AECA — Adaptive Experience Compression

Research into teaching an agent when to keep a raw memory, compress it to a skill, or crystallize it into a rule.

Most agent memory systems operate at one fixed compression level. AECA studies whether a learned policy can adaptively decide, per memory item, whether to keep it raw, compress it into a reusable skill, or crystallize it into a general rule — and rigorously compares that learned policy against a strong heuristic baseline to find out when reinforcement learning actually earns its cost over simpler methods. Schema, migrations, and the episode-ingestion pipeline are built and tested; the comparison study is in progress.