mixle.reason.design moduleΒΆ

Budgeted selection over cross-modal evidence whose outcomes are already observed.

next_evidence() and this module score the realized evidence produced from stored payloads. That is useful for cost-aware selection from an existing evidence corpus, but it is not pre-acquisition expected information gain: hidden future outcomes require a declared predictive observation model. select_evidence_batch() therefore requires an explicit evidence_is_observed=True assertion and fails closed otherwise.

select_evidence_batch() performs the selection; the returned AcquisitionPlan carries the chosen (index, fidelity, gain, cost) trail, the total nats gained, and the assimilated belief.

class AcquisitionPlan(items=<factory>, total_cost=0.0, total_gain=0.0, belief=None)[source]

Bases: object

A budgeted evidence-acquisition plan: the chosen items, the nats they bought, and the final belief.

Parameters:
property indices: list[int]

Return selected evidence item indices.

select_evidence_batch(store, belief, *, budget, query=None, fine_cost=1.0, coarse_cost=0.2, fidelities=('coarse', 'fine'), candidates=None, max_items=None, min_gain=1e-9, evidence_is_observed=False)[source]

Select already-observed evidence by realized gain under a total budget.

This function evaluates the actual evidence built from each payload, so it is not a pre-acquisition expected-information-gain planner. Callers must explicitly set evidence_is_observed=True to confirm the candidate payloads are already observed and selection does not leak hidden outcomes. A future acquisition requires a predictive observation model.

Parameters:
Return type:

AcquisitionPlan