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Together, the results connect long-context retrieval with representation geometry of counting, supporting a state-tracking account of CoT reasoning.
Multimodal Large Language Models (MLLMs) often struggle with complex mathematical visual reasoning primarily due to a lack of fine-grained perception, causing initial visual hallucinations to directly trigger cascading reasoning failures. In traditional end-to-end reinforcement learning (RL), sparse rewards fail to dec...
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