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