Frontier LRM accuracy collapses on hard puzzles

Researchers investigating Large Reasoning Models found that frontier reasoning systems experience complete accuracy collapse beyond certain complexity thresholds in puzzle environments. The authors report a counterintuitive scaling limit where models reduce their reasoning effort as problems become more complex, despite having adequate token budgets. Furthermore, comparisons with standard language models revealed three distinct performance regimes based on task complexity. Open article →