pith. sign in

arxiv: 2606.30561 · v1 · pith:GRB7FJQLnew · submitted 2026-06-29 · 💻 cs.AI · cs.CV· cs.HC

The Human Creativity Benchmark

Aspen Hopkins , Allison Nulty , Alexandria Minetti , Anoop Pakki , Angad Singh This is my paper

Pith reviewed 2026-06-30 05:51 UTC · model grok-4.3

classification 💻 cs.AI cs.CVcs.HC
keywords creative AI evaluationprofessional disagreementtaste variationhuman preferencesbenchmark designconvergence divergenceworkflow phasesexpert judgment
0
0 comments X

The pith

Evaluating creative AI requires separating where professionals agree on standards from where their tastes legitimately differ.

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

Modern AI evaluation frameworks treat disagreement among evaluators as noise to be averaged away. In creative domains, however, professional disagreement often reflects genuine differences in taste rather than error. The paper introduces the Human Creativity Benchmark to collect pairwise preferences, scalar ratings, and rationales from domain experts while keeping convergence and divergence signals distinct across five domains and three workflow phases. Data from 15,000 judgments show convergence concentrating on verifiable dimensions such as technical correctness while divergence concentrates on taste-driven dimensions such as aesthetic direction. Collapsing both signals into one score erases the distinction between aspects where models must be reliable and aspects where they should remain adaptable to individual preferences.

Core claim

The paper claims that creative AI evaluation must preserve two distinct signals: convergence, where professionals align around shared best practices, and divergence, where individual taste legitimately varies. The Human Creativity Benchmark operationalizes this separation by collecting pairwise preferences, scalar ratings on prompt adherence, usability, and visual appeal, and qualitative rationales from domain professionals. Across 15,000 professional judgments spanning five creative domains and three workflow phases, convergence concentrates on verifiable dimensions like technical correctness and visual hierarchy, while divergence concentrates on taste-driven dimensions like aesthetic direc

What carries the argument

The Human Creativity Benchmark, which partitions expert judgments into convergence and divergence categories to preserve distinct signals instead of averaging them.

If this is right

  • Models can be assessed for reliability on dimensions where convergence occurs and for adaptability on dimensions where divergence occurs.
  • Evaluation must be performed separately for each workflow phase because performance patterns differ across ideation, mockup, and refinement.
  • Single quality metrics lose the information needed to decide where models should match shared standards and where they should support variation.
  • Benchmark results can guide targeted improvements by identifying specific dimensions and phases where steerability is preferred over correctness.

Where Pith is reading between the lines

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

  • The same separation of signals could be applied to other subjective domains such as text generation or music composition to distinguish objective constraints from stylistic choice.
  • Developers could use divergence data to train models that produce varied outputs rather than converging toward averaged preferences.

Load-bearing premise

The collected pairwise preferences, scalar ratings, and rationales from domain professionals can be partitioned into convergence and divergence categories in a way that reflects genuine taste variation rather than prompt-specific artifacts, domain selection, or rater pool composition.

What would settle it

If re-running the same collection process with altered prompts or a different rater pool produces convergence and divergence partitions that no longer align with the original patterns on verifiable versus taste-driven dimensions, the separation would not hold.

Figures

Figures reproduced from arXiv: 2606.30561 by Alexandria Minetti, Allison Nulty, Angad Singh, Anoop Pakki, Aspen Hopkins.

Figure 1
Figure 1. Figure 1: The creative process as a sideways martini: broad ideation narrows through mockup, ending in refinement. Example [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Inter-rater divergence vs. convergence on a single output. Examples of two model outputs for the same creative stage [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (A) Scalar ratings and pairwise win rates across domains. Each point represents a model within a domain, plotted by its mean scalar quality rating (1–5 scale, horizontal axis) against its pairwise win rate (%, vertical axis) aggregated over all head-to-head comparisons in that domain. The aggregation removes phase- and axis-level differences. (B) Head-to-head pairwise win rates in the Ideation stage for pr… view at source ↗
Figure 4
Figure 4. Figure 4: Scalar performance across the three phases for video generation, comprising one ribbon for each model–metric [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Contra Labs evaluation interface, extended views. (A) Introductory screen for the pairwise comparison task, with [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (A) shows an example output for brand images, along with a sampled quote included as explanation of domain expert [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of respondent sentiment toward AI’s role in the future of creative work (50 responses). Mixed sentiment is [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of the self-reported share of the creative process that is AI-supported (50 responses). The most common [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Image generation tools mentioned in survey responses. Midjourney is cited most frequently at 26 responses, followed [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 14
Figure 14. Figure 14: Mean scalar ratings (1–5) for ad-image deliverables across the three pipeline stages, broken out by evaluation question. [PITH_FULL_IMAGE:figures/full_fig_p016_14.png] view at source ↗
Figure 10
Figure 10. Figure 10: Video generation tools mentioned in survey responses. Runway is the most frequently cited at 9 responses, followed [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 15
Figure 15. Figure 15: Scalar performance across the three phases for ad-image generation, comprising one ribbon for each model–metric [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 11
Figure 11. Figure 11: Audio, voice, and music generation tools mentioned in survey responses. Only two tools were named: ElevenLabs, [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Model win rates across the three ad-image generation stages identified in the paper: Ideation, Mockup, and Refinement. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 23
Figure 23. Figure 23: Example overview of scalar performance for Brand Design content; y-axis marks the stage, while x-axis marks mean [PITH_FULL_IMAGE:figures/full_fig_p019_23.png] view at source ↗
Figure 13
Figure 13. Figure 13: Model win rates across the three ad-image generation stages identified in the paper: Ideation, Mockup, and Refinement. [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 24
Figure 24. Figure 24: Model win rates across the three phases for brand-design generation. GPT-Image-1.5 leads Ideation at 63%, followed [PITH_FULL_IMAGE:figures/full_fig_p020_24.png] view at source ↗
Figure 16
Figure 16. Figure 16: Head-to-head pairwise win rates for ad-image generation in the Ideation stage. Each cell reports the row model’s win [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Head-to-head pairwise win rates for ad-image generation in the Mockup stage. Each cell reports the row model’s win [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 33
Figure 33. Figure 33: Scalar performance across the three phases for landing-page generation, with one ribbon for each model–metric [PITH_FULL_IMAGE:figures/full_fig_p022_33.png] view at source ↗
Figure 18
Figure 18. Figure 18: Head-to-head pairwise win rates for ad-image generation in the Refinement stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 34
Figure 34. Figure 34: Model win rates across the three phases for landing-page generation. Claude-Opus-4.6 leads Ideation at 80%, followed [PITH_FULL_IMAGE:figures/full_fig_p023_34.png] view at source ↗
Figure 19
Figure 19. Figure 19: Overall pairwise win rates across the three pipeline stages for product-video generation. Veo3.1 leads Ideation at [PITH_FULL_IMAGE:figures/full_fig_p024_19.png] view at source ↗
Figure 35
Figure 35. Figure 35: Mean scalar ratings (1–5) for landing-page deliverables across the three pipeline stages, broken out by evaluation [PITH_FULL_IMAGE:figures/full_fig_p024_35.png] view at source ↗
Figure 20
Figure 20. Figure 20: Head-to-head pairwise win rates for product-video generation in the Ideation stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p025_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Head-to-head pairwise win rates for product-video generation in the Mockup stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Head-to-head pairwise win rates for product-video generation in the Refinement stage. Each cell reports the row [PITH_FULL_IMAGE:figures/full_fig_p027_22.png] view at source ↗
Figure 25
Figure 25. Figure 25: Overall pairwise win rates across the three pipeline stages for brand-design generation. GPT-Image-1.5 leads Ideation [PITH_FULL_IMAGE:figures/full_fig_p028_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Head-to-head pairwise win rates for brand-design generation in the Ideation stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p029_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Head-to-head pairwise win rates for brand-design generation in the Mockup stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p030_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Head-to-head pairwise win rates for brand-design generation in the Refinement stage. Each cell reports the row [PITH_FULL_IMAGE:figures/full_fig_p031_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Scalar performance across the three phases for brand-design generation, comprising one ribbon for each model–metric [PITH_FULL_IMAGE:figures/full_fig_p032_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Head-to-head pairwise win rates for desktop-app generation in the Ideation stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p033_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Head-to-head pairwise win rates for desktop-app generation in the Mockup stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p034_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Head-to-head pairwise win rates for desktop-app generation in the Refinement stage. Each cell reports the row [PITH_FULL_IMAGE:figures/full_fig_p035_32.png] view at source ↗
Figure 36
Figure 36. Figure 36: Head-to-head pairwise win rates for landing-page generation in the Ideation stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p036_36.png] view at source ↗
Figure 37
Figure 37. Figure 37: Head-to-head pairwise win rates for landing-page generation in the Mockup stage. Each cell reports the row model’s [PITH_FULL_IMAGE:figures/full_fig_p037_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: Head-to-head pairwise win rates for landing-page generation in the Refinement stage. Each cell reports the row [PITH_FULL_IMAGE:figures/full_fig_p038_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: Scalar performance across the three phases for landing-page generation, comprising one ribbon for each model–metric [PITH_FULL_IMAGE:figures/full_fig_p039_39.png] view at source ↗
Figure 40
Figure 40. Figure 40: Spread of axes agreement across domains. [PITH_FULL_IMAGE:figures/full_fig_p039_40.png] view at source ↗
read the original abstract

Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved. In creative domains, professional disagreement reflects genuine differences in taste, not measurement error. We argue that evaluating creative AI requires preserving two distinct signals: convergence, where professionals align around shared best practices, and divergence, where individual taste legitimately varies. We present the Human Creativity Benchmark (HCB), a benchmark that operationalizes this separation by collecting pairwise preferences, scalar ratings on prompt adherence, usability, and visual appeal, and qualitative rationale from domain professionals. Across 15,000 professional judgments spanning five creative domains and three workflow phases (ideation, mockup, refinement), we find that convergence concentrates on verifiable dimensions like technical correctness and visual hierarchy, while divergence concentrates on taste-driven dimensions like aesthetic direction and conceptual risk. No model excels uniformly across all phases. Collapsing these signals into a single quality metric discards the most actionable information: where models must be correct versus where they should remain steerable.

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 / 0 minor

Summary. The paper argues that creative AI evaluation should preserve two signals—convergence on verifiable dimensions (e.g., technical correctness) and divergence on taste-driven dimensions (e.g., aesthetic direction)—rather than treating professional disagreement as noise to be aggregated into a single metric. It introduces the Human Creativity Benchmark (HCB) operationalized via 15,000 pairwise preferences, scalar ratings (prompt adherence, usability, visual appeal), and rationales collected from domain professionals across five creative domains and three workflow phases (ideation, mockup, refinement). The central empirical claim is that convergence concentrates on verifiable aspects while divergence concentrates on taste aspects, with no model excelling uniformly, implying that single-metric collapse discards actionable information about where models must be correct versus remain steerable.

Significance. If the reported partition between convergence and divergence proves robust, the work supplies a concrete, multi-dimensional evaluation framework that directly addresses a recognized limitation in current creative-AI benchmarks. The emphasis on preserving steerability information rather than forcing consensus is a substantive contribution to evaluation methodology in generative AI.

major comments (2)
  1. [Abstract] Abstract: The manuscript states findings from 15,000 professional judgments yet supplies no methods detail on sampling, exclusion criteria, inter-rater reliability statistics, or raw-data summary. Without these, the claim that convergence concentrates on verifiable dimensions cannot be assessed for robustness or post-hoc selection.
  2. [Abstract] Abstract: The operationalization of partitioning judgments into convergence versus divergence categories is presented without any validation that the observed patterns reflect genuine taste variation rather than prompt artifacts, domain selection, or rater-pool composition; this partition is load-bearing for the central claim that single-metric aggregation discards actionable information.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need for greater methodological transparency in the abstract and explicit validation of the convergence-divergence partition. We address each point below and will revise the manuscript accordingly to strengthen the presentation of the Human Creativity Benchmark.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The manuscript states findings from 15,000 professional judgments yet supplies no methods detail on sampling, exclusion criteria, inter-rater reliability statistics, or raw-data summary. Without these, the claim that convergence concentrates on verifiable dimensions cannot be assessed for robustness or post-hoc selection.

    Authors: We agree that the abstract omits these details. The full manuscript contains a Methods section specifying rater recruitment (domain professionals with minimum experience thresholds recruited through professional networks), exclusion criteria (failed attention checks and insufficient domain expertise), inter-rater reliability (Krippendorff's alpha computed separately for pairwise preferences and scalar ratings), and raw-data summaries (distribution of judgments across domains and phases). To address the concern directly, we will revise the abstract to include a concise methods overview so readers can evaluate robustness without needing the full text. revision: yes

  2. Referee: [Abstract] Abstract: The operationalization of partitioning judgments into convergence versus divergence categories is presented without any validation that the observed patterns reflect genuine taste variation rather than prompt artifacts, domain selection, or rater-pool composition; this partition is load-bearing for the central claim that single-metric aggregation discards actionable information.

    Authors: The partition is operationalized by first identifying dimensions via rater rationales and then measuring agreement: high consensus on verifiable aspects (prompt adherence, technical correctness, visual hierarchy) versus persistent divergence on taste aspects (aesthetic direction, conceptual risk). The multi-domain and multi-phase design provides initial safeguards against single-prompt or single-domain artifacts. We acknowledge that the current manuscript does not include dedicated robustness checks (e.g., prompt perturbation tests or rater demographic subgroup analyses). We will add these validation analyses in a new subsection to confirm the patterns are not artifacts of the experimental setup. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper defines the Human Creativity Benchmark through fresh collection of 15,000 professional pairwise preferences, scalar ratings, and rationales across five domains and three workflow phases. No equations, fitted parameters, predictions, or self-citation chains appear in the supplied text; the separation into convergence (verifiable dimensions) versus divergence (taste dimensions) is an empirical partition of the newly gathered data rather than a reduction to prior inputs by construction. The claim that single-metric aggregation loses actionable information follows directly from the operationalization once the data partition is performed.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on the untested premise that professional judgments can be cleanly partitioned into convergence and divergence without additional modeling assumptions; no free parameters, axioms, or invented entities are introduced in the provided text.

pith-pipeline@v0.9.1-grok · 5698 in / 1186 out tokens · 23115 ms · 2026-06-30T05:51:47.464335+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

30 extracted references

  1. [1]

    Langley , title =

    P. Langley , title =. Proceedings of the 17th International Conference on Machine Learning (ICML 2000) , address =. 2000 , pages =

    2000

  2. [2]

    T. M. Mitchell. The Need for Biases in Learning Generalizations. 1980

    1980

  3. [3]

    M. J. Kearns , title =

  4. [4]

    Machine Learning: An Artificial Intelligence Approach, Vol. I. 1983

    1983

  5. [5]

    R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification. 2000

    2000

  6. [6]

    Suppressed for Anonymity , author=

  7. [7]

    Newell and P

    A. Newell and P. S. Rosenbloom. Mechanisms of Skill Acquisition and the Law of Practice. Cognitive Skills and Their Acquisition. 1981

    1981

  8. [8]

    A. L. Samuel. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 1959

    1959

  9. [9]

    Proceedings of the 1st Workshop on Evaluation and Comparison of

    We Need to Consider Disagreement in Evaluation , author =. Proceedings of the 1st Workshop on Evaluation and Comparison of

  10. [10]

    Advances in Neural Information Processing Systems , volume =

    Picking on the Same Person: Does Algorithmic Monoculture Lead to Outcome Homogenization? , author =. Advances in Neural Information Processing Systems , volume =

  11. [11]

    Sociology , volume =

    Sociology and the Cultural and Creative Industries , author =. Sociology , volume =

  12. [12]

    Philip and Skene, Allan M

    Dawid, A. Philip and Skene, Allan M. , journal =. Maximum Likelihood Estimation of Observer Error-Rates Using the

  13. [13]

    Inel, Oana and Khamkham, Khalifeh and Cristea, Tatiana and Dumitrache, Anca and Rutjes, Heine and Ploeg, Jelle and Romaszko, Lukasz and Aroyo, Lora and Sips, Robert-Jan , booktitle =

  14. [14]

    Intergovernmental Committee for the Protection and Promotion of the Diversity of Cultural Expressions , year =

    Culture, Platforms and Machines: The Impact of Artificial Intelligence on the Diversity of Cultural Expressions , author =. Intergovernmental Committee for the Protection and Promotion of the Diversity of Cultural Expressions , year =

  15. [15]

    Rethinking Creativity: Creative Industries,

    Lee, Hye-Kyung , journal =. Rethinking Creativity: Creative Industries,

  16. [16]

    Journal of Creativity , volume =

    Extending Human Creativity with. Journal of Creativity , volume =

  17. [17]

    Catastrophic Forgetting and Mode Collapse in

    Thanh-Tung, Hoang and Tran, Thanh , booktitle =. Catastrophic Forgetting and Mode Collapse in

  18. [18]

    2024 , howpublished =

    Stitch , author =. 2024 , howpublished =

    2024

  19. [19]

    Why Good Thoughts Block Better Ones: The Mechanism of the Pernicious

    Bilali\'c, Merim and McLeod, Peter and Gobet, Fernand , journal =. Why Good Thoughts Block Better Ones: The Mechanism of the Pernicious

  20. [20]

    Advances in Neural Information Processing Systems , volume =

    Auditing for Human Expertise , author =. Advances in Neural Information Processing Systems , volume =

  21. [21]

    Advances in Neural Information Processing Systems , volume =

    Human Expertise in Algorithmic Prediction , author =. Advances in Neural Information Processing Systems , volume =

  22. [22]

    Human Computation , volume =

    Framing is Mightier than the Sword: Detection of Episodic and Thematic Framing in News Media , author =. Human Computation , volume =. 2024 , doi =

    2024

  23. [23]

    The Journal of Creative Behavior , volume =

    Needed Research on the Measurement of Creativity , author =. The Journal of Creative Behavior , volume =

  24. [24]

    We need to consider disagreement in evaluation

    Valerio Basile, Michael Fell, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, Massimo Poesio, and Anca Uma. We need to consider disagreement in evaluation. In Proceedings of the 1st Workshop on Evaluation and Comparison of NLP Systems , pages 15--21, 2021

    2021

  25. [25]

    Creel, Arvind Kumar, Dan Jurafsky, and Percy Liang

    Rishi Bommasani, Kathleen A. Creel, Arvind Kumar, Dan Jurafsky, and Percy Liang. Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? In Advances in Neural Information Processing Systems, volume 35, pages 3663--3678, 2022

    2022

  26. [26]

    Philip Dawid and Allan M

    A. Philip Dawid and Allan M. Skene. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28 0 (1): 0 20--28, 1979

    1979

  27. [27]

    CrowdTruth : Machine-human computation framework for harnessing disagreement in gathering annotated data

    Oana Inel, Khalifeh Khamkham, Tatiana Cristea, Anca Dumitrache, Heine Rutjes, Jelle Ploeg, Lukasz Romaszko, Lora Aroyo, and Robert-Jan Sips. CrowdTruth : Machine-human computation framework for harnessing disagreement in gathering annotated data. In The Semantic Web -- ISWC 2014 , volume 8797 of Lecture Notes in Computer Science, pages 486--504. Springer, 2014

    2014

  28. [28]

    Rethinking creativity: Creative industries, AI and everyday creativity

    Hye-Kyung Lee. Rethinking creativity: Creative industries, AI and everyday creativity. Media, Culture & Society, 44 0 (3): 0 601--612, 2022

    2022

  29. [29]

    Catastrophic forgetting and mode collapse in GANs

    Hoang Thanh-Tung and Thanh Tran. Catastrophic forgetting and mode collapse in GANs . In 2020 International Joint Conference on Neural Networks ( IJCNN ) , pages 1--10. IEEE, 2020

    2020

  30. [30]

    Treffinger and John P

    Donald J. Treffinger and John P. Poggio. Needed research on the measurement of creativity. The Journal of Creative Behavior, 6 0 (4): 0 263--267, 1972

    1972