AQuA's "self-improvement" updates research state, not the agent LM. What should a local port freeze?

AQuA's preprint uses "recursive self-improvement" for a bounded research loop. It does not say the research-agent LM rewrites its own weights.

The paper separates three objects:

• The language model driving the research agent and the evaluator stay fixed within each part.
• Each part has its own persistent research state. Validated experiments update that state and guide later proposals; Part I and Part II do not share it.
• In Part II, each config diff produces a separately trained hybrid task-model variant. That training is part of the experiment, not an update to the research-agent LM.

This matters if someone ports the harness to a local LM. Inside one AQuA run, later proposals can change because the retained state changed even though the agent LM did not. Across two ports, a score difference could come from the agent model, state history, prompts and tools, evaluator feedback, or the candidate-training path unless those variables are separated.

A useful release would include:

• the exact agent model and quant, plus context and sampling settings;
• the system prompt, tool schema, and initial research state;
• every state update and evaluator return;
• evaluator version, config diffs, and candidate-training settings;
• seeds and per-run traces.

At minimum, the comparison needs three ablation families: swap only the agent LM under the same harness and starting state; keep the LM fixed while resetting or replaying the research state; and keep the LM and state fixed while changing the harness or evaluator contract. For Part II, candidate task-model training needs its own log, or a change in the trained candidate gets folded into a claim about agent-model capability.

The preprint gives the architecture-level separation, but it does not publish a runnable implementation or enough detail for end-to-end reproduction. A local-model port would be a new experiment, not a reported AQuA result.

If only one artifact shipped first, which would make attribution most credible: the state-transition log, the evaluator contract, or a full model-by-harness matrix with repeated seeds?