MAA cuts token use ~75% while beating prior methods on 14 of 16 settings for self-improving memory.

Marginal Advantage Accumulation fixes contradictory cross-batch feedback in trace distillation by building differential signals, accumulating per-operation evidence via EMA, and merging semantic identities for traceability (arXiv:2606.20475). For anyone building agents that learn from their own run history, this is the rare paper that improves quality and slashes cost at once. Worth a close read if your agent's memory layer is getting expensive.