LLMs Keep Acting When They Should Stop, New Study Shows
Across 28 000 web‑shopping, terminal and QA tasks, 13 LLM‑agent systems rarely know when to quit, often grinding through futile steps. Even larger models can be worse at timely abstention, exposing a safety gap for deployed agents. The authors’ CONVOLVE technique doubles Llama‑3.3‑70B’s timely recall from 27 % to 57 % without retraining.