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cs.CV2026
Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains
Jiawei Guo, Donglei Yu, Yu Chen +6
Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interpretation can be premature: a t…
cs.AI2026
JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Xinjie Chen, Biao Fu, Jing Wu +4
Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefull…