Add bounded same-state update validation and functional gates
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Digital Drosophila Archive
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# Conditioning execution branch — bounded preparation protocol
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Status: DRAFT, NOT REGISTERED, NO GPU EXECUTION AUTHORITY YET.
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This is a new independent model branch authorized by the user. All previously
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registered sources, raw data, frozen brains and experiment artifacts remain
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read-only. Existing scientific failures remain failures. The completed AP
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interface test does not establish functional learning.
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## Scope and the next scientific decision
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Consolidate updated-weight/same-state continuation, fixed functional gates and
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measured complete-process resource costs in this package. Then register and run
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the already proposed normal acquisition and all six controls, for all eight
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cells and five independent training replicates. Do not keep replacing functional
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learning experiments with unbounded engineering milestones.
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No learning-rate, threshold, gain, transmitter, anatomical weight, input-rate,
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odor, mask, APL calibration or eligibility-v2 changes are permitted by this
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package. Any future biological dynamics hypothesis requires a separately named
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model/version and a pre-outcome hypothesis; permission for a new branch does not
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reclassify a changed model as an exact optimization of the old one.
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No games, decoder, RL, visual stimulation, web services or 3D are included.
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## Exact boundary semantics
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- A fresh, sealed neural epoch and its actual original E postprocess identify
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the one permitted update. Recompute original U from the actual sealed spike
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events in a separate CPU interpreter, without executing the brain or E again.
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- Install original base plus the full after-delta once, using the frozen
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float32-mV to float64-SI bridge. Never add the after-delta to old effective
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weights. Keep the original 14,232 existing candidate edges and fixed floor.
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- Preserve voltage, existing g, refractory state, pending spike ring, APL state,
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complete APL pending amplitudes and absolute tick. Counts/events are explicitly
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segment-local in the new recording, not falsely lifetime counts.
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- Already accumulated g is not recomputed or rescaled. Pending presynaptic spike
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flags use current weights when they arrive, exactly as the frozen runtime
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does. Already queued APL amplitudes are preserved. The delays remain 18 steps;
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the spike ring has 19 slots and the APL ring has 18.
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- Later idle continuations retain the same after-overlay. A probe can fork a
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checkpoint but cannot become an idle ancestor. No analytical time advance.
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- New checkpoints use their own explicit updated-boundary schema. They do not
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claim the original C fixed-weight boundary rule was satisfied.
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The independent oracle re-executes the exact fresh parent prefix, never calls
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restore, verifies live prefix state/events against that parent, changes only
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the authorized weights once and executes the unchanged reference timestep loop.
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Its explicit within-run weight switch is diagnostic, not a production backend.
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The one added boundary hook is independently removed to verify exact AST
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rollback. Its honest `within_trial_weight_updates=true` admission is not passed
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off as an unchanged-weight checkpoint.
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## Bounded validation inventory
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Use sealed AP reference epoch-001 parents for D1, D0 and frozen. New outputs never
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go into AP. Compare D1 restored reference/light 100 ms suffixes to a fresh
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220 ms continuous oracle. Compare D0/frozen new reference/light suffixes to
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original C fixed-weight continuation. Compare all events and final state/queues
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exactly; compare per-tick hashes, request acceptance, CSR queue and APL traces
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where actually recorded. Do not claim light records unseen intermediate states.
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AP D1 epoch-001 has 765 spikes in its final 18 ticks, but none from its 79
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changed-edge sources. Thus it has useful real pending queues but cannot alone
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prove updated-weight handling on a pending changed candidate edge.
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Add one deterministic mechanical diagnostic parent: 120 ms fresh dynamics with
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no requests until tick 1199, then one requested spike in the minimum canonical
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KC source ID among actual y5B'2a candidates, together with the four verified
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PAM15 DAN IDs. Run original U with US at tick 1199, D=1 and the unchanged rule.
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This is a test fixture stimulus, not a new odor or a learning-success trial.
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No seed search or parameter adjustment follows missing coverage. Require a
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real changed existing candidate edge, a real source spike crossing the boundary,
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and an accepted timed arrival. Nonempty queues alone are insufficient. Compare
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its restored reference/light suffixes to the independent continuous oracle.
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Also run actual no-input 1 s then 9 s idle dynamics from AP D1, preserving one
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lineage. At 0, 1 and 10 s fork A/B no-US/no-update 120 ms probes. The two saved
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evaluation inputs use the original first block's distinct evaluation streams
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from master 20260930, the original L physiological rate tables and [0,100 ms)
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CS window; no sampler is run until registration. Repeated probes are not new
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training seeds and never feed back to the idle chain.
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The bounded engineering suite does not run acquisition or claim retention of
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a learned association. A failure is preserved with partial outputs; no silent
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retry, replacement seed or post-result trial selection is allowed.
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Two fixed measurement calls avoid extrapolating incomplete or mismatched costs.
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Re-execute AP D1 epoch-001's original saved natural input from a fresh state and
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its recorded before-overlay, using light recording followed by original E and
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the independent CPU validator. Compare its events, final state and actual delta
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to AP. Separately measure one fresh-state/no-US A evaluation probe with the
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original zero overlay. Retention-continuation probes are not substituted for
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normal fresh-state probe costs. These measurements are engineering diagnostics,
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not additional acquisition or a seed search.
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The complete proposed inventory is 23 new brain calls / 125,200 ticks, seven
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CPU update/validation children and one GPU sampler process (two uniform batches):
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31 child processes. Compilation caches may evolve and are inventoried separately;
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previous scientific outputs remain immutable. All future checkpoints/hashes are
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derived from actual completed children, not fabricated during registration.
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## Functional gates fixed before acquisition
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`learning_gates.py` retains the frozen normal/delayed predicates, requires five
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independent training replicates, checks full 63-bit stream identities and keeps
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evaluation streams separate. A majority means at least three replicates each
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satisfy the entire conjunction, not different seeds passing different clauses.
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Two explicit additional operationalizations are fixed before outcomes:
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1. Reversal and real-time retention use nonMBONDownstream for the downstream
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functional gate, while reporting the entire legacyDownstream union as well.
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MBON-local change cannot be counted twice as downstream transfer.
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2. The legacy H mean-control predicate is called separately for the PAM15 and
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PPL103 control sources in each (input mode, reinforced cue) group. Both must
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pass that separate audit. No control pooling or favorable-source selection.
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Reversal keeps the original conditioned-minus-neutral orientation and original
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pre baseline, with strict nonzero sign crossing and no-reversal continuation
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remaining on its original side. Retention requires the same replicate to pass
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both 1 s and 10 s against time-matched frozen controls. Delta change alone is
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never a functional learning gate. See the pinned original PROTOCOL_DRAFT and
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EVALUATION_POLICY_DRAFT for the complete unchanged input/assay inventory.
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Pure gate functions authenticate neither disk/process evidence nor anatomy;
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their results explicitly say conditional predicates, not scientific PASS.
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The eventual coordinator must bind completed real trajectories and input,
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population, seed, delta and checkpoint receipts before making a learning claim.
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## Resource accounting and stop policy
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Proposed bounded validation cap: 4 GiB of new outputs, at least 10 GiB free
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reserve, 1,800 seconds per child and 7,200 seconds total. Confirm measured free
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space and commit the registration before execution. Use only the admitted
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RTX 4090, check all three ComfyUI queues and do not stop or evict services.
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Measure parent wall time from before spawn through actual exit and independent
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result validation, including input hashing, CUDA startup/compilation, graph load,
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CPU update verification, lossless compression and I/O. Worker/core timing is a
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separate field. No extrapolation may label GPU-only time as complete latency.
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The proposed normal suite has 30,000 training/control epochs plus 3,920 probes:
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33,920 brain calls and 4,070.4 biological seconds. The full proposal has 68,000
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epochs, 9,440 probes and 80 actual 10 s idle lineages, totaling 11,892.8 biological
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seconds before engineering validation. These are inventory arithmetic, not wall
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time or disk measurements. Predeclare a finite normal-run resource budget using
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measured complete outputs, a stated reserve and explicit uncertainty about
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learning-dependent activity. No unbounded automatic launch is authorized here.
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If normal acquisition fails the registered all-cell gate, preserve and seal the
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actual failure; delayed/reversal/retention are NOT-RUN. Do not tune to rescue it.
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If it passes, run the already registered follow-up matrix. Frozen integrity and
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required numerical regressions remain independent gates, not learning evidence.
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## Provenance
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Before registration require every new source to match its recorded Git commit,
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snapshot the protocol and sources, and pin all old inputs by SHA-256. Record
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actual child exits; worker self-receipts do not prove their own process exited.
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All output files receive SHA-256 and an exact manifest at sealing. No old source
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or runtime global is patched. Full experimental arrays remain server-side;
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source/evidence Git synchronization must not be described as a bulk-data backup.
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`audit_execution.py` reads actual recorded process exits and full output hashes;
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`prepare_execution.py integrity` runs in a separate CPU process after execution.
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`finalize_execution.py` requires externally supplied registration, numerical,
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budget and after-integrity report hashes and creates an exclusive experiment
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seal. The entire model directory is not itself claimed sealed: every registered
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source is bound to its committed blob and sealed source snapshot instead.
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`budget_report.py` may return insufficient information rather than invent future
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large-suite overhead. A few measured cycles cannot establish the cost of a
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growing full-directory hash policy, unknown learning-dependent activity or final
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integrity work. No normal-suite resource assumptions or execution authorization
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are implicitly supplied by this finite engineering registration. Real measured
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cycle cost and conservative accounting bounds must still be reported even when
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a credible reserved complete-suite projection is unavailable.
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