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arxiv: 2606.05259 · v1 · pith:PNLHBRL5new · submitted 2026-06-03 · 💻 cs.CV

VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding

Lin Fu , Zheyuan Yang , Yang Wang , Tingyu Song , Arman Cohan , Yilun Zhao This is my paper

Pith reviewed 2026-06-28 06:58 UTC · model grok-4.3

classification 💻 cs.CV
keywords video reasoningknowledge-intensive understandingdataset constructionchain-of-thought rationalesbenchmark evaluationpost-traininghuman-in-the-loop generation
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The pith

A new dataset of 315K video reasoning examples improves models on knowledge-intensive tasks while staying competitive on general benchmarks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents VideoKR as a training corpus built from 145K videos and 315K examples to target deeper knowledge and reasoning in video understanding. It uses a human-in-the-loop pipeline to generate chain-of-thought rationales that emphasize progressive skill development and reliability. A companion benchmark, VideoKR-Eval, is designed so that questions cannot be solved through text alone. Experiments under standard post-training show gains on knowledge-heavy video reasoning without loss on general tasks, pointing to data construction as the main lever for progress.

Core claim

Post-training models on VideoKR produces better results on knowledge-intensive video reasoning than prior post-training methods while remaining competitive on general video reasoning, which the authors attribute to the design of the examples and their rationales.

What carries the argument

The human-in-the-loop, skill-oriented example generation pipeline that creates progressively deeper reasoning examples and reliable CoT rationales from expert-domain videos.

If this is right

  • Post-training on VideoKR raises accuracy on knowledge-intensive video reasoning tasks relative to earlier datasets.
  • The same models stay competitive on standard general video reasoning benchmarks.
  • Ablation studies separate the contribution of the new data from other training factors.
  • Data design choices, including skill progression and rationale quality, drive measurable gains in video reasoning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pipeline could be adapted to generate training data for other complex multimodal tasks where shortcuts are common.
  • Scaling the number of videos while preserving the human-in-the-loop checks might further widen the gap on knowledge-heavy benchmarks.
  • Benchmarks like VideoKR-Eval could become standard tests for whether video models truly integrate visual and knowledge sources.

Load-bearing premise

The pipeline ensures that the chain-of-thought rationales demand genuine video understanding and external knowledge instead of allowing models to exploit textual patterns or shortcuts.

What would settle it

Models post-trained on VideoKR would show no advantage on VideoKR-Eval if the videos were replaced by static text descriptions of their content.

Figures

Figures reproduced from arXiv: 2606.05259 by Arman Cohan, Lin Fu, Tingyu Song, Yang Wang, Yilun Zhao, Zheyuan Yang.

Figure 1
Figure 1. Figure 1: An overview of the VideoKR training corpus. All videos are newly collected and CC licensed, and span a wide range of professional domains. We develop a skill oriented QA synthesis pipeline in which every example is grounded in one of three core skills essential for advanced video reasoning, and examples in the CoT subset are further paired with a high quality reasoning trace. 2025; Chen et al., 2025a), and… view at source ↗
Figure 2
Figure 2. Figure 2: (Left) Overview of data construction pipeline. (Right) Statistics of VideoKR-SFT-201K and VideoKR-RL-114K training corpus. et al., 2024b), VSI-Bench (Yang et al., 2025b), and Video￾Vista (Li et al., 2024c) assess perceptual skills, spatiotem￾poral comprehension, and cross-modal reasoning, provid￾ing a solid foundation for evaluating video understand￾ing. Building on this trend, a growing set of knowledge-,… view at source ↗
Figure 3
Figure 3. Figure 3: Inference-time frame scaling results on general and knowledge-intensive video reasoning benchmarks. The figure shows category-wise average accuracies for Qwen2.5-VL-7B￾Instruct and its VideoKR post-trained variant (SFT+RL) under different input frame budgets. Appendix D.1 provides the full per-benchmark results for post-trained Qwen2.5-VL-7B-Instruct and Qwen3-VL-8B-Instruct models. intensive average of Qw… view at source ↗
Figure 4
Figure 4. Figure 4: A VideoKR-SFT-201K example from the natural science domain. The reasoning process is presented in a concise and abbreviated form to improve readability. Q: What device is physically connected to the phone before measurements begin? Answer: External speakers Reasoning: I need to examine the video carefully to identify what device is physically connected to the phone before the measurement phase begins.(...a… view at source ↗
Figure 5
Figure 5. Figure 5: A VideoKR-SFT-201K example from the healthcare domain. The reasoning process is presented in a concise and abbreviated form to improve readability. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A VideoKR-SFT-201K example from the engineering domain. The reasoning process is presented in a concise and abbreviated form to improve readability. Q: In the illustration with a warehouse, how many helmeted figures stand in a row? Answer: D Reasoning: I examine the warehouse illustration with helmeted stick figures in the video(...abbreviated...). This illustration appears under the "Step 1: Define Proble… view at source ↗
Figure 7
Figure 7. Figure 7: A VideoKR-SFT-201K example from the engineering domain. The reasoning process is presented in a concise and abbreviated form to improve readability. Q: At around 03:32, which item is shown immediately after the text sequence ends? Answer: C Reasoning: Let me work through the timeline to understand what happens at the timestamp mentioned in the question.(...abbreviated...)Examining the sequence around that … view at source ↗
Figure 8
Figure 8. Figure 8: A VideoKR-SFT-201K example from the humanities and social science domain. The reasoning process is presented in a concise and abbreviated form to improve readability. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of model responses on a knowledge-intensive video reasoning sample. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of model responses on a knowledge-intensive video reasoning sample. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of model responses on a knowledge-intensive video reasoning sample. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces VideoKR, a 315K-example training corpus over 145K newly collected videos, generated via a human-in-the-loop skill-oriented pipeline that produces CoT rationales, together with the expert-annotated VideoKR-Eval benchmark. Under a fixed SFT→GRPO post-training regime, models trained on VideoKR are reported to outperform prior post-training methods on knowledge-intensive video reasoning while remaining competitive on general video reasoning; comprehensive ablations are said to isolate the contributions of the data design.

Significance. If the central claims are substantiated, the work supplies a large-scale, expert-curated resource and a new benchmark that could serve as a foundation for future video-reasoning research. The emphasis on data curation as a driver of progress, together with the reported ablations, would provide concrete guidance for dataset construction in the field.

major comments (2)
  1. [VideoKR-Eval and experiments] VideoKR-Eval description and experiments: the attribution of gains to 'genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts' is load-bearing for the headline claim. No text-only baseline accuracies, caption-only results, or quantitative verification that questions cannot be solved from text/priors alone are reported, leaving the assumption that the human-in-the-loop pipeline and expert annotation enforce video necessity unsupported by evidence.
  2. [Experiments] Experiments and ablations: while the abstract states that models 'outperform prior post-training approaches' and that 'comprehensive ablations' were conducted, the provided text supplies neither numerical metrics, specific baseline names and scores, nor details on what was ablated (e.g., data scale, rationale quality, skill categories). This absence prevents assessment of effect sizes and robustness of the central result.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'comprehensive ablations' is used without enumerating the factors varied; a short parenthetical list would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The two major comments highlight important gaps in evidence and reporting that we will address through revisions. We respond point-by-point below.

read point-by-point responses

  1. Referee: [VideoKR-Eval and experiments] VideoKR-Eval description and experiments: the attribution of gains to 'genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts' is load-bearing for the headline claim. No text-only baseline accuracies, caption-only results, or quantitative verification that questions cannot be solved from text/priors alone are reported, leaving the assumption that the human-in-the-loop pipeline and expert annotation enforce video necessity unsupported by evidence.

    Authors: We agree this verification is necessary to support the central claim. In the revised manuscript we will add text-only baseline results (models evaluated on question text alone) and caption-only results on VideoKR-Eval, together with quantitative comparisons showing substantial performance drops when video content is removed. These additions will directly demonstrate that the benchmark questions require video input rather than textual shortcuts or priors. revision: yes

  2. Referee: [Experiments] Experiments and ablations: while the abstract states that models 'outperform prior post-training approaches' and that 'comprehensive ablations' were conducted, the provided text supplies neither numerical metrics, specific baseline names and scores, nor details on what was ablated (e.g., data scale, rationale quality, skill categories). This absence prevents assessment of effect sizes and robustness of the central result.

    Authors: We acknowledge that the main text should contain explicit numerical results, baseline names, scores, and ablation details for transparency. The revised version will expand the experiments section to include these: specific scores against named prior methods, effect sizes, and breakdowns of ablations on data scale, rationale quality, and skill categories. This will allow direct assessment of the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results from new data and standard training

full rationale

The paper introduces a new corpus (315K examples) and benchmark via a described human-in-the-loop pipeline, then reports empirical performance of models post-trained with standard SFT→GRPO on that data versus priors. No equations, fitted parameters, or self-citations reduce the claimed gains to inputs by construction. The VideoKR-Eval design and ablations are methodological choices whose validity is externally testable via model accuracy; they do not tautologically define the outcome. This matches the default expectation for data-centric papers with independent evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work depends on the assumption that human annotators can reliably produce reasoning chains that genuinely require video input; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Human annotators in the loop produce accurate and video-dependent chain-of-thought rationales
    The pipeline description in the abstract relies on this to ensure example quality and benchmark validity.

pith-pipeline@v0.9.1-grok · 5707 in / 1246 out tokens · 23465 ms · 2026-06-28T06:58:34.323831+00:00 · methodology

discussion (0)

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Reference graph

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    as our reinforcement learning algorithm. Following the standard RLVR-style reward formulation, the total reward is defined as R= 0.1·R f + 0.9·R a, where Rf and Ra denote theformatandaccuracyrewards, respectively. Specifically, Rf is set to 1.0 if the model output strictly satisfies the required format: <think>...</think><answer>...</answer>. For the accu...

    2024