4 papers
What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems
Chen Huang, Yuhao Wu, Wenxuan Zhang
Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is o…
SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation
Jingxi Qiu, Zeyu Han, Cheng Huang
Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer. We…
Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization
Xueyun Tian, Minghua Ma, Bingbing Xu +6
Supervised fine-tuning (SFT) on chain-of-thought (CoT) trajectories demonstrations is a common approach for enabling reasoning in large language models. Standard practices typicall…
Qwen3 Technical Report
An Yang, Anfeng Li, Baosong Yang +57
In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, a…