arXiv Computer Vision
From Intent to Evidence: Policy-Steered Multi-Strategy Retrieval for Long-Video Agents
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arXiv Machine Learning
Aug 28
Finding the Right Evidence: Factor-Guided Coarse-to-Fine Reasoning for Long Videos
The paper introduces PACE, a factor-guided, progressive framework for acquiring evidence in long-video question answering. PACE first indexes clip-level descriptions using question-derived factors, then refines evidence retrieval with contrastive cues derived from candidate answers. On the MMR‑V dataset, PACE achieves 42.6% accuracy and recovers 66.9% of annotated cues, outperforming direct inference and prior agentic baselines, and shows consistent improvements across several long-video benchmarks.
arXiv Computer Vision
Aug 24
Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
arXiv:2608.20805v1 Announce Type: new Abstract: Long-form video understanding remains challenging for video agents due to the mismatch between query demands and evidence acquisition strategies. Altho...
arXiv Computer Vision
4d ago
Beyond Frame Selection: Rethinking Long-Video Understanding with MLLMs
arXiv:2608.05592v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have made strong progress in video understanding, yet long videos remain difficult: the visual token budge...
arXiv AI
Jun 6
Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding
arXiv:2512. 05774v2 Announce Type: replace-cross Abstract: Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrelevant content.
arXiv Computation and Language
4d ago
VLX-VR: An Agentic-Aware Video Reasoning Model
arXiv:2609.09985v1 Announce Type: new Abstract: Real-world video understanding requires integrating visual, audio, textual, and temporal evidence distributed across a video. Yet many pipelines use a...
arXiv Machine Learning
Aug 11
LAVE: Latent Visual Evidence-Enhanced Planning for Video Tool-use Agents
arXiv:2608. 07585v1 Announce Type: cross Abstract: Long-video understanding requires models to efficiently acquire and reuse sparse visual evidence from long and redundant video streams.