most citedRAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL2026

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…

cs.AI2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.AI20252 cited

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…