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    <abstract>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. Leveraging the property that internal representations uniquely encode input trajectories, FlashMem identifies the last hidden state as a sufficient statistic for the interaction history. This enables a Shared-KV Consolidator to synthesize memory by attending directly to the backbone’s frozen cache, eliminating redundant re-parameterization. Furthermore, a parameter-free Cognitive Monitor leverages attention entropy to adaptively trigger consolidation only when high epistemic uncertainty is detected. Experiments demonstrate that FlashMem matches the performance of heavy baselines while reducing inference latency by 5 times, effectively bridging the gap between efficiency and persistent cognition.</abstract>
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%0 Conference Proceedings
%T FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse
%A Hou, Yubo
%A Chen, Zhisheng
%A Wan, Tao
%A Qin, Zengchang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hou-etal-2026-flashmem
%X 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. Leveraging the property that internal representations uniquely encode input trajectories, FlashMem identifies the last hidden state as a sufficient statistic for the interaction history. This enables a Shared-KV Consolidator to synthesize memory by attending directly to the backbone’s frozen cache, eliminating redundant re-parameterization. Furthermore, a parameter-free Cognitive Monitor leverages attention entropy to adaptively trigger consolidation only when high epistemic uncertainty is detected. Experiments demonstrate that FlashMem matches the performance of heavy baselines while reducing inference latency by 5 times, effectively bridging the gap between efficiency and persistent cognition.
%U https://aclanthology.org/2026.findings-acl.230/
%P 4687-4705

Markdown (Informal)

[FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse](https://aclanthology.org/2026.findings-acl.230/) (Hou et al., Findings 2026)

• FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse (Hou et al., Findings 2026)

ACL

• Yubo Hou, Zhisheng Chen, Tao Wan, and Zengchang Qin. 2026. FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4687–4705, San Diego, California, United States. Association for Computational Linguistics.