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cs.CV2026
Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs
Dingyang Lin, Yingfeng Luo, Chenglong Wang +4
Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answers into errors. We investig…
cs.LG2026
Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning
Yifu Huo, Shunjie Xing, Chenglong Wang +8
Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aim…