Zhisheng Chen
2026
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
Tingyu Wu | Zhisheng Chen | Ziyan Weng | Shuhe Wang | Shuo Zhang | Sen Hu | Silin Wu | Qizhen Lan | Huacan Wang | Ronghao Chen
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Tingyu Wu | Zhisheng Chen | Ziyan Weng | Shuhe Wang | Shuo Zhang | Sen Hu | Silin Wu | Qizhen Lan | Huacan Wang | Ronghao Chen
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding. We present Knowme-Bench, a publicly releasable benchmark built from long-form autobiographical narratives, where actions, context, and inner thoughts provide dense evidence for inferring stable motivations and decision principles. Knowme-Bench reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions spanning factual recall, subjective state attribution, and principle-level reasoning. Across diverse narrative sources, retrieval-augmented systems mainly improve factual accuracy, while errors persist on temporally grounded explanations and higher-level inferences, highlighting the need for memory mechanisms beyond retrieval.
FlashMem: Distilling Intrinsic Latent Memory via Computation Reuse
Yubo Hou | Zhisheng Chen | Tao Wan | Zengchang Qin
Findings of the Association for Computational Linguistics: ACL 2026
Yubo Hou | Zhisheng Chen | Tao Wan | Zengchang Qin
Findings of the Association for Computational Linguistics: ACL 2026
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.