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A single MLLM can now perform temporal grounding and video QA with significant inference speedups by treating them as structured decision tasks.
Event camera localization accuracy jumps up to 27% by learning correspondence confidence that directly optimizes pose estimation, overcoming depth-bias issues in prior methods.
