activity
20242026
collaborators
Showing cs.CLShow all

8 papers · 1 filter

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

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

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.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

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

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…

cs.CL2024

Commonsense Knowledge Editing Based on Free-Text in LLMs

Xiusheng Huang, Yequan Wang, Jun Zhao +1

Knowledge editing technology is crucial for maintaining the accuracy and timeliness of large language models (LLMs) . However, the setting of this task overlooks a significant port…

cs.CL2024

Improving Zero-shot LLM Re-Ranker with Risk Minimization

Xiaowei Yuan, Zhao Yang, Yequan Wang +2

In the Retrieval-Augmented Generation (RAG) system, advanced Large Language Models (LLMs) have emerged as effective Query Likelihood Models (QLMs) in an unsupervised way, which re-…