sea

name	sea
description	Plan abi SEA (Sparse Evidence Attention) self-learning work — evidence-augmented completion, 8-signal scorer, EMA modulator persistence, and constitution audit. Use when working on ai_learn / complete --learn / evidence recall. Routes to sea-learn-loop, abi-superpower-sea, abi-superpower-constitution, and sea-learning-controller.

sea

Entry point for abi's SEA self-learning loop (src/features/ai/). Routes:

You want to…	Use
Drive abi complete --learn end-to-end	sea-learn-loop
Deep-dive SEA scoring / adaptive modulation	abi-superpower-sea
Constitution audit (observability-only)	abi-superpower-constitution
Toggle SEA in the REPL (/learn)	sea-learning-controller

Facts that constrain any SEA plan

• SEA = evidence-augmented self-learning completion with an 8-signal scorer + budgeted greedy selection. It classifies 7 task types; 3 currently apply non-baseline signal-weight deltas and 4 retain the baseline weights.
• AdaptiveModulator weights (EMA, alpha=0.3, key modulator:weights) persist in WDBX only on the --learn/SEA path. Plain complete re-runs sentiment each turn with no EMA persistence.
• Constitution audit (6 principles) is observability-only, not a gate — sets audit_passed / audit_vetoed / escore, std.log.warns on violation, but complete / run still return the response. Safety+privacy hard-veto only when either < 0.5.
• The live EMA path is completeAdaptive / completeWithStoreAdaptive via runLearnLoop only; there is no separate routeInputAdaptive wrapper.

Honest boundary

No accuracy / energy / learning-gain claims without a repo benchmark.