Spanda: Open-Source Hallucination Detector Runs in 1.5ms on CPU, 90,000x Faster than Semantic Entropy

Otherwise_Nobody_721 · reddit · 2026-09-05

Spanda (MIT, pip install spanda) is a zero-dependency, pure-Python hallucination detector positioning itself as a production alternative to Semantic Entropy (Farquhar et al., Nature 2024).
The baseline's pain: Semantic Entropy needs DeBERTa clustering at 136 seconds per query plus GPU VRAM. Spanda instead samples K outputs and computes a normalized lexical consensus ratio: 1.5ms on CPU, zero extra API/VRAM cost—roughly 90,000x faster.
Accuracy holds up: 0.889 AUROC on GSM8K for 7B–27B models, matching heavy NLI clustering.
Key caveat discovered: "Confident Mode Collapse"—heavily aligned frontier models (120B+) can repeat the exact same hallucinated answer across all seeds even at temperature 0.7, at which point all self-consistency methods fail.
Author invites discussion on real-time uncertainty scoring in production pipelines.

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