Reliable Reasoning

Systematizing the signals and methods that make LLM reasoning reliable

Research on making Large Language Model (LLM) reasoning more reliable expanded rapidly in 2025–2026. This book connects methods that use external signals to improve or evaluate reasoning rather than treating longer generation alone as evidence of progress.

It organizes the literature around training-side signals, inference-side signals, and structural approaches, with particular attention to capability gains, correctness estimation, and the allocation of inference compute.