VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
Researchers introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. They develop a human-in-the-loop, skill-oriented example generation pipeline and curate VideoKR-Eval, a new expert-annotated benchmark. Experiments show that models post-trained on VideoKR under a standard SFT→GRPO pipeline outperform prior approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning.
[2606.05259] VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
[Submitted on 3 Jun 2026]
Title:VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
View a PDF of the paper titled VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding, by Lin Fu and 5 other authors
View PDF HTML (experimental)
Abstract:We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFT$\rightarrow$GRPO pipeline, models post-trained on VideoKR outperform prior post-training approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.
Comments: ICML 2026 Spotlight
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.05259 [cs.CV]
(or arXiv:2606.05259v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.05259
arXiv-issued DOI via DataCite
Submission history
From: Lin Fu [view email] [v1] Wed, 3 Jun 2026 16:14:20 UTC (6,730 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding, by Lin Fu and 5 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)