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 descriptions, verbal reflections, and experience-derived rules, while the model's own internal representational state remains unobserved. Recent...
Bo Lin, He-Jia Geng, Xinyi Xie et al.· 2 citations
The first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation is presented, demonstrating that user feedback prediction is a learnable capability and revealing different aspects that influence user experience.
Mengze Hong, Xia Zeng, Zeyang Lei et al.· arXiv.org· 1 citation
Experiments across diverse models, benchmarks, and agent harnesses show that supervised fine-tuning on SKT-generated trajectories consistently improves skill-use performance, establishing verified data synthesis as an effective and scalable approach for skill-use training.
Zelin Tan, Yi-Qun Zhang, Hao Li et al.· 2 citations
In a multi-day deployment with more than 70 iterations, HoH autonomously develops a first-person-shooter game, featuring a coherent storyline, fully implemented core mechanics, human-playable experience, polished visuals and integrated audio.
Hao Yan, Min-Le Su, Hang-Fan Zhang et al.· 4 citations
This work presents AgentPanel, a multi-agent forum for human--AI collaboration in scientific exploration, a multi-agent forum for human--AI collaboration in scientific exploration that outperforms a centralized multi-agent debate baseline and shows that users value AgentPanel for perspective diversity and exploration s...
Zhi-Yao Cui, Qianyi Wang, Hao Yan et al.· 1 citation
We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, trea...
Zelin Tan, Zhouliang Yu, Bo-Cheng Lin et al.· arXiv.org· 4 citations
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