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This work proposes IQA-T1, a tool-based visual evidence reasoning framework that augments MLLM reasoning with explicit perceptual observations and constructs Q-Tool, a dataset containing 11k multimodal reasoning chains grounded in tool-generated evidence.
Remember-R1 is proposed, a reinforcement learning framework that mitigates long-context visual forgetting by applying process-level supervision directly on the original reasoning trajectory, demonstrating its effectiveness in mitigating long-context visual forgetting.
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
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