A new research benchmark called ChartAnno evaluates how well multimodal large language models can generate annotations for charts, a task that helps explain data and highlight key findings in visualizations. The work examines whether these models can automate annotation authoring, which is normally done by hand. It is presented as an arXiv paper revision.
A new arXiv paper introduces UniCAR-RL, a reinforcement learning approach aimed at improving how multimodal large language models handle math problems involving diagrams and figures. The authors argue that weak fine-grained visual perception leads models to hallucinate details early, which then causes errors to compound through the rest of the reasoning chain. The method is framed as improving perception before deeper reasoning steps are attempted.
A new arXiv preprint argues that multi-modal large language models handle circuit schematics poorly because these diagrams pack dense component layouts and connectivity into a single image. The authors propose Circuit-MLLM, which uses topological logic to steer visual reasoning within the model's latent space rather than relying on surface-level image features. The work is cross-listed under arXiv's cs.AI and cs.LG categories.