Long-form video understanding requires locating sparse, question-relevant evidence in long, multimodal videos. Real-world video distributions differ in modality-specific information density, content structure, and evidence patterns, causing fixed video-agent designs to incur redundant processing or fail when mismatched.
arXiv:2609.38413v1 Announce Type: new Abstract: Vision-language models (VLMs) can answer questions about hour-long videos, but processing every frame is prohibitively expensive, even though the evide...
arXiv:2609.37950v1 Announce Type: new Abstract: Video understanding agents acquire evidence through an executable harness that controls what they observe and how they use those observations. However,...
arXiv:2609.12818v1 Announce Type: new Abstract: Long video understanding often behaves like a visual needle-in-a-haystack problem: query-relevant evidence is sparsely distributed across long temporal...
arXiv:2606.08091v2 Announce Type: replace Abstract: Agentic long video generation requires planning, tool orchestration, and cross-clip coordination over a long horizon. Most existing video agents ei...
arXiv:2608. 12290v1 Announce Type: cross Abstract: Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows.
Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes.
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, sp...
VideoResearcher is a training‑free, multi‑agent framework that autonomously designs, tests, and refines high‑impact tools for long‑video understanding. It operates through dual Solving and Evolving loops, analyzing tool‑use trajectories to identify gaps, coordinating specialized agents to develop and validate executable tools, and reusing evolved tools to improve evidence acquisition in subsequent reasoning. The approach achieves state‑of‑the‑art performance among self‑improving agents and approaches the human‑designed upper bound, demonstrating a paradigm that expands agent capabilities while reducing costly manual engineering.
VideoGen-Agent is a multimodal agent that uses multitask agentic reinforcement learning to coordinate external tools for video generation. It learns to augment, generate, and verify videos through multi‑turn interactions, guided by prompts and intermediate observations. On the new VABench benchmark, the agent improves base text‑to‑video performance by 19.1 points, and further upgrades to generation tools raise the score to 86.1, with human raters favoring the upgraded configuration in 84.3% of comparisons.
VideoEvolve is a framework that automatically evolves agent harnesses for video temporal grounding, a task that localizes events in videos based on natural-language queries. It introduces a Cloze-Structured Harness Representation to keep stage interfaces stable while allowing agent workflows and instructions to change, and uses Branch-Guided Harness Evolution to preserve promising code branches, guide local edits with execution feedback, and validate improvements. Experiments show that this automated evolution improves grounding performance across multiple benchmarks, with instruction refinement consistently yielding gains.
arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget,...
AgentVidBench is a new multi‑hop video question‑answering benchmark designed to evaluate spatial, temporal, and causal reasoning in multimodal large language models (MLLMs). Unlike existing tests that focus on simple scene queries or global summaries, AgentVidBench includes step‑by‑step solution traces to assess whether agents gather the necessary evidence to justify their answers. Experiments with 12 MLLMs show limited single‑turn performance, but integrating these models into agentic workflows improves both accuracy and trajectory scores, establishing AgentVidBench as a comprehensive testbed for future research on agentic video understanding.