arXiv Computer Vision
StreamVibe: Proactive Video Agents for Evolving User Intent
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arXiv AI
Jun 17
LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams
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.
arXiv AI
Jul 15
Action-Aware Generative Sequence Modeling for Short Video Recommendation
arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.
arXiv Machine Learning
Oct 16
From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios
The paper introduces “P…IBench”, a benchmark for proactive video understanding that tests daily perception, risk prediction, and intervention in real‑life streaming scenarios. It evaluates 12 video‑language models, finding that current systems struggle to make well‑calibrated response decisions. The authors also create new proactive training data and train “P…I-Assistant-4B”, which shows uneven improvements across the benchmark’s capabilities.
arXiv AI
Sep 30
Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
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.
arXiv Computer Vision
Oct 2
OneStreamer: Unifying Perception, Memory, and Proactive Response in Streaming Video Interaction
OneStreamer is a streaming video model that jointly learns to record evidence and respond to tasks through a shared proactive generation process. Its Proactive Hierarchical Caption Memory creates time‑grounded local‑detail captions and event summaries, while Proactive State Transition Learning reduces waiting states by supervising all output anchors. The authors also built a large OneStreamer‑1M dataset and show that a 4B model outperforms baselines on eight streaming video benchmarks, with ablations confirming the benefits of generated captions and PSTL.
arXiv AI
Oct 15
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search
arXiv:2610.16927v1 Announce Type: new Abstract: Short-video search requires compact query rewriters that infer personalized intent from brief, ambiguous queries under strict latency constraints. The...