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Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research Reports — arXiv:2610.31158

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31158
License (as archived): CC0-1.0
Archived: 2026-10-25T08:36:44.935Z
SHA-256 of the archived text: 1750628eeddc60c037cc9ea5b1a57c7c7f342b445d00cb343d075facd4d578d0
Stable URL of this page: https://metraipolis.com/documento/dc_a05ea2bf38bf

How to cite

Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research Reports — arXiv:2610.31158 — arXiv (en), https://arxiv.org/abs/2610.31158 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a05ea2bf38bf (Metraipolis archive dc_a05ea2bf38bf, sha256 1750628eeddc60c037cc9ea5b1a57c7c7f342b445d00cb343d075facd4d578d0)

BibTeX

@misc{dca05ea2bf38bf,
  title = {Beyond Watching and Searching: Benchmarking Agents for Video-centered Deep Research Reports — arXiv:2610.31158},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31158},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a05ea2bf38bf; sha256 1750628eeddc60c037cc9ea5b1a57c7c7f342b445d00cb343d075facd4d578d0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31158. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Deep research agents synthesize open-web evidence into long reports, but current benchmarks do not evaluate research centered on a source video. We introduce Video-Centered Deep Research Report Generation. Given a video and user request, an agent identifies research questions, retrieves external multimodal evidence, and produces an interactive report linked to relevant video moments. This task has direct practical value because it helps users locate relevant moments, verify claims, reproduce demonstrated procedures, and make informed decisions from video content. We present a skill-based workflow and OmniDR-Bench for this task. For evaluation, we propose Omni Atomic Rubric Trees (Omni-ARTs). Each tree contains three dimensions, 23 shared directions, and task-specific atomic binary criteria. The directions test audiovisual grounding, multimodal evidence use, and interactive presentation. Audited Rubric Construction (ARC) builds these trees through independent evidence collection, automated checks, behavioral calibration, and iterative human review. OmniDR-Bench contains 50 Chinese and English tasks across 19 realworld subcategories. It includes 6,414 criteria. Across 18 agent configurations, the best system scores 63.3 out of 100. Current agents still need broader content coverage, stronger causal and logical analysis across modalities, and more accurate citation support. We further analyze models, harnesses, skill configurations, video categories, and research trajectories. The code and data are released at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.31158 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T08:31:27.838Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).