TimelineBench

• Paper: Timeline-Bench: Evaluating Agents on Realistic Video-Editing Tasks, from Raw Footage to Final Cut (arXiv:2609.35143, cs.CV)
• Authors: Gunin Gupta, Nirmit Arora, Pavan Kalyan Tankala
• Project page, tasks, verifier and per-run results: timelinebench.tensortest.com
• Code, Apache-2.0: timelinebench.tensortest.com/code

TimelineBench is a benchmark of 56 real video-editing tasks. Each asks an agent to turn raw production material into a finished video. Every task provides a brief, source assets, a container and a set of tests. A task is resolved when the output passes every test. The tests check the delivery format, the content and the brief's explicit requirements, and include a quality test calibrated on 2,582 blind judgments by 43 video editors.

We evaluate 16 agents that pair frontier models with coding-agent harnesses such as Codex, Claude Code and OpenCode. The best, GPT-6 Astra in Codex with curated editorial guidance, resolves 15 of the 56 tasks (26.8%), and the average agent resolves 14.0%. Human editors prefer the reference edit in 83.5% of judgments. Most unresolved runs (562 of 771) fail only the quality test: agents perceive footage through stills and transcripts and check their renders for defects, not craft.

What is on this page

This page is the Hugging Face home for the paper. Source footage, professional cuts and agent outputs are not distributed here or in the code repository. To have an agent evaluated, use the access form on the project page.

Citation

@article{gupta2026timelinebench,
  title         = {Timeline-Bench: Evaluating Agents on Realistic Video-Editing Tasks, from Raw Footage to Final Cut},
  author        = {Gupta, Gunin and Arora, Nirmit and Tankala, Pavan Kalyan},
  journal       = {arXiv preprint arXiv:2609.35143},
  year          = {2026},
  eprint        = {2609.35143},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}

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