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cs.AI2026
Dual-Frontier: When Can an Agent Trust Its World Model?
Huatai Zhu, Qiang Chen, Ziqian Kou +5
Learned world models are becoming essential to general-purpose agents: by predicting action consequences, they support planning and decision-making while reducing reliance on costl…
cs.AI2026
FireWorldBench: Benchmarking Complex Physical World Intelligence through Coupled-Field Fire Dynamics
Qiang Chen, Hao Guo, Huatai Zhu +7
Understanding the physical world requires more than object recognition, scene description, and short-term visual prediction, as real-world physical systems involve multiple continu…
cs.AI2026
Tools as Continuous Flow for Evolving Agentic Reasoning
Tairan Huang, Siyu Shang, Qiang Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a step-wise paradigm that lacks…