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20242026
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cs.CL2026

Bridging the Agent-World Gap: Text World Models for LLM-based Agents

Yixia Li, Hongru Wang, Peng Lai +13

Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet m…

cs.CL2026

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration

Zijun Liu, Zhennan Wan, Peng Li +3

With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large quantity of retrieved knowledge t…

cs.CL2026

Beyond Words: Evaluating and Bridging Epistemic Divergence in User-Agent Interaction via Theory of Mind

Minyuan Ruan, Ziyue Wang, Kaiming Liu +3

Large Language Models (LLMs) have developed rapidly and are widely applied to both general-purpose and professional tasks to assist human users. However, they still struggle to com…

cs.CL2025

Thinking with Visual Abstract: Enhancing Multimodal Reasoning via Visual Abstraction

Dairu Liu, Ziyue Wang, Minyuan Ruan +4

Images usually convey richer detail than text, but often include redundant information, which potentially downgrades multimodal reasoning performance. When faced with lengthy or co…

cs.CL2025

MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models

Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai +8

Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks. MLLMs have shown promising capability in aligning visual and…