aceThe Illusion of Thinking
Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from da…
Large Reasoning Models generate detailed thinking traces before answering and demonstrate improved performance on reasoning benchmarks, with advantage over standard LLMs on medium-complexity controllable puzzles.
Frontier Large Reasoning Models face a complete accuracy collapse beyond certain puzzle complexities and exhibit a counterintuitive scaling limit where reasoning effort declines despite adequate token budget.
Findings are bounded to controllable puzzle environments and reveal that LRMs have limitations in exact computation and inconsistent reasoning across puzzles.
Evidence
- Peer-reviewedSuperIntelligence - Robotics - Safety & Alignment2025-09-23
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Truvace Impact Record TRV-2026-0487, v1: “The Illusion of Thinking.” Truvace, 2026-07-22. /record/TRV-2026-0487 (accessed at citation time). sha256 a56d29a25f21da80…
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