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University of North Carolina at Chapel Hill, Chapel Hill, USA
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Findings position LLMs as effective late-fusion mechanisms for multimodal learning analytics and demonstrate the viability of LLM-as-a-Judge for scalable, human-in-the-loop evaluation.
Achieve state-of-the-art video reasoning by using visual prompting during training to guide reinforcement learning, then distilling this ability into a model that performs grounded reasoning on raw videos without prompts at inference time.
