Show HN: Spanda – Sub-microsecond LLM epistemic uncertainty in Rust
Spanda is a Rust-based tool that quantifies LLM epistemic uncertainty using a zero-parameter metric called Exact-Match Normalized Entropy. It achieves sub-microsecond latency by eliminating the need for secondary neural networks, operating approximately 90,000 times faster than traditional Semantic Entropy methods. The system demonstrates high accuracy in detecting hallucinations for structured reasoning tasks but warns that frontier models may exhibit confident mode collapse on ungrounded factual queries.
- ▪Spanda computes uncertainty directly over deterministic lexical clusters without requiring GPU resources or secondary NLI cross-encoders.
- ▪Empirical tests show that Spanda matches or exceeds neural Semantic Entropy performance on structured reasoning benchmarks like GSM8K for models ranging from 1.5B to 27B parameters.
- ▪The research identifies a safety risk where 120B parameter models exhibit Confident Mode Collapse, producing identical incorrect answers that deceive self-consistency checks.
- ▪Performance benchmarks indicate the Spanda Rust gateway achieves a kernel latency of 652.1 nanoseconds and a cold startup time of 3.69 milliseconds.
- ▪External grounding via Retrieval-Augmented Generation is recommended as mandatory for frontier models to mitigate the risks of confident hallucinations.
2 outlets in our directory ran this story, first to last over 3 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Spanda: Sub-microsecond LLM epistemic uncertainty in Rust — Ycombinator
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| Original publisher | GitHub |
| Canonical URL | https://github.com/Adarshent/Spnda |
| Publication time | Fri, 11 Sep 2026 21:48:56 +0000 |
| Retrieval time | 2026-09-11T21:53:35.785Z |
| Last seen | 2026-09-11T21:53:35.785Z |
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| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LNdzMYfhx3pf · 2 stories |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
⚡ Spanda ($R_{sc}$) Zero-Cost Epistemic Uncertainty Quantification for Large Language Models Detect LLM hallucinations and quantify uncertainty in microseconds without secondary NLI cross-encoders. 📌 Overview Traditional epistemic uncertainty estimation in LLMs relies on Semantic Entropy (SE) (Kuhn et al., 2023; Farquhar et al., Nature 2024). While effective, Semantic Entropy requires clustering $K$ sampled generation paths using pairwise bidirectional NLI entailment classifiers (e.g., DeBERTa-v3-base). This introduces two severe production bottlenecks: Quadratic Cost: $\binom{K}{2}$ forward passes per query (45 neural evaluations for $K=10$). Serving Latency: Adds $\sim$90 ms of GPU overhead per inference call, making it unusable for high-throughput production serving.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.