3 papers
cs.AI2026
RewardHarness: Self-Evolving Agentic Post-Training
Yuxuan Zhang, Penghui Du, Bo Li +11
Evaluating instruction-guided image edits requires rewards that reflect subtle human preferences, yet current reward models typically depend on large-scale preference annotation an…
cs.MA2026
Evolving Idea Graphs with Learnable Edits-and-Commits for Multi-Agent Scientific Ideation
Jiangwen Dong, Bo Li, Wanyu Lin
LLM-empowered multi-agent systems offer new potential to accelerate scientific discovery by generating novel research ideas. However, existing methods typically coordinate agents t…
cs.AI2026
Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making
Heyang Ma, Qirui Mi, Qipeng Yang +3
Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While mul…