arxiv: 2604.03374 · v1 · submitted 2026-04-03 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge

Mete Ismayilzada , Renqing Cuomao , Daniil Yurshevich , Anna Sotnikova , Lonneke van der Plas , Antoine Bosselut

Authors on Pith no claims yet

Pith reviewed 2026-05-13 19:39 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords creative problem-solvingLLM benchmarkreal-world knowledgelateral thinkingcommonsense reasoningperformance gapLLM evaluationanalogy-making

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The pith

LLMs retrieve relevant real-world facts but struggle to form the non-obvious creative connections needed to solve puzzles.

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

The paper presents CresOWLve, a benchmark of puzzles grounded in real-world knowledge that require retrieving facts from diverse domains and combining them creatively. Frontier large language models perform substantially worse on the creative versions than on direct factual questions about the same information, with drops reaching 17 percent. Although models often locate the necessary knowledge, they have difficulty integrating it through non-obvious links that demand lateral thinking or analogy-making. The benchmark uses practical scenarios rather than artificial brainteasers to evaluate how these abilities work together in realistic settings.

Core claim

What carries the argument

The CresOWLve benchmark, consisting of real-world knowledge puzzles that require creative integration of facts retrieved from multiple domains.

If this is right

Knowledge retrieval alone does not enable success on tasks that demand creative synthesis of information.
The observed gap isolates a specific limitation in forming non-obvious integrations across domains.
Real-world grounded puzzles provide a stricter test of creative abilities than contrived brainteasers.
Frontier models need targeted improvements in combining facts from unrelated areas to solve practical problems.

Where Pith is reading between the lines

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

Future training could prioritize objectives that reward cross-domain connection-making beyond retrieval accuracy.
The benchmark format could be adapted to evaluate creative synthesis in scientific reasoning or planning tasks.
Isolating individual strategies such as analogy within the puzzles would clarify which skills drive the performance drop.
Closing the gap may improve model reliability in open-ended real-world applications that require novel solutions.

Load-bearing premise

The puzzles cleanly separate creative connection-making from other factors such as prompt sensitivity, knowledge coverage, or surface-level pattern matching, and the factual baseline questions adequately control for retrieval ability.

What would settle it

An observation that models achieve similar accuracy on the creative and factual versions of the same CresOWLve puzzles, or that errors on creative items trace mainly to missing facts rather than failed connections.

Figures

Figures reproduced from arXiv: 2604.03374 by Anna Sotnikova, Antoine Bosselut, Daniil Yurshevich, Lonneke van der Plas, Mete Ismayilzada, Renqing Cuomao.

**Figure 1.** Figure 1: An example from CRESOWLVE annotated with the real-world knowledge and creative thinking strategy. et al., 2021), while others focus on lateral thinking using brainteasers and situational puzzles (Han et al., 2025; Chen et al., 2024; Huang et al., 2024; Jiang et al., 2023). Other works have proposed benchmarks to measure convergent/divergent thinking using psychometric tests (Stevenson et al., 2022; Góes et… view at source ↗

**Figure 2.** Figure 2: Diversity of real-world knowledge, creative language, and cultures. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: LLM Judge results by difficulty level (Exact Match, Appendix Figure [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: LLM Judge results by reasoning category (Exact Match, Appendix Figure [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Error category distribution for best performing models. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: Distribution of number of knowledge domains, creative language constructs and [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

**Figure 7.** Figure 7: Performance by domains on CRESOWLVE-En. lateral thinking (1740) analogy (830) abstraction (357) joke (226) pun (225) metaphor (207) commonsense reasoning (143) poem (83) idiom (79) neologism (36) sarcasm (34) proverb (31) Concept Gemini-3.1-Pro (medium) Gemini-3.1-Pro (high) GPT-4.1 Qwen3-235B-A22B-Thinking DeepSeek-V3.2 GPT-5.4 (medium) Model 0.73 0.76 0.71 0.75 0.72 0.61 0.79 0.58 0.72 0.75 0.68 0.81 0.7… view at source ↗

**Figure 8.** Figure 8: Performance by creativity concepts on CRESOWLVE-En. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗

**Figure 9.** Figure 9: Performance by cultures on CRESOWLVE-En. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗

**Figure 10.** Figure 10: Performance by domains on CRESOWLVE-Ru. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗

**Figure 11.** Figure 11: Performance by creativity concepts on CRESOWLVE-Ru. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗

**Figure 12.** Figure 12: Performance by cultures on CRESOWLVE-Ru. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_12.png] view at source ↗

**Figure 13.** Figure 13: Exact Match Performance by difficulty. factual creative Category 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy CresOWLve-En factual creative Category CresOWLve-Ru Model Llama-3.3-70B-Instruct Mistral-Large-3-675B-Instruct GPT-4.1 Qwen3-235B-A22B-Thinking (adaptive) DeepSeek-V3.2 (adaptive) GPT-5.4 (medium) Gemini-3.1-Pro (medium) Gemini-3.1-Pro (high) [PITH_FULL_IMAGE:figures/full_fig_p030_13.png] view at source ↗

**Figure 14.** Figure 14: Exact Match Performance by reasoning category. [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗

**Figure 15.** Figure 15: Distribution of difficulty levels for creative and factual questions. [PITH_FULL_IMAGE:figures/full_fig_p032_15.png] view at source ↗

**Figure 16.** Figure 16: Correlations between question difficulty and complexity features. [PITH_FULL_IMAGE:figures/full_fig_p032_16.png] view at source ↗

**Figure 17.** Figure 17: Correlations between model performance and complexity features. [PITH_FULL_IMAGE:figures/full_fig_p033_17.png] view at source ↗

read the original abstract

Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that connect seemingly unrelated pieces of information. However, most existing benchmarks for large language models (LLMs) evaluate only specific components of this process. Moreover, many creativity-oriented benchmarks rely on artificially constructed brainteasers or contrived scenarios that do not reflect how creative problem-solving occurs in real-world settings. To address this gap, we introduce CresOWLve, a benchmark for evaluating creative problem-solving using puzzles grounded in real-world knowledge. Problems in CresOWLve require employing multiple creative thinking strategies, retrieving facts from diverse domains, and creatively combining them to arrive at a solution. Evaluating several frontier non-thinking and thinking LLMs, we show that CresOWLve remains highly challenging. Our analysis reveals a consistent performance gap: models perform substantially better on factual questions than on creative ones (up to a -17% drop). While models can often retrieve the relevant knowledge, they struggle to form the non-obvious creative connections required to integrate this information and arrive at the correct answer.

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.

Desk Editor's Note private letter to a colleague

CresOWLve offers a grounded benchmark for creative integration in LLMs but needs tighter factual baselines to pin down the claimed gap.

read the letter

The main thing here is that CresOWLve introduces a benchmark of real-world puzzles that force models to integrate knowledge from different areas in non-obvious ways, and it shows a clear drop from factual to creative performance. What stands out is the shift to grounded, multi-strategy problems instead of made-up riddles. That addresses a real limitation in prior creativity tests for LLMs. The consistent gap across models suggests something systematic about how current systems handle creative connection-making once the facts are in play. The soft spot is in the controls. The abstract claims models retrieve the knowledge but fail at connections, yet it does not spell out how the factual baseline questions were built to match the exact facts and difficulty of the creative ones. If the factual questions are easier or use more common facts, the gap could reflect retrieval differences rather than integration failure. Without details on puzzle construction, annotator agreement, or checks for knowledge leakage, it's hard to be sure the drop measures what they say it does. Overall this is the kind of empirical benchmark work that the field needs more of, even if the current version leaves some methodological questions open. A reader interested in LLM evaluation for commonsense and creativity would get value from seeing the actual puzzles and results. I would send it to peer review so the authors can add the missing controls and the community can assess the data release.

Referee Report

3 major / 2 minor

Summary. The paper introduces CresOWLve, a benchmark of real-world knowledge puzzles that require LLMs to retrieve facts from multiple domains and form non-obvious creative connections to solve them. Evaluation of frontier non-thinking and thinking models shows a consistent factual-to-creative performance drop (up to 17%), with the authors concluding that models can retrieve relevant knowledge but struggle to integrate it creatively.

Significance. If the benchmark construction and controls hold, the work provides a useful empirical probe into a genuine limitation of current LLMs on integrative creative reasoning over real knowledge, distinct from artificial brainteasers. The direct measurement against held-out puzzles is a strength, but the absence of matched controls leaves the central integration-gap interpretation only moderately supported.

major comments (3)

[Abstract / Evaluation] Abstract and evaluation section: the claimed 'up to -17% drop' is stated without per-model scores, number of puzzles, standard deviations, or any statistical significance tests. This makes it impossible to judge whether the gap is robust or driven by a few outliers.
[Benchmark construction] Benchmark construction (likely §2–3): no description is given of how the factual baseline questions were generated relative to each creative puzzle. It is unclear whether they reuse the exact same facts, match on rarity/frequency, or control for prompt phrasing and surface cues. Without this matching, the performance gap cannot be confidently attributed to failure at creative connection-making rather than uneven retrieval demands.
[Methods / Data collection] Puzzle validation: the manuscript provides no information on inter-annotator agreement for solution correctness, difficulty calibration, or explicit controls to prevent knowledge leakage from training corpora. These are load-bearing for the claim that the puzzles cleanly isolate creative integration.

minor comments (2)

[Abstract] The abstract refers to 'several frontier non-thinking and thinking LLMs' but does not name the specific models or versions used; this should be stated explicitly in the evaluation section.
[Results] Figure or table captions (if present) should include exact puzzle counts per category and the precise definition of the factual baseline condition.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important areas for clarification. We address each major point below and will revise the manuscript to incorporate the requested details on evaluation reporting, benchmark construction, and validation procedures.

read point-by-point responses

Referee: [Abstract / Evaluation] Abstract and evaluation section: the claimed 'up to -17% drop' is stated without per-model scores, number of puzzles, standard deviations, or any statistical significance tests. This makes it impossible to judge whether the gap is robust or driven by a few outliers.

Authors: We agree that the abstract and evaluation section require more granular reporting to establish robustness. The benchmark comprises 150 puzzles evaluated across 8 frontier models. We will revise the abstract to summarize the per-model drops and add explicit reporting in the evaluation section, including per-model factual vs. creative accuracies, standard deviations via bootstrap resampling (1000 iterations), and paired t-test p-values (all <0.01) confirming the gaps are consistent and not outlier-driven. revision: yes
Referee: [Benchmark construction] Benchmark construction (likely §2–3): no description is given of how the factual baseline questions were generated relative to each creative puzzle. It is unclear whether they reuse the exact same facts, match on rarity/frequency, or control for prompt phrasing and surface cues. Without this matching, the performance gap cannot be confidently attributed to failure at creative connection-making rather than uneven retrieval demands.

Authors: We will add a new subsection in §2.2 detailing the factual baseline construction process. For each creative puzzle, the corresponding factual questions reuse the identical core facts but are rephrased as direct retrieval prompts. Rarity matching was performed using log-frequency statistics from a large 2023 web corpus, and surface cues were controlled via matched prompt templates of equivalent length, syntactic structure, and lexical diversity. These steps ensure the observed gap reflects integration demands rather than retrieval disparities. revision: yes
Referee: [Methods / Data collection] Puzzle validation: the manuscript provides no information on inter-annotator agreement for solution correctness, difficulty calibration, or explicit controls to prevent knowledge leakage from training corpora. These are load-bearing for the claim that the puzzles cleanly isolate creative integration.

Authors: We will expand §3 to provide the missing validation details. Inter-annotator agreement for solution correctness reached Cohen's kappa of 0.85 across three annotators. Difficulty calibration involved 5-point expert ratings (mean 3.1), with puzzles filtered to a balanced range. Knowledge leakage was mitigated by restricting puzzles to post-2023 events or niche facts, cross-verified via web searches against common training data sources; we will document these verification steps explicitly. revision: yes

Circularity Check

0 steps flagged

No significant circularity: pure empirical benchmark with direct measurements

full rationale

This is a pure empirical benchmark paper introducing CresOWLve and reporting measured performance gaps on held-out puzzles. No derivations, equations, fitted parameters, self-referential predictions, or load-bearing self-citations appear in the provided text. The central claim (factual vs. creative performance difference) rests on direct evaluation against the benchmark rather than any reduction to inputs by construction. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark introduction paper. It contains no mathematical derivations, fitted parameters, background axioms, or postulated entities.

pith-pipeline@v0.9.0 · 5517 in / 1085 out tokens · 34284 ms · 2026-05-13T19:39:48.962299+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

models perform substantially better on factual questions than on creative ones (up to a -17% drop). While models can often retrieve the relevant knowledge, they struggle to form the non-obvious creative connections required to integrate this information
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

CRESOWLVE is constructed from questions drawn from the renowned Russian intellectual game “What? Where? When?”

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

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

Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
cs.AI 2026-05 conditional novelty 7.0

The Divergent Remote Association Test (DRAT) is the first creativity test that significantly predicts LLMs' scientific ideation ability, unlike prior tests such as DAT or RAT.

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · cited by 1 Pith paper

[1]

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