19 — The LM as language organ: direct cortex learning, two articulation paths

Originally pre-registered 2026-07-03 (human-approved, before implementation). Human-approved amendment 2026-07-09, still before implementation: the original label-prefix articulation path is preserved as Arm A and explicitly classified as parametric retrieval. A matched latent-control path, Arm B, is added as the primary test of whether learned cortex state can control a frozen language organ without retrieving answer content. This amendment does not modify eval/v2; it closes an interpretation loophole in the architecture under test.

Agents running this experiment MUST NOT edit this spec. Deviations are reported as deviations. Research/19 is a direct-online-learning diagnostic. Research/20 is the successor that meta-trains the cortex's update and consolidation rules.

Problem / reframe

Every failed mechanism tried to derive a useful LM control direction directly from an embedding of the correction. Cvec steering was refuted, the layer-L assumption failed on two architectures, and the cortex never received a behavior-aligned training loop. The one component that worked reliably was a retrieval reranker.

The intended architecture is different: the LM is a frozen language organ for perception and articulation, while experience changes a trainable cortex outside it. The first version of this spec conditioned articulation by supplying the cortex's winning label as text. That is a useful comparator, but it can be understood as a classifier retrieving content from its parameters and handing that content to the LM. Deleting raw correction text does not by itself make that path changed dynamics.

Research/19 therefore asks two separate questions on the same learned cortex:

1. Can a small cortex learn a request-to-label mapping that survives raw-trace deletion? This is parametric retrieval (Arm A).
2. Can the same cortex alter a frozen LM through a fixed-width latent interface, with no label or answer text injected at probe time? This is the primary latent-control test (Arm B).

Hypotheses

H-LABEL (comparator): a small head trained online from correction events on frozen LM embeddings maps requests to sense labels, transfers to paraphrases, and retains the mapping after raw-text deletion when its predicted label is supplied to the LM as text.

H-LATENT (primary): using the same perception features, correction events, parameter budget, and teaching order, a cortex with a learned latent articulation coupler produces held-out retention and transfer after raw-trace deletion while:

• the language-organ weights remain bit-identical;
• no predicted label, expected answer, correction text, exemplar, or retrieved content enters the LM prompt at probe time; and
• zeroing or swapping the learned cortex state causally removes or redirects the learned behavior.

A positive H-LABEL result cannot accept H-LATENT.

Architecture (fixed here)

• Frozen language organ: HFDriver with Qwen/Qwen2.5-0.5B-Instruct; model parameters and tokenizer are hashed before and after every run.
• Perception: final-layer, mean-pooled frozen LM features, the S1.4 winner.
• Shared cortex: a trainable head of at most 64k persistent parameters. It receives the request feature and is updated online from the correction the teacher actually supplied. Eval expected strings and scorer outputs are never training inputs.
• Arm A — label_prefix: the head predicts a sense label and that label is supplied as a short text prefix. This preserves the original Research/19 path and is reported as parametric retrieval.
• Arm B — latent_control: a learned cortex-owned coupler maps the query-conditioned cortex state to a fixed-width bank of soft embeddings or KV entries consumed by the frozen LM. The bank contains no decoded text and has a fixed shape independent of episode count. The coupler is trained on DEV, then frozen before any holdout run; its parameters count toward the 64k budget.
• Abstain path: below a DEV-calibrated confidence threshold, both arms fall through to the unmodified language organ.
• No meta-learned update yet: Research/19 uses one fixed optimizer and direct online gradients on the cortex head. Learning the update rule itself is Research/20.

Hard boundary: what Arm B may and may not transmit

Allowed at probe time:

• the current request;
• frozen LM features of that request;
• query-conditioned activations computed from persistent cortex parameters;
• the fixed-width latent control bank.

Banned at probe time:

• correction text or its tokens;
• predicted label text;
• expected-answer text;
• stored exemplars, nearest-neighbor matches, episode IDs, or raw traces;
• a variable-length latent bank that grows with experiences;
• any LM-parameter update.

The runner must emit a machine-checkable articulation audit containing prompt text, latent-bank shape, raw-trace count, language-organ hash, and persistent cortex bytes for every scored condition.

Protocol

1. Instrument: current human-approved frozen eval version at run time (v2.1 as of this amendment), with its manifest verified before and after.
2. Phase 0 distribution check on DEV only: measure no-update repeatability, confidence, and specificity distributions. Freeze the abstain threshold and the specificity equivalence margin in the run manifest before holdout. They require human sign-off and may not be changed after seeing holdout.
3. Coupler development: train Arm B's articulation coupler on stage-0 DEV tasks only. Freeze it before the final teaching/evaluation runs. Arm A's label phrasing is frozen at the same point.
4. Online teaching: initialize a fresh cortex head for each seed; teach stage-0 corrections in seed-shuffled order using the same examples and optimizer for Arms A and B.
5. Consolidation: finish head training, serialize cortex parameters, delete all correction texts and transient optimizer examples, and verify raw-trace count zero.
6. One-shot evaluation: stage-0 holdout retention; the complete, untaught stage-1 transfer battery; stage-2 holdout scope; untaught stages for specificity.
7. Causal intervention: rerun Arm B with learned state active, zeroed, and swapped with a different seed/task state. No relearning occurs between arms.
8. Seeds: at least 5. A fallback to 3 is allowed only if a measured seed exceeds 15 minutes and is reported as a deviation. Vanilla is mandatory.

Conditions / matched comparisons

C2 versus C3 isolates articulation. C3 versus C4 isolates learned cortex state. C3 versus C6 isolates whether semantically correct feedback drives the change.

Primary metrics

All metrics are computed separately for C2 and C3 and reported with mean and 95% CI over seeds.

1. retention_delta — stage-0 holdout accuracy minus C1.
2. transfer_delta — complete untaught stage-1 battery accuracy minus C1.
3. scope_delta — stage-2 holdout accuracy minus C1; reported but non-gating.
4. specificity_delta — change on untaught stages relative to C1.
5. causal_state_delta — C3 active minus C4 zeroed.
6. state_addressing_delta — C3 correct state minus C5 swapped state.
7. feedback_semantics_delta — C3 minus C6.
8. persistent_bytes and behavior_delta_per_byte, with coupler and head bytes both counted.

Acceptance and verdicts

Accept H-LATENT only if all of the following hold:

• C3 retention_delta > 0 with 95% CI excluding zero;
• C3 transfer_delta > 0 with 95% CI excluding zero;
• causal_state_delta > 0 and feedback_semantics_delta > 0, each with 95% CI excluding zero;
• specificity remains within the DEV-frozen equivalence margin;
• raw-trace deletion, fixed latent width, no-text-injection audit, and frozen-LM hash all pass.

Accept H-LABEL separately if C2 passes the original retention, transfer, and specificity criteria. It remains a parametric-retrieval result even if it outperforms C3.

Refute H-LATENT if its validity gates pass but any primary condition fails. If C2 accepts and C3 refutes, record: "a parametric label store works; latent control of the frozen language organ does not yet work."

Blocked if the frozen LM cannot perform the task with an oracle text demonstration on DEV, or if the latent coupler cannot produce coherent DEV articulation before holdout. No cortex-learning verdict is drawn in either case.

Reporting

The report must contain per-seed C0–C7 tables, the Phase 0 distribution record, frozen thresholds, deletion and articulation audits, model hashes, exact commands, and separate verdicts for H-LABEL and H-LATENT. Log to experiments_logs/<date>_s19_language_organ_two_readouts.md.

Relationship to the next projects

• Research/19 asks whether direct gradient training can make a small cortex control one frozen language organ.
• Research/20 learns the cortex update and consolidation rules across task families, then tests adaptation to unseen tasks without online backprop.
• Research/21 adds independently frozen specialist organs and asks whether the learned cortex can route and condition them while retaining task state.