Adversarial LLM fact-checking loops plateau after one pass, mini-experiment shows
burny_tech · x · 2026-10-28
The author argues LLM-based adversarial error correction in a loop is underrated — peer review is the OG human error-correction loop, and agents enable machine peer review of peer reviews. A minimal experiment on a paragraph of STEM text with injected errors, using web-search-grounded fact-checking agents and an LLM judge, shows accuracy jumps from 40% to 85-90% after one correction pass, then plateaus. He outlines limitations (weak models, tiny dataset) and future directions: symbolic/Lean-verifiable facts, evaluating whole agentic research pipelines, and a power-law hypothesis for verification difficulty across domains.
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