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arxiv: 2605.27705 · v1 · pith:HR4T3B42new · submitted 2026-05-26 · 💻 cs.CR · cs.MM

AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?

Zongheng Cao , Yi Zheng , Rui Song , Xinyu Hu This is my paper

Pith reviewed 2026-06-29 16:35 UTC · model grok-4.3

classification 💻 cs.CR cs.MM
keywords AI agentsvideo post-productionbenchmarkmultimodal modelstool uselong-horizon planningevaluation harness
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The pith

The strongest AI agent stack completes barely 30% of real-world video post-production tasks, far below human experts.

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

The paper presents AgenticVBench, a collection of 100 tasks drawn from actual post-production workflows supplied by 20 industry experts with an average of six years of experience. These tasks cover four families that together test multimodal understanding, long-horizon planning, and tool use across text, image, audio, and video. Frontier vision-language models paired with both vendor and open-source harnesses are evaluated using a mix of programmatic checks and expert rubrics. The top-performing stack reaches just over 30 percent success, while human experts perform substantially better on the identical set. The work also shows that harness choice alters model scores, tool-use patterns, and the kinds of failures that occur.

Core claim

AgenticVBench supplies 100 tasks across four families that mirror real video post-production workflows contributed by twenty practicing experts. When frontier vision-language models are run through vendor-native and open-source harnesses, the best combination succeeds on barely 30 percent of the tasks. Human experts achieve markedly higher performance on the same tasks. Harness selection measurably changes scores, tool-use statistics, and failure modes.

What carries the argument

AgenticVBench, a benchmark of 100 tasks paired with programmatic verifiers and expert rubrics that together measure composite multimodal capabilities and long-horizon tool use.

If this is right

  • Harness choice affects not only overall scores but also the specific tool-use patterns and failure modes observed in multimodal agents.
  • Current frontier models and harnesses require substantial gains before they can reliably handle the composite demands of video post-production.
  • The benchmark supplies concrete diagnostics that can guide targeted improvements to both models and harnesses.
  • AgenticVBench supplies a reusable foundation for measuring progress on agentic capabilities in video production.

Where Pith is reading between the lines

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

  • If the tasks capture typical production demands, practical deployment of AI agents in post-production will remain limited until success rates rise substantially.
  • The observed sensitivity to harness suggests that engineering the interface layer may be as important as scaling the underlying models.
  • The four task families could serve as a template for constructing similar benchmarks in other multimodal creative domains such as audio or 3D asset pipelines.

Load-bearing premise

The 100 tasks drawn from workflows contributed by twenty industry experts form a representative sample of real-world post-production demands.

What would settle it

An independent replication in which the same tasks are shown to omit central post-production operations or in which any agent stack exceeds 70 percent success on the full set would undermine the reported performance gap.

Figures

Figures reproduced from arXiv: 2605.27705 by Rui Song, Xinyu Hu, Yi Zheng, Zongheng Cao.

Figure 1
Figure 1. Figure 1: Overview of the four task families in AgenticVBench. Assembly evaluates storyboard [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Mean score per (model, harness) combination on four video production task families. Red [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Behavioral signature per (model, harness) combination. X: median tool calls per rollout. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two harness effects on a fixed model. (a) GPT-5.5 across three harnesses (Codex, OpenCode, OpenClaw) on the four task families: within-model harness spread ∆ reaches 0.20 on Assembly, comparable to the gap between adjacent models on the leaderboard. (b) Same model (Qwen3-VL-235B-A22B-Instruct) on Assembly: 0.009 on OpenCode versus 0.073 on OpenClaw. The eight-fold gap on a fixed model points to a harness-l… view at source ↗
Figure 5
Figure 5. Figure 5: Agreement rates for subjective Repurpose rubric items on the accepted 36-task split after [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
read the original abstract

Video production workflows offer a rich and demanding arena for evaluating multimodal AI agents: they require composite capabilities across text, image, audio, and video understanding, along with long-horizon planning, and tool use. To this end, we introduce AgenticVBench, a benchmark of 100 agentic tasks across 4 task families spanning the real world post-production workflow, constructed from real production workflows contributed by 20 industry experts averaging 6 years of professional experience. Tasks are paired with evaluation specifications that combine programmatic verifiers and expert rubrics. We evaluate frontier vision-language models (VLMs) with both vendor-native and open-source harnesses. The best evaluated agent stack barely crosses 30%, far below human expert performance on the same tasks. We further find that the choice of harness substantially affects model behavior, including scores, tool-use patterns, and failure modes. AgenticVBench provides a foundation for diagnosing and improving both models and harnesses for agentic video production. Benchmark website: https://agenticvbench.com.

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 AgenticVBench, a benchmark of 100 agentic tasks across 4 task families in real-world video post-production workflows. Tasks are constructed from workflows contributed by 20 industry experts (avg. 6 years experience) and paired with programmatic verifiers plus expert rubrics. Frontier VLMs are evaluated using vendor-native and open-source agent harnesses; the best stack exceeds 30% success but remains far below human expert performance on the same tasks. Harness choice is shown to affect scores, tool-use patterns, and failure modes. The benchmark aims to support diagnosis and improvement of models and harnesses for agentic video tasks.

Significance. If the tasks are shown to be representative, the benchmark would provide a grounded evaluation instrument for multimodal long-horizon agent capabilities in a demanding domain. Strengths include grounding in expert-contributed workflows, dual programmatic/rubric evaluation, and the empirical finding that harness design materially changes agent behavior; these elements could usefully guide future model and framework development.

major comments (2)
  1. [Abstract] Abstract: The headline claim that the best agent stack 'barely crosses 30%' and lies 'far below human expert performance on the same tasks' is load-bearing on the 100 tasks constituting a representative sample of real-world post-production demands. The abstract states construction from workflows by 20 experts across 4 families but supplies no selection criteria, diversity controls (studio size, geography, project scale, tool ecosystems), or post-construction validation against clustering; without these, generalization of the performance gap cannot be assessed.
  2. [Abstract] Abstract: The 30% result and human comparison are presented without task definitions, exclusion criteria, inter-rater reliability statistics for the expert rubrics, or details on how the aggregate score was computed. These protocol elements are required to establish that the evaluation reliably supports the central performance claim.
minor comments (1)
  1. [Abstract] The manuscript would benefit from an explicit table or section cross-reference listing the four task families and the number of tasks per family to improve readability of the benchmark scope.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive comments. We address each major comment below and indicate where revisions to the abstract are feasible.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that the best agent stack 'barely crosses 30%' and lies 'far below human expert performance on the same tasks' is load-bearing on the 100 tasks constituting a representative sample of real-world post-production demands. The abstract states construction from workflows by 20 experts across 4 families but supplies no selection criteria, diversity controls (studio size, geography, project scale, tool ecosystems), or post-construction validation against clustering; without these, generalization of the performance gap cannot be assessed.

    Authors: The abstract is intentionally concise. The full manuscript (Section 3) explains that the 100 tasks were derived directly from workflows contributed by 20 industry experts (average 6 years experience) and organized into four families that cover core post-production stages. No formal selection criteria, geographic or studio-size stratification, or post-construction clustering validation are described because the benchmark prioritizes ecological validity from real contributed workflows over statistical sampling. The performance gap is reported specifically on these tasks rather than as a claim of universal representativeness. We will revise the abstract to explicitly note the expert-contributed origin and the four-family structure. revision: partial

  2. Referee: [Abstract] Abstract: The 30% result and human comparison are presented without task definitions, exclusion criteria, inter-rater reliability statistics for the expert rubrics, or details on how the aggregate score was computed. These protocol elements are required to establish that the evaluation reliably supports the central performance claim.

    Authors: Task definitions, exclusion criteria, and the computation of the aggregate success rate (mean across all 100 tasks) appear in Section 3 and Section 4 of the full manuscript, with additional detail in the appendix. Inter-rater reliability statistics for the rubrics were not collected; rubrics were co-developed with the contributing experts and paired with programmatic verifiers to increase objectivity. We will add a single sentence to the abstract summarizing the dual evaluation approach (programmatic verifiers plus expert rubrics) and the aggregate scoring method. revision: partial

Circularity Check

0 steps flagged

No significant circularity; benchmark is external measurement instrument

full rationale

The paper introduces AgenticVBench as an empirical benchmark of 100 tasks derived from 20 industry experts' workflows, paired with verifiers and rubrics, then reports direct evaluation results (best agent stack ~30%). No equations, fitted parameters, predictions, or derivation chains exist. No self-citations are load-bearing for the central claim. The representativeness assumption is external and falsifiable but does not reduce any result to its own inputs by construction. This matches the default non-circular case for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, invented entities, or detailed axioms visible. The central claim rests on the unexamined premise that expert-contributed tasks are representative.

axioms (1)
  • domain assumption Tasks contributed by 20 industry experts with average 6 years experience accurately capture real post-production workflows.
    The benchmark's validity claim depends on this premise; no independent validation is described in the abstract.

pith-pipeline@v0.9.1-grok · 5713 in / 1191 out tokens · 39653 ms · 2026-06-29T16:35:40.585412+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions

    cs.CL 2026-06 unverdicted novelty 7.0

    EnterpriseClawBench is a benchmark for enterprise agents constructed from proprietary real-world sessions, with the reusable contribution being the construction and evaluation protocol rather than the data itself.

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    no tts" [#2] shell pip install gtts←Google TTS fallback [#3] shell ffprobe film.mp4→6:20, 1920×1080, 24000/1001 fps [#5] shell cat > generate_tts_parts.py parts = [ (

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