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cs.CL2026
DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos
Bomiao Wang, Zekai Shao, Jiexiang Lan +3
While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leaving a critical gap in understa…
cs.AI2026
ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation
Zhenghan Chen, Zekai Shao, Lidan Tan +10
Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer ne…
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,…