AI RESEARCH
FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse
arXiv CS.CL
•
ArXi:2601.05505v2 Announce Type: replace The stateless architecture of Large Language Models inherently lacks the mechanism to preserve dynamic context, compelling agents to redundantly reprocess history to maintain long-horizon autonomy. While latent memory offers a solution, current approaches are hindered by architectural segregation, relying on auxiliary encoders that decouple memory from the reasoning backbone. We propose FlashMem, a framework that distills intrinsic memory directly from transient reasoning states via computation reuse.