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I trained a 15M "decision-only" model two ways — linear recurrence vs Transformer attention. Accuracy was a coin flip; latency and training cost weren't.[R]

Inspired by TypeSafe's Jev (a non-generative model that only outputs bounded decisions — choices, probabilities, calibrated confidence), I built two small decision models end-to-end on identical budgets and tasks to answer one question: which substrate makes the better Jev? Repo: https://github.com…

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  1. 2026-10-14 18:07 · r/MachineLearning
    I trained a 15M "decision-only" model two ways — linear recurrence vs Transformer attention. Accuracy was a coin flip; latency and training cost weren't.[R]

More stories

  1. We’re releasing Jev 1.14.0 along with experimental features for dreamers — Hacker News Front Page
  2. Moralitybench.ai - Open benchmark testing LLMs against validated human-moral psychology instruments, updated with 3 new LLMs including Argon and Muse Wonder — r/ArtificialInteligence
  3. “Take work away from frontier LLMs”: Subquadratic’s decision model claims 12x Jev’s context window — The New Stack AI
  4. Saving tokens and cost by using Jev in Jinja templated Skills — r/AI_Agents
  5. Fine-tuned two small decision models on my own server logs, locally on a Mac Studio. On par with Jev (for my specific use). — r/LocalLLM
  6. LLMs vs traditional classifiers for large scale data processing — r/artificial
  7. Workday is using TypeSafe's Jev to build user interfaces on the fly — The New Stack AI
  8. A label-redefinition suffix moved our security classifier more than a length-matched neutral sentence — r/PromptEngineering

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