AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning, tool use, and verification. Scaling such agentic science remains difficult because workflows are hard to observe and reproduce, many scientific tools and laboratory systems are not agent-ready, and execution trac...
Lin-Feng Zhang, Si-Heng Chen, Yu-Zhu Cai et al.· AI Plus· 0 citations
Recursive self-improvement (RSI) seeks to enable AI systems to participate in improving their own capabilities. A concrete pathway is autonomous model development, where agents iteratively explore post-training strategies to improve a base model. This setting faces two challenges: agents may exploit open-ended experime...
Ya-Xin Du, Xi-Yuan Yang, Zhi-Fan Zhou et al.· 0 citations
Experiments show that joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple perturbation types, with benefits validated on real-robot deployment.
Ye Wang, Peibin Lin, Xiong-Hui Chen et al.· 13 citations· ⚡1
NavMCP is introduced, an agentic scaffolding framework that couples a VLM reasoning agent with an NFM executor for long-horizon exploration that achieves state-of-the-art results on HM-EQA, MT-HM3D, and EXPRESS-Bench.
Zi-Xing Lei, Geng-Ze Zhou, Xiong-Hui Chen et al.· 0 citations
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