independent, unfinished model-branch implementation.
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Its 21 engineering sources are registered and sealed by Phase 4AN; those files
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must not be edited. Phase 4AM and the finite Phase 4AN engineering gate passed.
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The scientific conditioning proposal and additional planning modules remain
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unregistered, have not run training, and have not produced a learned candidate.
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Engineering PASS is not learning PASS.
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Engineering evidence: `registry/phase4an-engineering-validation` in the project
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repository, commit `aeccb7d0ccdcf6788e0a8de60e7ab96f70dbe734`. Its exact
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21-source inventory is in `source-identity.json`; this README is not one of
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those registered sources. The experiment manifest SHA-256 is
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`e8952a48cd3ea6a93c811a1183a1f7b79e19646299924e7688e5f393737a9aa2`.
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## Existing code
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- `runtime_light.py`: derives a fixed-weight trial runner from the SHA-pinned
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AJ source. Its full-recording reference retains the entire original tick
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loop. The light variant removes an explicit diagnostics whitelist while
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preserving the ordered mathematical AST. It retains sparse events, stimulus
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acceptance, terminal neural state and delay queues. It supports an explicit
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longer admission bound, not hidden time truncation or approximate delays.
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- `seed_registry.py`: explicitly separates training, evaluation and schedule
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design random streams. Declared same-split aliases can give controls common
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random inputs; aliases do not count as independent replicates. Duplicate
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keys, cross-split aliases and seed collisions stop preparation. A full
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manifest is independently reconstructible.
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- `runtime_continuation.py`: carries full terminal state, spike/APL delay
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queues and the absolute clock across fixed-weight segments. It protects
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every ancestor checkpoint against output/build writes. The explicit
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arrival-admission change and reporting hooks are AST-audited. The finite AN
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gate passed 32 actual GPU calls and 74 exact comparisons, including pending
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arrivals across a split. This does not verify every future 120/180 ms
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training epoch or the unexecuted 1/10 s retention protocol.
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- `biological_contract.py`: binds observed canonical identity, exact DAN/MBON
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compartment tokens and real graph edges without filling unknown relations.
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- `trial_plan.py`: constructs and independently validates the proposed complete
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40-block training/control matrix and explicit random-stream aliases. It has
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not selected a formal master seed or generated scientific brain inputs.
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Complete-protocol measured storage and runtime budgets remain unfinished.
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- `evaluation_plan.py`: requires explicit evaluation timing, reversal points
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and per-control snapshot bindings. It reconstructs the parent inventory and
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retains 80 independent A/B evaluation streams, paired across snapshots within
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each training replicate. It does not bind actual state files or authorize a run.
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- `evaluation_policy.py`: supplies the explicit draft policy in
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`EVALUATION_POLICY_DRAFT.md`: 100 ms CS, 120 ms probes, seven curve points,
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actual ordered-CS-prefix control alignment and separate US/time accounting.
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Its conditional inventory contains 9,440 probes, not executed trials.
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- `conditioning_inputs.py`: consumes a full 63-bit semantic seed in the original
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discrete float32 Bernoulli CUDA sampler, with explicit device/draw budget and
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CS window. CPU/mock tests do not establish CUDA random-sequence equivalence or
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the influence of high seed bits. It never substitutes a CPU random generator.
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- `conditioning_update.py`: uses the SHA-pinned AM/J bridge to compute a single
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epoch-end delta from supplied actual sparse events. It never installs weights;
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scheduled D, actual DAN activity, attempted increments and actual changed
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edges remain distinct. Synthetic validation does not establish a trained brain.
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- `metrics.py`: pure, explicitly draft functional gates; keeps the historical
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downstream population and the non-MBON difference as distinct readouts.
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Caller-bound receipts and anatomical membership are still required.
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- `test_*.py`: CPU synthetic/structural checks. These cannot establish real
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GPU equivalence, biological validity or successful learning.
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- `EVIDENCE_DRAFT.md` and `PROTOCOL_DRAFT.md`: evidence and protocol proposals;
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neither is an approved or frozen experiment.
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## Required before registration or training
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1. Preserve completed AM/AN engineering evidence. AM completed its 144 zero
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and 144 fixed-overlay runs, five old frozen reference trials, and integrity
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checks. AN's 32-call continuation gate passed its finite exact comparisons
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and 28-root integrity checks; it did not rerun the five old reference trials.
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Neither gate executed conditioning.
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2. Review and lock the complete experimental matrix, true executed controls,
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separate training/evaluation stream table, data budget and stop rules.
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Report any additional gates as new model hypotheses, not inherited facts.
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The generic seed registry alone does not prove protocol coverage or semantic
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independence: preparation must require every planned stream and permit
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aliases only within the same training replicate and input role, for named
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matched controls/pre-post comparisons. Never alias two training replicates
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or CS and US roles. Save generator/version, target ordering, rates, timing,
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consumed stream ID and actual requests hash. Do not truncate a 63-bit seed
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to 32 bits or derive a new seed by adding the epoch number downstream.
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3. Bind **all** graph arrays, APL bundle, coefficient JSON, base-candidate
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vector, full 14,232-edge mask, DAN/MBON identity tables and input files to
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the registered manifest. A runtime's self-consistent spec or source hash
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is not sufficient authority for these data.
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4. Preserve the completed finite real-GPU light/reference comparisons, including exact
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ordered events, per-neuron counts, terminal V/g/ref, external-request
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acceptance, APL terminal state and both pending delay queues. No tolerance
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may excuse a spike divergence. Benchmark encoding, loading, simulation,
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validation and output time separately and together.
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5. Validate the actual retention protocol before claiming retention. The
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separate continuation module passed the finite AN GPU gate. V/g/ref,
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spike-ring phase, APL state/pending phase and global tick must survive;
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segmented and monolithic executions must agree. A checkpoint seal binds
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fresh state bytes when created; it cannot prove pre-seal immutability.
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6. Verify every learned snapshot's parent chain, input events and exact
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frozen eligibility-v2 update. The single-trial runtime verifies the
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bounded overlay and parent-hash declaration, but does not prove the
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coordinator actually computed that update. Within each neural trial the
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delta is fixed. The original base is never written.
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7. Record the realized wall-time/output budget before the full acquisition
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suite. A failed acquisition gate must not be rescued by tuning eta,
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thresholds, APL gain, trial count, mask membership or selected odors.
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## Interpretation limits
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PAM15 and PPL103 are specific, statically mapped artificial US interfaces;
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PAM/PPL cluster names are not general valence labels. The fixed eligibility
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rule is a single-direction causal-LTD engineering hypothesis, not a complete
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model of dopamine, bidirectional plasticity, forgetting or consolidation.
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The proposed fixed MBON type contrast is offline analysis, not a decoder.
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No old sealed artifact, raw connectome, transmitter sign, eligibility-v2 or
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base weight is modified here. No game, reinforcement-learning policy,
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classifier, action decoder, visual encoder, web service or 3D interface is
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introduced. A future learning PASS requires functional stimulus-specific
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responses and executed controls, never merely a changed delta array.
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