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cs.CL2026

Rethinking On-policy Optimization for Query Augmentation

Zhichao Xu, Shengyao Zhuang, Xueguang Ma +6

Recent advances in large language models (LLMs) have led to a surge of interest in query augmentation for information retrieval (IR). Two main approaches have emerged. The first pr…

cs.CL2025

DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services

Xiang Li, Huizi Yu, Wenkong Wang +17

Objective: Emergency medical dispatch (EMD) is a high-stakes process challenged by caller distress, ambiguity, and cognitive load. Large Language Models (LLMs) and Multi-Agent Syst…

cs.CL2025

HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs

Ken Deng, Zizheng Zhan, Wen Xiang +25

Large Language Models (LLMs) increasingly rely on Chain-of-Thought (CoT) reasoning to improve accuracy on complex tasks. However, always generating lengthy reasoning traces is inef…

cs.CL2025

MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds

Junxi Wu, Jinpeng Wang, Zheng Liu +4

The rapid advancement of large language models has intensified public concerns about the potential misuse. Therefore, it is important to build trustworthy AI-generated text detecti…

cs.CL2025

QZhou-Embedding Technical Report

Peng Yu, En Xu, Bin Chen +2

We present QZhou-Embedding, a general-purpose contextual text embedding model with exceptional text representation capabilities. Built upon the Qwen2.5-7B-Instruct foundation model…

cs.CL2025

Performance of GPT-5 Frontier Models in Ophthalmology Question Answering

Fares Antaki, David Mikhail, Daniel Milad +11

Large language models (LLMs) such as GPT-5 integrate advanced reasoning capabilities that may improve performance on complex medical question-answering tasks. For this latest gener…