21 — Cortex-routed frozen specialist organs: mouth, hands, and shared state

Pre-registered 2026-07-09 (human-authorized before implementation). Agents running this experiment MUST NOT edit this spec. Deviations are reported as deviations. Depends on Research/20 accepting H-META-CORTEX on at least one task family. If Research/20 refutes, this project is BLOCKED rather than a license to hide the failure behind specialist models.

Problem

Experiment/08 asks one small general LM to learn tool-call syntax, tool selection, parameter extraction, goal retention, and result integration at the same time. That confounds cortex learning with whether the language model is a competent action decoder.

The intended architecture does not require one model to be the whole organism. The cortex is the persistent learner and controller. Independently frozen models can be organs:

• a language organ articulates natural-language answers;
• an action organ emits structured tool calls;
• later organs may handle code, vision, or other modalities.

Research/21 isolates whether learned cortex state can select and condition these organs while retaining a task goal across turns. Retrieval remains an external baseline and is disabled in the primary condition.

Hypothesis

H-MULTI-ORGAN: a meta-trained cortex can learn the semantics of previously unseen, opaquely named tools from experience and feedback, preserve the task goal across tool-result turns, route each step to the correct frozen specialist organ, and complete held-out tool chains after raw-trace deletion.

Success must depend causally on cortex state, not tool-name priors, prompt examples, an answer-time exemplar store, or online updates to either specialist model.

Architecture

Frozen organs

1. Language organ: the frozen HF language model used by Research/20.
2. Action organ: a small structured-action model selected before main runs by a capability and latency record. It receives a fixed-width cortex latent plus the current observation and emits a schema-valid action. Its weights remain frozen during developmental and evaluation episodes unless a new human-approved spec explicitly permits joint developmental training.

Both organs expose model hashes and a deterministic greedy mode. If no action model passes the oracle gate, the project is BLOCKED.

Cortex-owned components

• the Research/20 fast and slow state plus learned update/consolidation rules;
• an organ router producing language, action, or abstain;
• one fixed-width latent coupler per organ;
• a recurrent task-goal state that is part of cortex fast state; and
• confidence/uncertainty outputs used for abstention.

The router and couplers are developmentally trained across training tool families, then frozen for meta-test. During a new task only cortex fast/slow state may change.

Tool-family instrument

The instrument is versioned separately from both eval/v2 and Experiment/08. It uses synthetic tools with opaque names such as op_k7 and op_r2; names are randomized independently of semantics so pretrained token priors cannot solve routing.

Each generated tool family specifies:

• JSON schema and parameter types;
• deterministic tool implementation in a sandbox;
• a natural-language teaching interaction with success/correction feedback;
• held-out requests using unseen parameter values;
• ambiguous requests where multiple tools share surface vocabulary;
• two- and three-step chains with tool results; and
• unrelated language-only turns testing abstention and specificity.

Meta-train/validation/test split by complete tool semantics and chains, not names or prompt paraphrases. Tool names are freshly permuted for every task. Generator, scorer, sandbox, seeds, and thresholds receive a versioned manifest and the same DEV distribution-check procedure as Research/20.

Oracle gates

Before cortex evaluation:

1. Action-organ gate: with the correct tool schema, correct route, complete task goal, and one worked example supplied directly, the frozen action organ must produce a schema-valid correct call and parameterization significantly above its no-information baseline.
2. Language-organ gate: with the original goal and complete tool results supplied directly, the frozen language organ must produce the correct final answer significantly above baseline.

A failed gate blocks that family. It is not evidence against cortex memory.

Conditions

C2/C3 isolates online cortex learning. C3/C5 isolates goal state. C3/C6 isolates routing. C1 identifies whether failures belong to an organ or cortex.

Protocol

1. Developmentally meta-train the cortex router, couplers, write rule, and goal state on training tool families; keep all organ weights frozen.
2. Freeze developmental parameters before meta-test.
3. Present one to five teaching interactions for a completely unseen tool family. Apply only the learned cortex update rule.
4. Consolidate and delete teaching text, tool examples, results, and transient traces; verify count zero.
5. Score unseen single-tool requests, ambiguous requests, multi-step chains, post-tool answer integration, and language-only abstention.
6. Replay the scored trajectories under C4–C7 interventions without relearning.
7. Run the existing Pi benchmark only as an external post-verdict battery. It cannot define or reverse the primary verdict.

Primary metrics

1. organ_route_accuracy — correct specialist selected per step.
2. tool_selection_accuracy — correct opaque tool selected.
3. parameter_accuracy — schema-valid parameters with correct values.
4. goal_retention_accuracy — correct next action/final answer after a tool result when the original request is no longer repeated.
5. chain_completion_rate — all required actions and final response correct.
6. feedback_semantics_delta — C3 minus C4.
7. goal_state_delta — C3 minus C5 on post-result turns.
8. routing_delta — C3 minus C6.
9. state_addressing_delta — C3 minus C7.
10. language_specificity_delta — change on language-only turns relative to C2.
11. Persistent bytes, update latency, and per-organ inference latency.

All estimates are reported per tool family and pooled, with independent task counts, seeds, and 95% CIs.

Acceptance

Accept H-MULTI-ORGAN only if:

• C3 beats C2 on tool selection, goal retention, and chain completion with 95% CIs excluding zero;
• C3 beats C4, C5, and C6 on their matched primary metrics;
• correct state beats swapped state;
• specificity remains within the DEV-frozen equivalence margin;
• trace deletion and both organ-hash audits pass; and
• at least one held-out tool family requires a two-step chain.

Refute if both oracle gates pass but any primary condition fails.

Blocked if Research/20 does not accept, an oracle gate fails, or the instrument is not manifest-frozen with human-approved distribution checks.

Interpretation constraints

• Correct tool JSON from the action organ is not by itself cortex learning; C3 must beat C2 and depend on correct feedback.
• Correct routing without post-result goal retention is a router result, not a multi-organ organism result.
• A retrieval baseline win is recorded honestly but cannot accept H-MULTI-ORGAN.
• Tool names, schemas, or demonstrations may not remain in the primary answer path after consolidation.
• Adding another specialist model after seeing meta-test failures requires a new pre-registered spec.

Reporting

Report model-selection and oracle-gate records first, then developmental curves, one-shot meta-test tables, intervention results, deletion/hash audits, resource costs, and the external Pi battery. Log to experiments_logs/<date>_s21_multi_organ_cortex.md.