arXiv Computer Vision
MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs
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Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark
arXiv:2609.37938v1 Announce Type: cross Abstract: Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggr...
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R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models
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Does Local Video Understanding Transfer Across Encounters? The EgoGears Benchmark
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Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving
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RESUME: Recurrent State Updates from Motion and Residual Signals for Efficient Video Language Modeling
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arXiv Computation and Language
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Select, Compress, Reinvest: A Controlled Study of Visual-Token Allocation in Long-Video MLLMs
The study investigates how long‑video language models decide which frames to keep, compress, and reuse, testing each decision in isolation across six selection rules, three benchmarks, and two answering models. It finds that selecting frames based on queries yields the biggest performance boost, that halving spatial resolution costs little, and that reallocating saved tokens to more compressed frames can further improve accuracy. The work also highlights the importance of a unified evaluation harness to avoid misleading comparisons.