Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities
- URL: http://arxiv.org/abs/2605.09678v1
- Date: Sun, 10 May 2026 17:55:38 GMT
- Title: Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilities
- Abstract summary: This paper proposes Absurd World, a benchmarking framework, to test large language models against altered realism.<n>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.
- Score: 3.706540783851095
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Computational Thinking Reasoning in Large Language Models [69.28428524878885]
Computational Thinking Model (CTM) is a novel framework that incorporates computational thinking paradigms into large language models (LLMs)<n>Live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing.<n>CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability.
arXiv Detail & Related papers (2025-06-03T09:11:15Z) - Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation [106.17986469245302]
Large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking.<n>Existing reasoning benchmarks either focus on domain-specific knowledge (crystallized intelligence) or lack interpretability.<n>We propose DRE-Bench, a dynamic reasoning evaluation benchmark grounded in a hierarchical cognitive framework.
arXiv Detail & Related papers (2025-06-03T09:01:08Z) - Reasoning LLMs are Wandering Solution Explorers [5.3795217858078805]
This paper formalizes what constitutes systematic problem solving and identifies common failure modes that reveal reasoning LLMs to be wanderers rather than systematic explorers.<n>Our findings suggest that current models' performance can appear to be competent on simple tasks yet degrade sharply as complexity increases.
arXiv Detail & Related papers (2025-05-26T17:59:53Z) - Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
arXiv Detail & Related papers (2025-02-17T18:49:25Z) - Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning [9.75748930802634]
We study the power of in-context-learning to improve machine learning performance.<n>We consider an extremely simple domain: next token prediction on simple language tasks.<n>We find that LLMs uniformly underperform n-gram models on this task.
arXiv Detail & Related papers (2025-01-06T07:57:51Z) - CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge [44.59258397967782]
Large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks.
We present a systematic evaluation of state-of-the-art LLMs' complex logical reasoning abilities.
We find that LLMs excel at reasoning over general world knowledge but face significant challenges with specialized domain-specific knowledge.
arXiv Detail & Related papers (2024-07-30T05:40:32Z) - Meta Reasoning for Large Language Models [58.87183757029041]
We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs)
MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task.
We evaluate the effectiveness of MRP through comprehensive benchmarks.
arXiv Detail & Related papers (2024-06-17T16:14:11Z) - MacGyver: Are Large Language Models Creative Problem Solvers? [87.70522322728581]
We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting.<n>We create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems.<n>We present our collection to both LLMs and humans to compare and contrast their problem-solving abilities.
arXiv Detail & Related papers (2023-11-16T08:52:27Z) - Improving Large Language Models in Event Relation Logical Prediction [33.88499005859982]
Event relation extraction is a challenging task that demands thorough semantic understanding and rigorous logical reasoning.
In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic.
Our study reveals that LLMs are not logically consistent reasoners, which results in their suboptimal performance on tasks that need rigorous reasoning.
arXiv Detail & Related papers (2023-10-13T14:53:06Z) - Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate [85.3444184685235]
We propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
Our framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation.
arXiv Detail & Related papers (2023-05-30T15:25:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.