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Findings show answer-only evaluation obscures important grounding failures and position ANCHOR as a testbed for evaluating accurate and evidence-aligned audio-visual reasoning.
Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physical laws underlying videos, a capability essential for converting videos into executable simulations and for providing structured physical supervision in robotics. Capti...
This work analyzes the decoding trajectories of LLaDA 2.0 and identifies a recurring diffusion confidence trap, which improves LLaDA 2.0 over confidence-based decoding, leading to more reliable mathematical reasoning.
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