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2 posts tagged with "hallucination"

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Vela 2.0: Open Foundation Routing Models

· 24 min read
Ádám Kovács
Co-founder @ KR Labs · LettuceDetect
Xunzhuo Liu
Intelligent Routing @vLLM | Open Source & AI @AMD

Still one model. Still one request. Now with spans. Sound on.

Open Foundation Routing Models. Bringing span-level decisions to System One.

Vela 2.0 is a family of open models for routing, safety checks and span-level decisions. Define your options, labels and rubrics at request time; get structured answers with probabilities and character-offset spans through one SystemOne-compatible interface.

Explore the four models → · Read about Decision →

LettuceDetect v2 in Semantic Router: Generative Hallucination Detection as a vLLM Endpoint

· 9 min read
Ádám Kovács
Co-founder @ KR Labs · LettuceDetect
Bowei He
Postdoctoral Researcher @ MBZUAI · McGill
Xunzhuo Liu
Intelligent Routing @vLLM | Open Source & AI @AMD
Huamin Chen
AI @Microsoft

Semantic Router can now verify grounded responses with a generative span detector served by vLLM. The new endpoint detector backend runs LettuceDetect v2 against every fact-checkable answer: unsupported spans are located to the character, typed against a hallucination taxonomy, and explained — in one call, before the response reaches the user.

The models come out of a joint paper between KR Labs and the Semantic Router team, Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents (arXiv:2607.00895). This post walks through the paper — the benchmark, the taxonomy, the models, and what they score — and then through the integration that puts the detector into the serving stack.

LettuceDetect v2 flagging contract hallucinations through Semantic Router