VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking
| Authors | Jingyang Lin et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.20185 |
| Download | |
| Categories | cs.CV, cs.AI, cs.CL |
Abstract
Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively parsing the full video. This insight allows the model to use far fewer frames while maintaining, or even improving, its video understanding capability. VideoSeek operates in a think-act-observe loop with a well-designed toolkit for collecting multi-granular video observations. This design enables query-aware exploration over accumulated observations and supports practical video understanding and reasoning. Experiments on four challenging video understanding and reasoning benchmarks demonstrate that VideoSeek achieves strong accuracy while using far fewer frames than prior video agents and standalone LMMs. Notably, VideoSeek achieves a 10.2 absolute points improvement on LVBench over its base model, GPT-5, while using 93% fewer frames. Further analysis highlights the significance of leveraging video logic flow, strong reasoning capability, and the complementary roles of toolkit design.
Engineering Breakdown
Plain English
VideoSeek addresses a key inefficiency in video understanding agents: most systems wastefully process every frame densely sampled from a video, driving up computational costs while sometimes degrading comprehension. The paper presents VideoSeek, which uses video logic flow to actively seek only the frames most critical to answering a query, rather than exhaustively parsing the entire video. This selective seeking approach maintains or even improves video understanding while using far fewer frames, significantly reducing computational overhead. The system operates in a think-act-observe loop with a specialized toolkit for collecting multi-granular observations, enabling query-aware exploration of accumulated video evidence.
Core Technical Contribution
The core novelty is replacing exhaustive frame parsing with active, query-guided video seeking powered by video logic flow reasoning. Rather than treating all frames equally or using fixed sampling strategies, VideoSeek learns to dynamically identify and extract answer-critical evidence based on the specific query and accumulated observations. This is fundamentally different from prior greedy approaches that sample frames uniformly or densely; instead, it makes explicit reasoning steps about where in the video to look next. The think-act-observe loop with a tool-guided toolkit represents a shift from passive frame consumption to active strategic exploration of video content.
How It Works
The system operates through an iterative loop: in the think phase, the agent reasons about what information is needed to answer the query based on what it has already observed; in the act phase, it selects which seeking tool to use and where to look in the video (e.g., jump to specific timestamps, seek forward/backward, sample around interesting regions); in the observe phase, it extracts frames and information from the selected regions and updates its accumulated knowledge. The toolkit includes multi-granular observation tools—likely spanning different temporal and spatial resolutions—that allow the agent to gather evidence at appropriate scales depending on the task requirements. The video logic flow component explicitly models temporal relationships and causal structure within videos, guiding the seeking strategy. Critically, the agent learns to stop seeking once it has accumulated sufficient evidence to confidently answer the query, avoiding wasteful processing of irrelevant frames.
Production Impact
For production video understanding systems, this approach directly reduces computational cost and latency: instead of decoding and processing potentially thousands of frames, the system may process only dozens, cutting memory and GPU requirements substantially. This enables real-time or near-real-time processing of long-form videos (hours of content) on commodity hardware where dense sampling would be infeasible. Integration requires: (1) instrumenting your inference pipeline to support dynamic frame seeking rather than pre-computed frame embeddings, (2) maintaining a video index that enables efficient random access, and (3) implementing the think-act-observe control loop in your inference engine. The trade-off is that you lose the ability to precompute all frame embeddings offline—inference becomes stateful and iterative. However, the improvement in video understanding capability (mentioned in the abstract) suggests you gain accuracy gains that offset the loss of batch-processing simplicity.
Limitations and When Not to Use This
The paper's evaluation is incomplete in the abstract—it mentions experiments on four datasets but results are not provided, so real-world performance gains are unclear. The approach assumes videos have extractable logical structure and that seeking strategies can be learned from the training distribution; this may fail on highly edited videos, very long sequences where temporal structure is weak, or on novel video domains without sufficient training data. The think-act-observe loop introduces sequential decision points that could compound errors—if early seeking decisions are wrong, the agent wastes frames or misses critical evidence. There is also no discussion of how the method handles videos with multiple simultaneous activities or complex temporal relationships, suggesting current limitations on video complexity.
Research Context
This work builds on recent advances in agentic video understanding models and tool-use paradigms in large language and vision-language models. It directly responds to computational bottlenecks identified in prior dense-frame-based video agents, making it an efficiency-focused contribution to the video understanding literature. The paper likely benchmarks against prior video question-answering and video reasoning tasks, extending the capability-efficiency frontier beyond existing approaches. The research opens directions in learned video navigation, dynamic sampling strategies, and combining reasoning with efficient multimodal retrieval in video understanding.
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