ReFlect: Training-Free Error Recovery for Long-Horizon LLM Reasoning

Long-horizon reasoning is where production LLM agents tend to quietly break. A model can produce a plausible-looking chain of thought, accept a wrong intermediate answer, and continue building on that error for every step that follows. By the time the final output appears, the damage is compounded and invisible. The paper behind ReFlect ( arXiv:2605.05737 , May 2026) quantifies exactly how bad this is: in controlled experiments, LLMs wrongly accept incorrect answers at least 76% of the time when