Current paradigms in artificial intelligence concentrate on scaling the capabilities of individual models, yet the collective behaviour of interacting agents is shaped by the topology of their interactions rather than by individual cognition alone. Here we show that the binding constraint on collective behaviour in age...
Shuo Lu, Wei-Cheng Meng, Ai-Jing Yu et al.· 1 citation
Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of individual decisions. Step-level credit assignment provides finer-grained supervision, but its estimates can be unreliable because observed returns also depend on s...
X. Wang, Wen-Hao Wu, Meng-Hao Zhang et al.· 0 citations
The results suggest that the frontier of Physical AI depends not only on stronger action models, but also on executable harnesses that integrate perception, task understanding and reasoning, and action execution into a unified, verifiable, and feedback-driven system.
X. Wang, Wen-Hao Wu, Meng-Hao Zhang et al.· 1 citation
Results show that TRACE converts high model potential into stable, consistent performance gain, and bridge the gap between potential and reliable performance to just 4.0 points.
Wen-Hao Wu, Meng-Hao Zhang, X. Wang et al.· 3 citations
G-ReAct is a reasoning framework for deep search that organizes reasoning as state evolution over a fixed-topology query graph, transforming exploratory search driven by textual history into graph-guided reasoning under explicit constraints.
Shaoxiong Yang, Mengyuan Zhang, Shao-Jun Lin et al.· 0 citations
Experiments on MemFuseBench show that MemFuse achieves the best overall performance among the evaluated memory systems under all three LLM settings and consistently improves performance on questions requiring cross-source evidence fusion.
Chao Li, Yuan-Fang Li, Wenhao Wu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
