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20242026
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cs.CL2026

REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning

Zhensheng Jin, Xin Dai, Zhenghao Liu +5

Large Language Models (LLMs) increasingly leverage long-form reasoning to solve complex tasks, yet their reasoning processes can deviate from the provided context when evidence is…

cs.CL2026

SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation

Ruochang Li, Pengcheng Huang, Zhenghao Liu +5

Retrieval-augmented generation (RAG) enhances LLMs by incorporating external knowledge to support response generation. However, conflicts between retrieved context and parametric k…

cs.CL2026

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling

Shuliang Liu, Zhipeng Xu, Zhenghao Liu +6

Large Language Models (LLMs) as automatic evaluators, commonly referred to as LLM-as-a-Judge, have also attracted growing attention. This approach plays a vital role in aligning LL…

cs.CL2026

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization

Haidong Xin, Xinze Li, Zhenghao Liu +6

Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. How…

cs.CL2026

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization

Shaohua Duan, Pengcheng Huang, Xinze Li +7

Long-context modeling is critical for a wide range of real-world tasks, including long-context question answering, summarization, and complex reasoning tasks. Recent studies have e…

cs.CL2026

Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation

Chunyi Peng, Zhipeng Xu, Zhenghao Liu +7

Multimodal Retrieval-Augmented Generation (MRAG) has shown promise in mitigating hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge. How…