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arXiv:2610.34926v1 Announce Type: new Abstract: An action seen once and the same action seen three times receive the same action-recognition label, so that supervision does not require a model to cou...
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.
arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily...
TwinPast is a new benchmark that tests whether streaming video models use historical evidence by presenting pairs of videos that differ only in critical past events while keeping recent context, questions, and options the same. The study evaluates 34 model configurations on 350 human‑reviewed pairs across five historical tasks, finding that most models rely on recent frames or the question alone, with only a few showing improved performance when historical inputs are provided. Even the best streaming system scores only 49.14% PairAcc, far below general‑purpose models, and case studies reveal that models often select states from the wrong time or contradict the recorded event order.
arXiv:2610.19849v1 Announce Type: new Abstract: We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Stream...
arXiv:2608.31005v1 Announce Type: new Abstract: Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurr...