3 citations · 7 across the 15 of their papers we have counts for
18 papers
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…
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…
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…
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…
VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation
Yubo Sun, Chunyi Peng, Yukun Yan +5
Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyo…
KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs
Dingjun Wu, Yukun Yan, Zhenghao Liu +2
Retrieval-Augmented Generation (RAG) improves factual accuracy by grounding responses in external knowledge. However, existing RAG methods either rely solely on text corpora and ne…