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cs.AI2026
ChartAnno: Evaluating MLLMs for Chart Annotation Generation
Zhenghan Chen, Zekai Shao, Lidan Tan +10
Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains under…
cs.AI2026
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning
Zhicong Li, Lingjie Jiang, Yulan Hu +7
Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However,…