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V2T first trains a teacher LVLM using interleaved visual and textual chains of thought, and then uses knowledge distillation to train a student LVLM using the teacher's logits and cross-entropy supervision from ground-truth textual reasoning.
Universal Activation Verbalizer (UAV), a framework that uses a shared decoder to explain activations from heterogeneous donor models, and provides the activation-grounded factual and semantic information needed for faithful explanations.
D3-Gym is introduced, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery, and how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows.
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