Date: 2026-09-22 Status: CONTEXT READ INTERFACE IMPLEMENTED — QUALIFICATION PENDING

Starting failure and diagnosis

The prior sealed Context probe reached 0.2% final accuracy despite proving that Context residuals changed hidden state. The current path used detached, normalized Context vectors added with a fixed bounded residual before the final normalization and output projection. Foundation had no learned mapping from that representation to answer-generating hidden states. This directive therefore tested a separate target-side reader without changing Foundation, Memory, or Context state semantics.

Reader implementation

ContextReadUnit is a separate gated cross-attention module:

q = Wq(h_last), k = Wk(C), v = Wv(C), delta = Wo(attention(q,k,v)), h_last' = h_last + tanh(gate) * delta.

It has 409,601 trainable parameters, hidden/context dimension 320, late placement immediately before final normalization/output, and an independently serializable lifecycle/artifact interface. The gate starts near zero. The Foundation checkpoint is not duplicated or modified.

Micro-overfit and controls

The mechanical gate passed. The causal controls degraded as expected, with no evidence of a prompt or answer bypass.

Sealed single-drone result

The first bounded training run used 2,500 supervised steps, AdamW, learning rate 5e-3, zero weight decay, gradient clipping 1.0, and 128 training identities. The sealed set contained 127 unseen identities (the run stopped at the single-drone gate before structural expansion) and achieved 0/127 exact final answers. Training loss fell from 5.7256 to 0.0075, so this is an identity/generalization failure rather than a no-gradient failure.

Classification: IDENTITY MEMORIZATION / ALIGNMENT OVERFIT.

Structural generalization, Foundation-only/Memory-only source probes, two-drone composition, blind-spot, movement, and full Context behavioral qualification were not started because the single-drone gate failed.

Integrity and cleanup

• Foundation SHA-256 remained the qualified hash: [checksum retained in the private evidence record].
• No Foundation or AdaptiveWeightUnit training occurred.
• Persistent Memory was not modified.
• The rejected reader binary was deleted after its lightweight evidence was persisted.
• Reader evidence remains in [retained internal evidence].

Disposition

CONTEXT READ INTERFACE IMPLEMENTED — QUALIFICATION PENDING

The reader can mechanically overfit a tiny single-drone set and is causally connected, but it does not generalize to unseen arbitrary identities. The directive’s hard stop applies. No structural or multi-drone work should begin until a bounded correction addresses this generalization failure.

Unseen-identity generalization correction

The prior reader consumed one pooled 320-dimensional latent from each drone. The correction preserved ContextDroneWindow.tokens and fed a token-level sequence through the frozen Foundation embedding table for the reader. The legacy pooled vector remains only for the existing bounded residual path. The reader was also tested before the final blocks so frozen Foundation computation could process the injected representation.

Bounded placement comparison, 32-example micro-overfit followed by an immediate 64-identity unseen probe:

The earlier late reader remains the comparison point: 31/32 micro-overfit, 0/127 unseen identities. Token-level representation and earlier placements did not produce a usable generalization signal. The corrective hard stop therefore applies before token-copy, binding, structural, or multi-drone qualification.

The corrected candidates are currently classified as COPY/TRANSPORT FAILURE or OUTPUT DECODING FAILURE; the micro gate prevents a stronger distinction. No Foundation, embedding, AdaptiveWeightUnit, or Memory state changed. Failed reader binaries were deleted; only lightweight JSON evidence remains in [retained internal evidence].

Updated final status: MAJOR ARCHITECTURAL DECISION REQUIRED.

All bounded representation and placement candidates remained at 0% unseen accuracy, and the corrected placements also failed the micro-overfit gate. Further scaling would be blind optimization rather than evidence-based progress. Stop for review before adding a larger bridge, changing the Foundation, or introducing another representation architecture.