2 citations · 2 across the 11 of their papers we have counts for
11 papers
Learn from Whoever Is Right: Answer-Verified Multi-Teacher Distillation for Multi-Domain LLMs
Xixiang He, Xingming Li, Baiqi Wu +4
Modern large language models (LLMs) rely on reinforcement learning to build strong capabilities in individual domains, but integrating those capabilities into a single deployable m…
MLLMs Get It Right, Then Get It Wrong: Tracing and Correcting Late-Layer Textual Bias
Xingming Li, Ao Cheng, Qiyao Sun +4
When vision contradicts text, multimodal large language models (MLLMs) consistently favor text, even when images provide clear evidence otherwise. This bias poses risks for applica…
Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation
Xixiang He, Qiyao Sun, Ao Cheng +5
Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieved strong results in improvin…
StemBind: When MLLMs Get Lost Between Rules and Instances in Abstract Visual Reasoning
Xixiang He, Baiqi Wu, Xingming Li +4
Multimodal large language models (MLLMs) often know the rule but pick the wrong answer: on abstract visual reasoning (AVR) tasks, a model can describe what it sees and name the und…
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
Junshu Pan, Panzhong Lu, Yixuan Weng +5
Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasing strain on traditional acade…
Efficient Hallucination Detection: Adaptive Bayesian Estimation of Semantic Entropy with Guided Semantic Exploration
Qiyao Sun, Xingming Li, Xixiang He +5
Large language models (LLMs) have achieved remarkable success in various natural language processing tasks, yet they remain prone to generating factually incorrect outputs known as…
AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
Minjun Zhu, Zhen Lin, Yixuan Weng +6
High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottl…
DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively
Yixuan Weng, Minjun Zhu, Qiujie Xie +4
While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined chal…
REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once
Zhuoshi Pan, Qizhi Pei, Yu Li +5
Recent Large Reasoning Models (LRMs) have achieved remarkable progress on task-specific benchmarks, yet their evaluation methods remain constrained by isolated problem-solving para…
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
Xixiang He, Hao Yu, Qiyao Sun +4
Instruction Fine-Tuning (IFT) is crucial for aligning large language models (LLMs) with human preferences, and selecting a small yet representative subset from massive data signifi…
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
Tiantian Gan, Qiyao Sun
Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)\cite{IntroducingMCP}, due…