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20242026
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cs.CL2026

MemTools: A Unified Research Framework for Interoperable Agent Memory

Chengfeng Zhao, Jinhui Chen, Sirui Liang +4

While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different…

cs.CL2026

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

Chengfeng Zhao, Yuqiao Tan, Shizhu He +3

While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interact…

cs.CL2026

Break Through the Compression Bottleneck: From Theory to Practice

Xiusheng Huang, Lu Wang, Yequan Wang +2

As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead. Existing compression methods…

cs.CL2026

If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs

Siqi Fan, Xiusheng Huang, Yiqun Yao +6

Large language models (LLMs) can carry out human-like dialogue, but unlike humans, they are stateless due to the superposition property. However, during multi-turn, multi-agent int…

cs.CL2025

Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement

Xiaowei Yuan, Zhao Yang, Ziyang Huang +5

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect context…

cs.CL2025

Capability Localization: Capabilities Can be Localized rather than Individual Knowledge

Xiusheng Huang, Jiaxiang Liu, Yequan Wang +2

Large scale language models have achieved superior performance in tasks related to natural language processing, however, it is still unclear how model parameters affect performance…