Meta-harness belief state (harness log)

This describes the optional belief_state object emitted by scripts/meta-harness/evaluate_turn.py next to the existing composite score. It is a deliberate, low-dimensional logging model inspired by Bayesian control ideas in Papamarkou et al., Position: Agentic AI systems should be making Bayes-consistent decisions (SSRN 6143772; stable copy: HAL hal-05480691).

What it is not

• Not production routing for hsm_console, agent-chat, or promoted HsmRunnerConfig (see docs/EVAL_AND_META_HARNESS.md).
• Not a calibrated value-of-information (VoI) calculation for real tool costs.
• Not a claim that the LLM is Bayesian; only that the harness summarizes turn evidence in a conjugate-friendly scalar for offline analysis and smoke regressions.

Model (harness_beta_task_success_v1)

Single Beta pseudo-posterior over a latent “task succeeded well enough for this harness” quantity:

• Online-ish updates: each stream error line adds failure mass; tool_start / tool_end (and sub_agent_spawned) increments tool count and, past soft thresholds, add small failure mass (heavy tool use).
• Terminal update: finalize_response / final_answer / done outcomes, answer length, “no tools” prior, and lack of finalize add success or failure mass.

Fields in JSON:

Field	Meaning
task_success.alpha, task_success.beta	Beta pseudo-counts after the turn.
task_success.posterior_mean	α / (α + β).
task_success.entropy_nats	Bernoulli entropy of the posterior mean (diagnostic).
voi_proxy.extra_tool_nats	Proxy only: entropy × headroom under a soft tool budget (see belief_state.py).
voi_proxy.information_saturated	Heuristic “little left to learn” / extreme-mean flag for quick filtering.

Code

• scripts/meta-harness/belief_state.py — HarnessBeliefV1, BetaBelief, VoI proxy helper.
• scripts/meta-harness/evaluate_turn.py — integrates belief updates in the same pass as score_turn.

Future work (product-side)

A real Bayes-consistent control layer would live next to orchestration (tool choice, budgets, stopping), use explicit utilities and costs, and be validated against telemetry — with the Python harness used only to measure behavior, not to substitute for that layer.