LLMs Think They Can Solve Puzzles (But Sometimes Forget How to Move Disks)

Overview

Paper Summary › Explain Like I'm Five › Conflicts of Interest › Identified Limitations › Rating Explanation › Good to know › Topic Hierarchy › File Information ›

Paper Summary

Paperzilla title

Large Reasoning Models (LRMs), despite self-reflection mechanisms, face accuracy collapse beyond certain puzzle complexities and exhibit counterintuitive scaling limits, reducing thinking effort as difficulty increases. Three reasoning regimes emerge: standard LLMs outperform LRMs in simple puzzles, LRMs excel in moderately complex ones, and both fail in highly complex puzzles, highlighting fundamental limitations in their generalizable reasoning capabilities.

Explain Like I'm Five

Scientists found that super-smart computer brains are good at puzzles that are just right, not too easy and not too hard. But for really simple or super-duper tricky puzzles, even these big brains get stuck and don't think as well.

Possible Conflicts of Interest

Authors are affiliated with Apple, which has vested interests in the development and application of advanced language models. This potential COI is acknowledged in the paper.

Identified Limitations

Limited Generalizability of Puzzle Environments

The puzzle environments, while offering controlled experimentation, represent a narrow slice of reasoning tasks and might not generalize well to real-world scenarios. It isn't clear if the puzzle solving strategies learned can transfer to knowledge intensive reasoning and/or complex real-world problems.

Limited Access to Internal Model Mechanisms

The study primarily focuses on closed-source LLMs (via API) and open-source models where thinking traces are accessible. This limits the scope of analysis and prevents deeper investigation into the internal mechanisms of other prominent models.

Strict Success Criterion

Evaluation relies on perfect move sequences, with a single incorrect move leading to failure. This strict criterion may not reflect real-world reasoning scenarios where partial solutions or iterative refinements are possible.

Rating Explanation

The paper presents a well-designed controlled study on the reasoning capabilities of Large Language Models using algorithmic puzzle environments. The methodology enables systematic investigation into how complexity affects solution accuracy and the thinking process. The findings, including the identification of three distinct reasoning regimes and the counterintuitive scaling limit of thinking tokens, are valuable contributions to the field. While the puzzle environment focus introduces limitations in generalizability, the rigorous methodology, insightful analysis, and practical implications for LRM development warrant a strong rating. The potential conflict of interest with Apple affiliation is acknowledged and considered in the rating.

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Topic Hierarchy

Domain: Physical Sciences

Field: Computer Science

Subfield: Artificial Intelligence

File Information

Original Title: The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

Uploaded: July 13, 2025 at 04:08 PM

Privacy: Public