collaborators

19 papers

cs.CL2026

ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation

Pengcheng Huang, Zhenghao Liu, Yukun Yan +8

Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence. However, they remain susceptib…

cs.AI2026

Test-Time Deep Thinking to Explore Implicit Rules

Wentong Chen, Xin Cong, Zhong Zhang +8

With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in environments governed by im…

cs.AI2026

SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules

Yuxuan Chen, Changwei Lv, Yunduo Xiao +5

Large Language Models (LLMs) are central to the one-for-all intelligent paradigm, but they face a fundamental challenge when dealing with heterogeneous scientific data such as mole…

q-bio.BM2026

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

Chen Wei, Fanding Xu, Minghao Sun +5

Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design requires models that generate…

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…

cs.CL2026

LLMMapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System

Yu Chao, Siyu Lin, xiaorong wang +7

We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorp…