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Jeff put a 2B classifier next to Jev: 96% on labels, 62.6% on JudgeBench

This story is from 2026-09-29. It is preserved in the archive; the latest stories are on the live feed.

The Jeff repo is a family of tiny "decision models" that do zero-shot classification and return a calibrated probability per option from a single forward pass. No text generation, no parsing. The README ships a head-to-head table against Jev across five benchmarks plus JevBench's hard tier, and I k…

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  1. 2026-09-29 01:15 · DEV Community — Machine Learning
    Jeff put a 2B classifier next to Jev: 96% on labels, 62.6% on JudgeBench

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  1. Tutorial Walks Through Four Python Examples of Image-Based Decision Making — Towards Data Science
  2. Free Open-Source Alternatives to Jev Highlighted for Local Use — KDnuggets
  3. Study Examines Whether Decision Models Can Serve as Multilingual Judges — arXiv cs.CL
  4. Decision models: the AI that can't ramble, and why my prompt-and-parse code wants one — DEV Community — AI
  5. Google joins race to build decision models, chasing startup TypeSafe's Jev — AI Weekly
  6. 92.2% vs 90.0%: Google's decision API edges out Jev — DEV Community — Machine Learning
  7. Gemini Decision 1 vs Jev 1.14: Evaluate the Routing Policy, Not One Score — DEV Community — AI
  8. Update: Google just released their very own Decisions API. I will have to check how it goes against Jev for zero-shot work. https://blog.google/innovation-and-ai/technology/developers-tools/gemini-decisions-api/ — DEV Community — AI

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