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OststaDiff is presented, a decomposed diffusion-policy framework with a lightweight obstacle-aware visual encoder that extracts a structured target-obstacle-background representation, enabling the downstream alignment policy to generate end-effector trajectories toward a target-centered bottleneck pose while reasoning about surrounding obstacles.
Multilingual and multimodal audio understanding is critically under-evaluated, with EXAM$^2$ revealing up to 21.7% performance gaps in current models.
