Researcher Proposes 'Convergence Point Theory' to Explain Why LLM Uncertainty Varies by Topic, Not Model

An independent researcher has published a unified framework suggesting that LLM hallucinations, uncertainty, and calibration failures stem not from model limitations but from the inherent consensus density of knowledge on a given topic. The theory identifies three zones—Full Consensus (math, physics), Partial Consensus (ethics, politics), and Non-Consensus (consciousness, philosophy)—and finds that conflicting data destabilizes AI more than absent data. The work raises safety concerns about training-induced confidence on unanswerable philosophical questions where humanity has no agreement.

Why it matters: Understanding what drives LLM uncertainty is critical for AI safety, reliability, and deployment decisions—and this framework offers a testable explanation that unifies previously isolated research on hallucinations and failure modes.