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papersTODAY 04:00 UTC

Circuit-MLLM applies topological logic guidance to circuit schematic reasoning

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.

papersTODAY 04:00 UTC

UniCAR-RL targets fine-grained perception failures in multimodal math reasoning

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.