Brake Overthinking in Long‑Reasoning Models by Detecting Semantic Redundancy
Long‑thinking LLMs often waste 41‑52% of tokens after the final answer; the PUMA framework detects when reasoning stops producing new semantic information, enabling early exit that cuts average token usage by 26.2% while keeping accuracy stable and even improving speed across multiple benchmarks.
