arXiv: Grad Detect uses gradient signals to catch LLM hallucinations

arXiv·medium signal

Grad Detect proposes a gradient-based method for detecting hallucinations in LLM outputs, leveraging internal model signals rather than external fact-checking or sampling-based consistency. Reliable, low-overhead hallucination detection is a core dependency for agent reliability — agents that can flag their own uncertain outputs can route to verification or human review instead of acting on fabrications. The gradient approach is notable for working at inference without requiring multiple sampled generations.