arxiv: 2605.09678 · v1 · submitted 2026-05-10 · 💻 cs.AI

Recognition: 1 theorem link

· Lean Theorem

Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities

Dianbo Liu, Golam Md Muktadir, Mehrab Hossain, Ryan Albright, S M Jubaer, Zarif Ikram

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:58 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM reasoningbenchmarkingabsurd worldslogical reasoningAI evaluationprompting techniquesreasoning robustness

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The pith

Altering real-world scenarios into absurd but logically identical versions reveals whether LLMs reason from logic or from memorized patterns.

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

The paper introduces Absurd World, a framework that takes real-world problems and automatically changes their symbols, actions, sequences, and events to produce absurd variants. These variants keep the exact same logical structure so that humans can still solve them easily, but the changes remove familiar real-world cues that models might have learned during training. Testing many LLMs with both basic and advanced prompts shows that the framework distinguishes models that rely on pattern matching from those that can follow logic alone. The approach can be applied repeatedly to the same original task to check whether an LLM's reasoning holds up under variation.

Core claim

By breaking real-world scenarios into symbols, actions, sequences, and events and then automatically altering those elements to create absurd worlds, Absurd World keeps the original logic intact while stripping away learned real-world patterns, providing a direct test of whether LLMs solve problems through genuine reasoning.

What carries the argument

The Absurd World transformation that decomposes a scenario into symbols, actions, sequences, and events and replaces them with new ones while preserving solvability.

If this is right

Models that succeed on standard versions but fail on absurd versions are using real-world patterns rather than logic.
The same original problem can be turned into many absurd variants to test consistency of reasoning.
Advanced prompting techniques can be measured for how much they improve performance on logic-only versions.
The framework applies to any real-world task to verify whether reasoning is robust to changes in surface details.

Where Pith is reading between the lines

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

The method offers an automated way to generate large numbers of test cases without manual rewriting of each problem.
Standard benchmarks may overestimate reasoning ability if they only use familiar real-world settings.
Similar absurdification could be applied to evaluate reasoning in other AI systems or domains.

Load-bearing premise

Automatically changing symbols, actions, sequences, and events always leaves the original logical relationships and solution method unchanged.

What would settle it

Finding a model that solves the absurd versions at the same rate as the original versions, or discovering an alteration that makes the problem unsolvable or logically different.

Figures

Figures reproduced from arXiv: 2605.09678 by Dianbo Liu, Golam Md Muktadir, Mehrab Hossain, Ryan Albright, S M Jubaer, Zarif Ikram.

**Figure 2.** Figure 2: Decomposition of a real-world scenario into symbolic primitives. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Semantic prior conflict in Absurd World. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Spider graph displaying average DO-0 scores on all tested rulesets (MISS & SWITCH, MISSING, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: DO-FS scores (y-axis) compared with DO-0 scores (x-axis) for each model across seven rulesets [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: Spider graph displaying DO-0 scores on all tested rulesets (MISS & SWITCH, MISSING, [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: DO-0 scores for the MISS & SWITCH ruleset (y-axis) compared with DO-0 scores for the [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: TOP HALF: DO-0 (left) and DO-FS (right) scores compared with average entropy. Each dot represents a model-ruleset pair (that is, the results for a particular model on a particular ruleset), color-coded by model. BOTTOM HALF: Average entropy in incorrect answers compared with average entropy in correct answers for DO-0 (left) and DO-FS (right). Each dot represents a model-ruleset pair. (Model-ruleset pairs … view at source ↗

read the original abstract

While extremely powerful and versatile at various tasks, the thinking capabilities of large language models (LLMs) are often put under scrutiny as they sometimes fail to solve problems that humans can systematically solve. However, recent literature focuses on breaking LLM reasoning with increasingly complex problems, and whether an LLM is robust in simple logical reasoning remains underexplored. This paper proposes Absurd World, a benchmarking framework, to test LLMs against altered realism, where scenarios are logically coherent, and humans can easily solve the tasks. Absurd World breaks a real-world model into symbols, actions, sequences, and events, which are automatically altered to create absurd worlds where the logic to solve the tasks remains the same. It evaluates a large collection of models with simple and advanced prompting techniques, and proves that it is an effective tool to determine LLMs' ability to think logically, ignoring the patterns learned from the real world. One can use this framework to extensively test an LLM against a real-world problem to verify whether the LLM's reasoning capability is robust against variations of the task.

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

Absurd World gives a simple automated pipeline for rewriting logic tasks into absurd but supposedly equivalent versions, yet the paper provides little evidence that the transformations actually preserve the logic or strip all patterns.

read the letter

The main takeaway is that this paper offers an automated way to take ordinary reasoning scenarios, break them into symbols actions sequences and events, then swap those pieces to create absurd versions that are meant to keep the same underlying logic. The goal is to test whether LLMs can still solve them without relying on real-world statistical shortcuts from training data. They apply the method to a range of models using both basic and chain-of-thought style prompts and claim it reveals limits in logical thinking. The automated alteration step is the clearest new element here. Earlier robustness benchmarks often rely on hand-made adversarial cases or entirely separate domains, so a general procedure that starts from existing tasks and absurdifies them could be practical for repeated testing. Running the same altered problems across many models and prompt types also gives a useful comparative view of where different systems hold up or fail. The soft spot is the missing support for the key assumption. Nothing in the description shows a human validation step where people solve the original and absurd versions at comparable rates, nor any check that the new symbols or sequences do not introduce fresh regularities that models could exploit. Without those controls or reported numbers on performance drops, it is difficult to attribute any gaps specifically to the removal of real-world patterns rather than to changes in the task itself. The abstract uses strong language like proves effectiveness, but the lack of error analysis or preservation checks leaves the central claim under-supported. This work is aimed at researchers who build or use LLM reasoning benchmarks and want ideas for out-of-distribution variants. A reader already working on evaluation methods could extract the pipeline and try it on their own tasks, but the current version would benefit from added validation experiments before it becomes a standard tool. I would send it for peer review if the authors add human baselines and quantitative breakdowns, because the core idea is straightforward enough to be worth testing properly.

Referee Report

2 major / 2 minor

Summary. The paper proposes Absurd World, a benchmarking framework that decomposes real-world scenarios into symbols, actions, sequences, and events, then automatically alters them to generate absurd but logically coherent variants. These variants are intended to preserve the original solution logic while stripping away real-world statistical patterns that LLMs might exploit. The work evaluates a collection of LLMs using both simple and advanced prompting techniques and claims to demonstrate that the framework effectively isolates and reveals deficiencies in logical reasoning.

Significance. If the transformations reliably preserve logical equivalence and eliminate exploitable cues, the framework could offer a practical, extensible method for constructing controlled probes of LLM reasoning that go beyond standard benchmarks prone to contamination from training data. This would help address the gap between models' apparent competence on familiar tasks and their robustness to logically equivalent but unfamiliar variants.

major comments (2)

[Abstract] Abstract: the claim that the method 'proves that it is an effective tool to determine LLMs' ability to think logically, ignoring the patterns learned from the real world' is unsupported without quantitative results; the manuscript must report specific accuracy deltas, error breakdowns, and statistical tests comparing original vs. absurd variants across models and prompts.
[Method] Method description: no formal preservation argument, human validation study, or cue-removal analysis is described for the automatic alteration of symbols/actions/sequences/events; without evidence that humans solve the absurd versions at rates comparable to the originals and that the new symbol sets introduce no learnable regularities, performance gaps cannot be attributed to logical-reasoning deficits rather than transformation artifacts.

minor comments (2)

[Experiments] Clarify the exact number of models evaluated, the full list of prompting techniques, and the size of the task suite in the experiments section to allow reproducibility.
[Introduction] The abstract and introduction would benefit from a brief comparison to related benchmarks (e.g., those using counterfactual or symbolic variants) to better situate the novelty of the automatic alteration pipeline.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and describe the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the method 'proves that it is an effective tool to determine LLMs' ability to think logically, ignoring the patterns learned from the real world' is unsupported without quantitative results; the manuscript must report specific accuracy deltas, error breakdowns, and statistical tests comparing original vs. absurd variants across models and prompts.

Authors: We agree that the abstract's phrasing is overly strong and that more explicit quantitative support would improve clarity. The manuscript already reports evaluation results across multiple LLMs and prompting methods on both original and absurd variants, showing performance differences. In revision we will change 'proves' to 'demonstrates' in the abstract, expand the results section with concrete accuracy deltas between conditions, per-model error breakdowns, and statistical tests (such as paired significance tests) comparing original versus absurd performance. revision: yes
Referee: [Method] Method description: no formal preservation argument, human validation study, or cue-removal analysis is described for the automatic alteration of symbols/actions/sequences/events; without evidence that humans solve the absurd versions at rates comparable to the originals and that the new symbol sets introduce no learnable regularities, performance gaps cannot be attributed to logical-reasoning deficits rather than transformation artifacts.

Authors: We acknowledge that additional validation would strengthen the attribution of performance gaps to reasoning rather than artifacts. The transformation is constructed to preserve logical structure by replacing surface elements while retaining relational dependencies and solution steps; we will add an explicit formal preservation argument to the method section. We will also incorporate a small-scale human validation study confirming comparable human accuracy on original and absurd versions, together with an analysis of symbol and sequence distributions to show that no new exploitable regularities are introduced. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmarking proposal with no derivation chain or self-referential reductions

full rationale

The paper presents Absurd World as an empirical benchmarking framework that breaks scenarios into symbols/actions/sequences/events and alters them to test LLM logical reasoning. No equations, first-principles derivations, or predictions are claimed; the work consists of method description followed by model evaluations under prompting techniques. The assertion that logic is preserved while real-world cues are removed is presented as a design property of the automatic alteration process rather than a result derived from prior fitted parameters or self-citations. No load-bearing step reduces to its own inputs by construction, and the central effectiveness claim rests on experimental outcomes rather than definitional equivalence. This is a standard non-circular empirical proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical method paper with no mathematical derivations; no free parameters, axioms, or invented entities are introduced beyond the benchmarking procedure itself.

pith-pipeline@v0.9.0 · 5510 in / 1056 out tokens · 39314 ms · 2026-05-12T01:58:30.125138+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/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear
Absurd World breaks a real-world model into symbols, actions, sequences, and events, which are automatically altered to create absurd worlds where the logic to solve the tasks remains the same.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 3 internal anchors

[1]

E. Abbe, S. Bengio, A. Lotfi, C. Sandon, and O. Saremi. How far can transformers reason? the globality barrier and inductive scratchpad. InThe Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URLhttps://openreview.net/forum?id=FoGwiFXzuN

work page 2024
[2]

Barron and D

J. Barron and D. White. Too big to think: Capacity, memorization, and generalization in pre-trained transformers, 2025. URLhttps://arxiv.org/abs/2506.09099

work page arXiv 2025
[3]

A is B” fail to learn “B is A

L. Berglund, M. Tong, M. Kaufmann, M. Balesni, A. C. Stickland, T. Korbak, and O. Evans. The reversal curse: Llms trained on "a is b" fail to learn "b is a", 2024. URLhttps://arxiv.org/abs/ 2309.12288

work page arXiv 2024
[4]

A. P. Gema, A. Hägele, R. Chen, A. Arditi, J. Goldman-Wetzler, K. Fraser-Taliente, H. Sleight, L. Petrini, J. Michael, B. Alex, P. Minervini, Y. Chen, J. Benton, and E. Perez. Inverse scaling in test-time compute, 2025. URLhttps://arxiv.org/abs/2507.14417

work page arXiv 2025
[5]

Z. Han, F. Battaglia, K. Mansuria, Y. Heyman, and S. R. Terlecky. Beyond text generation: Assessing large language models’ ability to reason logically and follow strict rules.AI, 6(1), 2025. ISSN 2673-2688. doi: 10.3390/ai6010012. URLhttps://www.mdpi.com/2673-2688/6/1/12

work page doi:10.3390/ai6010012 2025
[6]

Kung-Hsiang Huang, Akshara Prabhakar, Onkar Thorat, Divyansh Agarwal, Prafulla Ku- mar Choubey, Yixin Mao, Silvio Savarese, Caiming Xiong, and Chien-Sheng Wu

K.-H. Huang, A. Prabhakar, O. Thorat, D. Agarwal, P. K. Choubey, Y. Mao, S. Savarese, C. Xiong, and C.-S. Wu. Crmarena-pro: Holistic assessment of llm agents across diverse business scenarios and interactions, 2025. URLhttps://arxiv.org/abs/2505.18878

work page arXiv 2025
[7]

D. Li, S. Cao, T. Griggs, S. Liu, X. Mo, E. Tang, S. Hegde, K. Hakhamaneshi, S. G. Patil, M. Zaharia, J. E. Gonzalez, and I. Stoica. Llms can easily learn to reason from demonstrations structure, not content, is what matters!, 2025. URLhttps://arxiv.org/abs/2502.07374

work page arXiv 2025
[8]

S. Lin, J. Hilton, and O. Evans. Truthfulqa: Measuring how models mimic human falsehoods, 2022. URLhttps://arxiv.org/abs/2109.07958

work page internal anchor Pith review Pith/arXiv arXiv 2022
[9]

J. Lu, Z. Xu, and M. Kankanhalli. Reasoning llms are wandering solution explorers, 2025. URL https://arxiv.org/abs/2505.20296

work page arXiv 2025
[10]

I. R. McKenzie, A. Lyzhov, M. Pieler, A. Parrish, A. Mueller, A. Prabhu, E. McLean, A. Kirtland, A. Ross, A. Liu, A. Gritsevskiy, D. Wurgaft, D. Kauffman, G. Recchia, J. Liu, J. Cavanagh, M. Weiss, S. Huang, T. F. Droid, T. Tseng, T. Korbak, X. Shen, Y. Zhang, Z. Zhou, N. Kim, S. R. Bowman, and E. Perez. Inverse scaling: When bigger isn’t better, 2024. UR...

work page arXiv 2024
[11]

S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work?, 2022. URLhttps://arxiv. 12 org/abs/2202.12837

work page arXiv 2022
[12]

Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models

I. Mirzadeh, K. Alizadeh, H. Shahrokhi, O. Tuzel, S. Bengio, and M. Farajtabar. Gsm-symbolic: Understanding the limitations of mathematical reasoning in large language models, 2025. URL https://arxiv.org/abs/2410.05229

work page arXiv 2025
[13]

Nezhurina, L

M. Nezhurina, L. Cipolina-Kun, M. Cherti, and J. Jitsev. Alice in wonderland: Simple tasks showing complete reasoning breakdown in state-of-the-art large language models, 2025. URL https://arxiv.org/abs/2406.02061

work page arXiv 2025
[14]

Training language models to follow instructions with human feedback

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. Training language models to follow instructions with human feedback, 2022. URLhttps://arxiv.org/abs/2203.02155

work page internal anchor Pith review Pith/arXiv arXiv 2022
[16]

The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity, 2025

P. Shojaee, I. Mirzadeh, K. Alizadeh, M. Horton, S. Bengio, and M. Farajtabar. The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity, 2025. URLhttps://arxiv.org/abs/2506.06941

work page arXiv 2025
[17]

W. Sun, C. Zhang, X. Zhang, X. Yu, Z. Huang, P. Chen, H. Xu, S. He, J. Zhao, and K. Liu. Beyond instruction following: Evaluating inferential rule following of large language models, 2024. URL https://arxiv.org/abs/2407.08440

work page arXiv 2024
[18]

R. Wang, G. Todd, Z. Xiao, X. Yuan, M.-A. Côté, P. Clark, and P. Jansen. Can language models serve as text-based world simulators?, 2024. URLhttps://arxiv.org/abs/2406.06485

work page arXiv 2024
[19]

K. Xie, I. Yang, J. Gunerli, and M. Riedl. Making large language models into world models with precondition and effect knowledge, 2024. URLhttps://arxiv.org/abs/2409.12278

work page arXiv 2024
[20]

Yadav, I

A. Yadav, I. Nalawade, S. Pillarichety, Y. Babu, R. Ghosh, S. Basu, W. Zhao, A. Nasaeh, S. Bala- subramanian, and S. Srinivasan. Hop, skip, and overthink: Diagnosing why reasoning models fumble during multi-hop analysis, 2025. URLhttps://arxiv.org/abs/2508.04699

work page arXiv 2025
[21]

Z. Yi, Q. Jiang, R. Ma, X. Chen, Q. Yang, M. Wang, F. Ye, Y. Shen, Z. Tu, X. Li, and Linus. Too good to be bad: On the failure of llms to role-play villains, 2025. URLhttps://arxiv.org/abs/ 2511.04962

work page arXiv 2025
[22]

C. Zhao, Z. Tan, P. Ma, D. Li, B. Jiang, Y. Wang, Y. Yang, and H. Liu. Is chain-of-thought reasoning of llms a mirage? a data distribution lens, 2026. URLhttps://arxiv.org/abs/2508.01191

work page internal anchor Pith review Pith/arXiv arXiv 2026
[23]

C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, S. Zhang, G. Ghosh, M. Lewis, L. Zettlemoyer, and O. Levy. Lima: Less is more for alignment, 2023. URL https://arxiv.org/abs/2305.11206. 13 A DO-0 (zero-shot) performance per model in MISSING and MISS & SWITCH rulesets. 0.40 0.50 0.60 0.70 0.80 0.90 1.00 DO-0 Score (MISSING Ru...

work page arXiv 2023