From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity

·ArXiv cs.LG··

arXiv:2605.00939v1 Announce Type: new Abstract: Traditional hallucination detection fails on "Stubborn Hallucinations" -- errors where LLMs are confidently wrong. We propose a geometric solution: Embedding-Perturbed Gradient Sensitivity (EPGS). We hypothesize that while robust facts reside in flat minima, stubborn hallucinations sit in sharp minima, supported by brittle memorization. EPGS detects this sharpness by perturbing input embeddings with Gaussian noise and measuring the resulting spike ...

Read full article →

Related Articles

OpenAI’s o1 correctly diagnosed 67% of ER patients vs. 50-55% by triage doctors
donsupreme · Hacker News · 5mo ago
Accelerating Gemma 4: faster inference with multi-token prediction drafters
amrrs · Hacker News · 5mo ago
A couple million lines of Haskell: Production engineering at Mercury
unignorant · Hacker News · 5mo ago
Harvard particle physicist Matthew Schwartz drops 36 papers authored with Claude
xqcgrek2 · Hacker News · 1d ago
An AI agent emailed researchers for help. It told us why
sbulaev · Hacker News · 20h ago