Memory that learns from memory Contextually Aware · Luis and Carina An assistant remembers a temporary demo instruction and accidentally replaces the production decision. What would help its next version handle a different exception correctly? Luis and Carina examine a proposed learning loop built around stored decisions, verified corrections, and later outcomes. The episode starts with Luis's documented rule that project facts belong in memory and reusable lessons belong in versioned skills. It then explores a small learned decision layer for deciding whether instructions apply permanently, temporarily, or need clarification. The database scenario is hypothetical. The combined system is a proposed design: no model training, benchmark reproduction, or deployment result is claimed. In this conversation Diagnose retrieval and extraction failures before treating them as model judgment failures. Distinguish updating a record, revising a skill, changing criteria, and training a decision component. Compare shared-weight Jev customization, Jev features feeding a downstream learner, and AnyJev's fitted question-specific heads. Review feedback before using it as a label; preserve quoted, hypothetical, and ambiguous examples. Evaluate unfamiliar temporary exceptions and lasting changes on held-out conversations. Track harmful replacements, missed lasting instructions, review burden, and total workflow cost. Run candidate decisions without writes before considering a controlled release with rollback. Listener question: Which mistake have you corrected twice, and what evidence would distinguish a retrieval failure from a judgment failure? Sources These sources were checked in the accompanying research through September 26, 2026. This adaptation does not add a new research pass or claim independent reproduction. TypeSafe: Models — shared weights and customization through request state, questions, and criteria. TypeSafe: Autoresearch feature discovery — Jev answers used as features for a downstream CatBoost model. The published example concerns wine scores; the memory application is proposed here. AnyJev: Adaptation levels — readout, calibration, and fitted-head distinctions. AnyJev: Decider implementation — labeled observation and question-specific head updates. Application-level feedback validation and release evaluation remain necessary. Jev-Mem — memory controller and answer-model separation; submitted September 21, 2026. Not evidence of continual retraining from user corrections. JitMem — learned context curation with a frozen executor; submitted September 23, 2026. Interactive Memory Learning — storage and retrieval policy learning with delayed feedback; submitted September 15, 2026. Model-judge rewards are a relevant evidence limitation. Production disclosure Scripted with AI assistance and performed using Luis’s approved synthetic voice and the Carina synthetic co-host voice. This is not a recording of a live conversation.
