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cs.AI2026
Dual Exam: Controlled Evaluation of Multimodal Agents and World Models
Zhefan Rao, Meng Chu, Xuan Zhang +80
World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction require…
cs.AI2026
AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
Haotian Luo, Haoyu Wang, Zeyu Qin +5
Recent gains in language model capability have come more from data than from architecture. Frontier labs and data companies produce verifiable agentic tasks, which supervised finet…
cs.CL2026
On Stable Long-Form Generation: Benchmarking and Mitigating Length Volatility
Zhitao He, Haolin Yang, Rui Min +2
Large Language Models (LLMs) excel at long-context understanding but exhibit significant limitations in long-form generation. Existing studies primarily focus on single-generation…