13 papers
Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution
Bohan Lin, Hejia Geng, Xinyi Xie +5
Skill-based LLM agents select reusable procedures from an external library to solve complex tasks, yet their routing decisions rely entirely on text-level signals such as task desc…
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
Zhiyao Cui, Qianyi Wang, Haoyang Yan +26
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a s…
SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation
Zelin Tan, Yiqun Zhang, Hao Li +11
Agent skills have become an important mechanism for equipping language-model agents with reusable procedural knowledge. However, providing skills alone does not guarantee that curr…
UXBench: Benchmarking User Experience in AI Assistants
Mengze Hong, Xia Zeng, Zeyang Lei +26
UXBench is a user‑centric benchmark that uses real interaction logs to evaluate how well AI assistants align with user preferences and generate engaging dialogue, featuring three t…
Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
Lei Bai, Zongsheng Cao, Yang Chen +50
The paper introduces Agents-A1, a 35B mixture-of-experts agent model that attains trillion-parameter-level performance by extending the length of reasoning horizons and integrating…
Self-Harness: Harnesses That Improve Themselves
Hangfan Zhang, Shao Zhang, Kangcong Li +5
The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment. Because different models exhibit d…