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:2606. 17798v1 Announce Type: cross Abstract: Despite the remarkable progress of Video Large Language Models (Video-LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long-horizon contextual memory.
The paper introduces Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming video reasoning that maintains compact natural-language memory entries linked to video time ranges. WTI decides whether to answer, continue watching, or recall relevant past intervals for each question, avoiding replay of the full history. The authors build a large dataset, WTI-82K, and a training method, Stream-GDPO, achieving state‑of‑the‑art performance on StreamingBench and OVO-Bench.
The paper introduces TetherMem, a training‑free, query‑aware memory router designed for streaming autoregressive video models that generate long videos in chunks. By separating subject and scene queries and modulating historical access with region‑ and age‑conditioned priors, TetherMem prevents the model from anchoring the scene to stale backgrounds and viewpoints, a problem termed memory‑anchored scene under‑progression. In blinded pairwise evaluations, TetherMem outperforms eight baseline methods in overall quality and scene progression, and on full 30‑second videos it maintains background, viewpoint, and scene changes while preserving subject identity and temporal continuity.
arXiv:2609.28466v1 Announce Type: new Abstract: Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environme...
arXiv:2606. 13035v1 Announce Type: cross Abstract: Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content.
LayerRecall is a memory router for autoregressive video diffusion that selectively retrieves and injects historical key/value states into specific layers of the model, based on the current context. It addresses the problem that existing memory mechanisms expose nonlocal history but do not guarantee effective use, by recognizing that different layers prefer current, recent, or distant context. The method, combined with Cross‑Horizon Prediction Matching, achieves state‑of‑the‑art long‑range consistency on MemoBench and MovieBench while maintaining local continuity and incurring negligible inference overhead.
MemEvo presents an automated approach to discover memory mechanisms for streaming video understanding. It frames memory design as a search over executable programs expressed in a lightweight domain-specific language, and uses a pretrained large language model to generate and refine candidate memory programs. The framework evaluates each candidate deterministically while keeping the underlying vision-language model frozen, ultimately producing a training-free, bounded-memory mechanism that shows strong performance on StreamingBench and OVO-Bench.
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content. However, extending these models to minute-level generation remains challenging: the limited KV-cache budget prevents the model from retaining the full history, while repeatedly conditioning on self-generated frames induces a context distribution shift that accumulates over time, leading to visual artifacts, quality degradation, and temporal drift.
arXiv:2606. 09803v1 Announce Type: cross Abstract: We present \textbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models.
arXiv:2607. 09759v1 Announce Type: cross Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing goal, and recently multimodal agents equipped with long-term memory over video streams have attracted increasing interest.
arXiv:2608. 02392v2 Announce Type: replace-cross Abstract: A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation.