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arxiv: 2605.10574 · v2 · pith:XVWEDMQ3new · submitted 2026-05-11 · 💻 cs.AI

LLM Jaggedness Unlocks Scientific Creativity

Shray Mathur , J. Anibal Boscoboinik , Esther H. R. Tsai , Kevin G. Yager This is my paper

Pith reviewed 2026-05-21 08:13 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM jaggednessscientific creativityidea generationmodel ensemblesSciAidanBenchinference-time computeknowledge pooling
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The pith

LLM jaggedness in scientific idea generation can be harnessed to build ensembles that outperform any single model.

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

Large language models advance unevenly rather than uniformly, producing jagged performance profiles when asked to generate scientific ideas. The work introduces SciAidanBench, which measures creative potential by counting the number of unique coherent ideas a model produces for open-ended scientific questions. Across dozens of models the authors observe this jaggedness at three scales: general versus scientific tasks, prompt to prompt within one model, and across scientific subfields. They then show that the same unevenness supplies complementary strengths that can be combined through inference-time compute, knowledge pooling, and brainstorming. The resulting meta-model ensembles generate more valid ideas than the strongest individual model, turning a seeming limitation into a practical resource for AI-assisted science.

Core claim

Jaggedness appears both across models and inside single models, with high variability across questions and fragmented strengths across domains. This structural feature of current LLM progress can be leveraged via inference-time compute, knowledge pooling, and brainstorming to construct meta-model ensembles that outperform any single model on the benchmark.

What carries the argument

Jagged capability profiles of LLMs, where performance varies unevenly across tasks, prompts, and scientific domains and is then combined in meta-model ensembles.

Load-bearing premise

The total number of unique valid ideas generated serves as a reliable proxy for scientific creative potential.

What would settle it

A follow-up evaluation in which the proposed ensembles produce no more valid ideas than the single best model on a fresh set of scientific questions.

Figures

Figures reproduced from arXiv: 2605.10574 by Esther H. R. Tsai, J. Anibal Boscoboinik, Kevin G. Yager, Shray Mathur.

Figure 1
Figure 1. Figure 1: Average responses per question on general creativity (AidanBench, [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Response count ranges across models on SciAidanBench. Each row [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Probability density of response counts for three models of increasing [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Domain-level response profiles for the top five distinct models on [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relationship between average reasoning token usage and SciAidan [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Domain-level performance across SciAidanBench subfields for the [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
read the original abstract

As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing unevenly across tasks, domains, and model scales. In this work, we examine this dynamic jaggedness through the lens of scientific idea generation. We introduce SciAidanBench, a benchmark of open-ended scientific questions designed to measure the scientific creativity of large language models (LLMs). Given a scientific question, models are asked to generate as many unique and coherent ideas as possible, with the total number of valid responses serving as a proxy for creative potential. Evaluating 19 base models across 8 providers (30 total variants including reasoning versions), we find that jaggedness manifests both across models and within models. First, in a cross-task comparison between general and scientific creativity, improvements in general creativity do not translate uniformly to scientific creativity, revealing divergent capability profiles across models. Second, at the prompt level, stronger models do not improve uniformly; instead, they exhibit high variability, with bursts of creativity on some questions and limited performance on others. Third, at the domain level, individual models display uneven strengths across scientific subfields, reflecting fragmented internal capability profiles. Finally, we show that this jaggedness can be harnessed. We explore mechanisms of inference-time compute, knowledge pooling, and brainstorming to combine models effectively and construct meta-model ensembles that outperform any single model. Our results position jaggedness not as a limitation, but as a resource, a structural feature of AI progress that, when understood and leveraged, can amplify LLM-driven scientific creativity.

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 manuscript introduces SciAidanBench, a benchmark for assessing scientific creativity in LLMs by prompting models to generate as many unique and coherent ideas as possible for open-ended scientific questions, using the count of valid responses as a proxy for creative potential. It evaluates 19 base models across 8 providers (including 30 variants), identifies jaggedness in capabilities across tasks (general vs scientific), prompt levels, and domains, and proposes harnessing this through meta-model ensembles via inference-time compute, knowledge pooling, and brainstorming, claiming these outperform single models.

Significance. Should the count of valid ideas prove to be a reliable indicator of scientific creativity, this work would demonstrate that non-uniform progress in LLMs can be leveraged through ensemble methods to enhance idea generation in science. The broad evaluation across multiple models and the exploration of combination strategies could inform future approaches to AI-supported scientific discovery by emphasizing diversity over monolithic scaling.

major comments (2)
  1. The abstract describes the evaluation of 19 models and ensemble methods but provides no details on the criteria for determining 'valid' responses, inter-annotator agreement, statistical tests for the observed patterns, or controls for prompt sensitivity. This is a load-bearing issue because the jaggedness findings and the superiority of ensembles are all quantified using this unelaborated proxy.
  2. The central assumption that the total number of valid responses serves as a proxy for creative potential lacks any reported correlation to external measures such as expert-assessed novelty, feasibility, or impact. If higher counts primarily capture verbosity or tolerance for repetition rather than deeper insight, the claims about harnessing jaggedness for scientific creativity do not follow from the presented results.
minor comments (1)
  1. Consider adding a sentence on the scale of SciAidanBench (e.g., number of questions or domains covered) to provide immediate context for the evaluation scope.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of our evaluation methodology. We address each major comment below and indicate the revisions we plan to incorporate.

read point-by-point responses

  1. Referee: The abstract describes the evaluation of 19 models and ensemble methods but provides no details on the criteria for determining 'valid' responses, inter-annotator agreement, statistical tests for the observed patterns, or controls for prompt sensitivity. This is a load-bearing issue because the jaggedness findings and the superiority of ensembles are all quantified using this unelaborated proxy.

    Authors: We agree that the abstract should be more self-contained. In the revised version we will expand the abstract to briefly state the validity criteria (unique and coherent ideas, per the benchmark definition in Section 3), note that responses were annotated by multiple raters with inter-annotator agreement reported in the main text, mention that statistical tests were applied to the observed jaggedness patterns, and indicate that prompt-sensitivity controls were included in the experimental design. These elements already exist in the body of the paper; the revision will surface them in the abstract as well. revision: yes

  2. Referee: The central assumption that the total number of valid responses serves as a proxy for creative potential lacks any reported correlation to external measures such as expert-assessed novelty, feasibility, or impact. If higher counts primarily capture verbosity or tolerance for repetition rather than deeper insight, the claims about harnessing jaggedness for scientific creativity do not follow from the presented results.

    Authors: We acknowledge the value of external validation. The benchmark definition already requires both uniqueness and coherence precisely to limit the influence of verbosity or repetition; these constraints are detailed in Section 3. Nevertheless, we accept that an explicit correlation study with expert ratings on novelty and feasibility would strengthen the proxy interpretation. We will therefore add a targeted validation experiment (or, if space is limited, a clear limitations paragraph) that reports such correlations on a sampled subset of ideas. Until that analysis is complete we will also revise the language to emphasize that the count serves as a proxy under the stated constraints rather than a direct measure of insight. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark with independent measurements

full rationale

The paper is an empirical study that defines SciAidanBench, instructs models to generate ideas, counts valid responses as a proxy metric, evaluates 19 models across tasks/domains, and reports observed performance differences plus ensemble gains. No equations, derivations, or first-principles claims exist that reduce by construction to fitted parameters, self-definitions, or self-citations. The proxy is an explicit operational choice whose validity can be assessed externally; the reported patterns and ensemble results are direct measurements against that metric rather than any loop that forces the outcome from the inputs. This is a standard benchmark evaluation with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claims rest on empirical measurement choices and the assumption that idea count proxies creativity; no new theoretical entities or derivations are introduced.

free parameters (2)
  • Model selection and variants
    Choice of which 19 base models and 30 total variants to evaluate is a selection decision that affects reported jaggedness patterns.
  • Validity and uniqueness criteria
    Rules for counting an idea as valid and unique are not detailed in the abstract yet determine the creativity scores.
axioms (1)
  • domain assumption The total number of unique coherent ideas generated serves as a valid proxy for scientific creative potential.
    Explicitly stated in the abstract as the measurement basis for the benchmark.

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Reference graph

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