AIHOT 于 2026-08-17 收录了“StreamOPD：面向流式视频理解的后训练方案与时空线索门控”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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[2608.16320] StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Computer Science > Computer Vision and Pattern Recognition
arXiv:2608.16320 (cs)
-
[Submitted on 17 Aug 2026]
Title:StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Authors:Keming Wu, Baoyi Wang, Kaichen Zhang, Xiang An, Zuhao Yang, Sudong Wang, Haowei Zhu, Tingxuan Huang, Hongcheng Gao, Bin Wang
View a PDF of the paper titled StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding, by Keming Wu and 9 other authors
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HTML (experimental)
Abstract:Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compression, yet a training-free sliding-window baseline already matches them. We therefore fix a memory-free recent-window protocol and ask how far post-training alone can go. Reinforcement learning with verifiable rewards fits this regime poorly, encouraging long ``think-then-answer'' generations, while on-policy distillation (OPD) supplies dense token-level teacher supervision on student trajectories but is stable only when both models train in thinking mode. These observations lead to \textsc{StreamOPD}, a recipe combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment. It raises StreamingBench from $77.9\%$ to $83.9\%$---within $0.3$ points of the 9B teacher---and improves OVO-Bench excluding its hallucination-detection subtask (HLD) by $9.1$ points under unchanged inference. As a teacher-privilege extension, \emph{Spatio-Temporal CueGate (ST-CueGate)} aggregates cue-versus-no-cue teacher likelihood ratios into a group-relative response score that reweights OPD. It reaches $71.9\%$ on OVO-Bench (excluding HLD) and $64.9\%$ on Video-MME, and is the only variant that stays above the base model on all four benchmarks. Replacing the teacher with a frozen copy of the student's initial policy---on-policy self-distillation---retains most of these gains and lifts HLD to $57.0\%$, above both the untrained student and the 9B teacher, so abstention loss is not intrinsic to the recipe. We provide a transparent and reproducible reference for open-source streaming-video research.
Comments:
Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16320 [cs.CV]
(or
arXiv:2608.16320v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16320
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Keming Wu [view email]
[v1]
Mon, 17 Aug 2026 09:25:36 UTC (7,474 KB)
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View a PDF of the paper titled StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding, by Keming Wu and 9 other authors
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arXiv:2608.16320 (cs)
-
[Submitted on 17 Aug 2026]
Title:StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Authors:Keming Wu, Baoyi Wang, Kaichen Zhang, Xiang An, Zuhao Yang, Sudong Wang, Haowei Zhu, Tingxuan Huang, Hongcheng Gao, Bin Wang
View a PDF of the paper titled StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding, by Keming Wu and 9 other authors
View PDF
HTML (experimental)
Abstract:Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compression, yet a training-free sliding-window baseline already matches them. We therefore fix a memory-free recent-window protocol and ask how far post-training alone can go. Reinforcement learning with verifiable rewards fits this regime poorly, encouraging long ``think-then-answer'' generations, while on-policy distillation (OPD) supplies dense token-level teacher supervision on student trajectories but is stable only when both models train in thinking mode. These observations lead to \textsc{StreamOPD}, a recipe combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment. It raises StreamingBench from $77.9\%$ to $83.9\%$---within $0.3$ points of the 9B teacher---and improves OVO-Bench excluding its hallucination-detection subtask (HLD) by $9.1$ points under unchanged inference. As a teacher-privilege extension, \emph{Spatio-Temporal CueGate (ST-CueGate)} aggregates cue-versus-no-cue teacher likelihood ratios into a group-relative response score that reweights OPD. It reaches $71.9\%$ on OVO-Bench (excluding HLD) and $64.9\%$ on Video-MME, and is the only variant that stays above the base model on all four benchmarks. Replacing the teacher with a frozen copy of the student's initial policy---on-policy self-distillation---retains most of these gains and lifts HLD to $57.0\%$, above both the untrained student and the 9B teacher, so abstention loss is not intrinsic to the recipe. We provide a transparent and reproducible reference for open-source streaming-video research.
Comments:
Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16320 [cs.CV]
(or
arXiv:2608.16320v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16320
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Keming Wu [view email]
[v1]
Mon, 17 Aug 2026 09:25:36 UTC (7,474 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding, by Keming Wu and 9 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.CV
< prev
|
next >
new
|
recent
| 2026-08
Change to browse by:
cs
References & Citations
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- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
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Bibliographic and Citation Tools
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Connected Papers (What is Connected Papers?)
Litmaps Toggle
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Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
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DagsHub Toggle
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Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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AIHOT 摘要
StreamOPD 提出免推理时记忆的后训练方案，将 StreamingBench 从 77.9% 提升至 83.9%，距 9B 教师模型仅差 0.3 个百分点。其扩展模块 ST-CueGate 在 OVO-Bench（排除幻觉检测子任务）和 Video-MME 上分别达 71.9% 和 64.9%，是唯一在所有四项基准上均高于基础模型的变体。
为什么值得关注
流媒体视频理解常依赖推理时记忆或检索，该配方把提升转移到后训练阶段，部署时保持原推理开销，对延迟敏感场景更有参考价值。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：流媒体视频理解常依赖推理时记忆或检索，该配方把提升转移到后训练阶段，部署时保持原推理开销，对延迟敏感场景更有参考价值。
- AIHOT 评分：52；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。