CPU-Based Entropy Method Detects Local LLM Hallucinations Without GPU Overhead

Researchers introduced Spanda, a lightweight Python tool that detects hallucinations in local LLMs using CPU-based string normalization and Shannon entropy in just 1.3 microseconds. By evaluating multiple sampled outputs at a temperature of 0.7, it eliminates the need for VRAM-heavy GPU cross-encoders. This approach enables developers running local models to deploy zero-latency, zero-VRAM hallucination guardrails on consumer hardware. It makes using medium-to-large local models (7B to 27B parameters) for structured tasks like code, math, and JSON extraction significantly more reliable and efficient. Testing showed accuracy peaks around 0.89 AUROC on 27B models because confident answers produce identical string tokens, whereas 1.5B models perform poorly (~0.58 AUROC) due to inconsistent output formatting. Surprisingly, frontier 120B models exhibited 'Confident Mode Collapse' (0.09 AUROC), generating identical hallucinated answers across all stochastic runs with false certainty.