VibeVideoBench is a benchmark that evaluates multimodal agents capable of generating videos by writing code to render each frame and sound. The benchmark contains 239 self‑contained tasks in English and Chinese across 12 categories, requiring agents to produce a finished MP4 with sound within one hour without using any pre‑trained image or video generation model. Evaluation is hierarchical, using program checks, 13k task‑specific rubric checks, and five quality dimensions scored by three VLM judge panels, and it shows that GPT‑6 Astra and Claude Opus 5.5 perform best among nine frontier agents.
Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to on-screen events. A different rout...
arXiv:2610.35864v1 Announce Type: new Abstract: Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a ruleconsistent game or narration sy...
Timeline-Bench is a benchmark comprising 56 real video‑editing tasks that require AI agents to transform raw production material into finished videos. Each task includes a brief, source assets, a container, and a set of tests that assess format, content, brief compliance, and quality based on 2,582 blind judgments by 43 video editors. In evaluations, the best agent resolved only 15 of the 56 tasks, and most failures were due to quality tests rather than technical errors.
VideoWeaver is an agent harness and benchmark for long‑video generation that evaluates and evolves reusable skills. It allows an agent to dynamically compose foundation skills into a workflow based on a single high‑level instruction, rather than following a static pipeline. The benchmark includes 16 task categories and 285 cases with multimodal references, and an evidence‑grounded agent‑as‑judge inspects execution traces and final videos to guide skill evolution, improving both process and output quality.
Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM templates and score them with untrained, generic multimodal models. More fundamentally, their evaluation taxonomies remain rudimentary (overall visual quality, coarse text alignment and temporal smoothness) rather than the professional Cinematic Language criteria by which films are actually made and judged, so they assess basic video plausibility rather than film-grade craft.
arXiv:2607. 24241v1 Announce Type: cross Abstract: Progress in video generation keeps narrowing the visual gap between AI-generated and professionally produced footage, yet most benchmarks still draw prompts from web sources or LLM templates and score them with untrained, generic multimodal models.
arXiv:2609.15478v1 Announce Type: new Abstract: Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmar...
arXiv:2608.21460v1 Announce Type: cross Abstract: Vision Language Models have recently shown improvements in several objective and verifiable domains such as object detection but continue to underper...
arXiv:2605. 14398v3 Announce Type: replace Abstract: Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes distort, and motion loses consistency.
arXiv:2602. 11103v2 Announce Type: replace Abstract: Despite rapid progress on coding agents, progress on their multimodal counterparts has lagged behind.
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.
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.