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Arxiv
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Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
- Shojaee et al. (2025) found that Large Reasoning Models (LRMs) face 'accuracy collapse' on planning puzzles beyond certain complexity thresholds.
- The study argues that the reported failures primarily stem from experimental design issues rather than inherent reasoning deficiencies.
- Key issues identified include Tower of Hanoi experiments exceeding model output limits, leading to failure despite acknowledging these constraints.
- The automated evaluation system fails to differentiate between reasoning failures and practical limitations, resulting in misjudgment of model abilities.
- Authors note that River Crossing benchmarks feature mathematically unsolvable instances for N > 5 due to boat capacity constraints, yet models are marked as failures for not solving these problems.
- When experimental artifacts are addressed by requesting generating functions instead of exhaustive move lists, preliminary tests suggest high accuracy on Tower of Hanoi instances previously deemed as complete failures.
- The study underscores the significance of meticulous experimental design in the assessment of AI reasoning proficiency.
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