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Manuscript received April 21, 2026
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Achieve state-of-the-art 6D object pose estimation with 43x speedup by combining mask-aware pose proposals with amodal-driven refinement.
Forget balanced multimodal learning – letting the best modality lead the way actually unlocks better performance.
Solve the cold-start problem in CTR prediction by using multimodal LLMs to generate proxy embeddings that align with existing ID embeddings, enabling effective ranking of new items without usage data.
A confidence-based gating mechanism lets a 14B parameter reward model outperform 70B parameter models, achieving a new accuracy-efficiency Pareto frontier.
