Why LLMs Fail to Check Their Own Work—and How ReFlect Fixes It

This paper exposes a critical weakness in production LLM agents: standard self-critique methods fail 76% of the time because models lack structural incentive to contradict their own output. ReFlect solves this by moving error detection outside the model entirely, using a deterministic external harness that catches numerical inconsistencies and logical contradictions at inference time. This approach requires no fine-tuning or model changes, making reliable long-horizon reasoning immediately accessible to developers.