UniMem Routes Novel Tasks to Episodic Buffers and Recurring Patterns Into Parametric Memory, Gaining 4.0 EM Points Without Task Labels

arXiv 2607.26017·medium signalXBlueskyLinkedInCopy link

UniMem (arXiv 2607.26017, 2026-07-28) attacks the stability-plasticity dilemma agents hit on boundary-agnostic task streams: retrieval memory absorbs new evidence fast but never internalizes recurring execution patterns and pays retrieval overhead at inference, while parametric memory is stable but usually needs explicit task boundaries and fixed parameter budgets. The framework uses learnable routing tokens as memory controllers, keeping novel or sparse tasks in an episodic buffer for retrieval-augmented execution while consolidating recurring, reliable patterns into expandable parametric memory blocks. Decoupling task identification from execution lets memory expand on demand without deployment-time task labels or uncontrolled parameter growth, yielding an average 4.0 EM-point gain across three backbone models on long-horizon streaming sequences.