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Evaluation grading with decision models

Replacing LLM-as-judge steps with typed rubric scores or yes/no checks inside eval harnesses and regression suites.

118 public projects · page 3 of 4

  • Small eval that uses Jev to judge whether shadcn-ui/lint findings are correct and whether each message says what to change.

    ProjectresearchJavaScript
  • Harness that runs Jev on all 19,646 TMMLU+ Traditional Chinese questions as four-way Choices, reporting accuracy and calibration.

    ProjectresearchPython
  • Tool that recomputes Jev's confidence identities on outputs published in public repositories to spot answers Jev never actually produced.

    ServiceresearchPython
  • Research adding plain-English constraints to JEPA world-model planning, with Jev judging each imagined step instead of an LLM.

    ProjectresearchPython
  • Inference-time loop where a System One judge like Jev scores a model's thought paths and picks which reasoning branch to keep.

    ProjectresearchPython
  • Study finding commercial decision models including Jev give different yes/no probabilities on Taiwan statements by Chinese script.

    ServiceresearchPython
  • Independent, source-linked research on Jev with about 948 rubric-scored public repositories, recurring patterns and verified findings.

    Projectresearch
  • TypeSafe's official drop-in replacement for the SDK's system_one call backed by OpenAI, Anthropic or Gemini, for comparing Jev with LLMs.

    LibraryproductionPython
  • Official TypeSafe code to reproduce evals.typesafe.ai: four workflow evals comparing Jev with frontier LLMs on cost and accuracy.

    LibraryproductionPython
  • MCP server exposing a judge tool for typed noul, choice and score questions over JSON state, backed by Jev.

    MCP serverbetaTypeScript
  • Declarative compiler that turns state, decisions and labeled examples into a measured, frozen JSON program of typed Jev questions.

    LibraryprototypePython
  • Research proposal-review contract: an LLM proposes one action, Jev answers four narrow questions, code produces evidence, applies nothing.

    LibraryresearchTypeScript
  • Public wall where Jev answers short questions yes, no or it depends in about 100 ms and picks which LLM answers signed-in users.

    AppproductionTypeScript
  • Writing checker running separate named Jev checks such as hedging, filler, tone and passive voice, each with its own verdict.

    DemobetaTypeScript
  • Community tool that fetches a SaaS homepage as Markdown and asks Jev ten questions about first-visit clarity, with a leaderboard.

    ServicebetaTypeScript
  • CLI that scans TS/JS and Python code for LLM calls that are really decisions, converts them to Jev and shadow-tests them on real traffic.

    DemobetaTypeScript
  • CLI that transcribes a video, has Jev score every sentence against rubric flags, and renders a 16:9 edit with a live meter.

    AppbetaPython
  • Reliability testing for typed decision models: contracts, calibration metrics, metamorphic fuzzing and CI pass/warn/fail gates.

    DemoprototypePython
  • Minimal web demo: paste an agent step trace and Jev judges whether to continue, retry, escalate or stop.

    DemoprototypeTypeScript
  • Upload a paper PDF and get a fast Jev-based judgment of understandability and completeness, with low-confidence answers flagged.

    DemoprototypeTypeScript
  • Self-hosted playground running SystemOne requests through an emulator, Jev and an LLM judge to compare per-question fidelity.

    AppprototypePython
  • Startup-idea evaluator where Jev scores eight indie-hacker questions and code turns the weighted average into KILL, FIX or SHIP.

    AppprototypeTypeScript
  • Proof of concept that scores replayed support-call transcripts live against a weighted scorecard using OpenAI Decisions or Jev.

    AppprototypeTypeScript
  • Video demo code comparing Jev with Laya, Strands Decider and Claude on grading, fetching and prompt-injection resistance.

    AppresearchPython
  • Workbench where Jev sorts training records into keep, review or reject, then trains LoRA adapters and evaluates them.

    ModelbetaPython
  • Bespoke Labs' open recipe, data and 9B LoRA model for Jev-style choice and true/false decisions, runnable on Apple Silicon or NVIDIA GPUs.

    ModelresearchPython
  • Comparative judgment of ASAP essays by Jev and two LLM judges, turning pairwise decisions into scores compared with human grades.

    ServiceresearchPython
  • Reinforcement-learning lab where cached Jev judgments act as reward signals for DQN agents on four classic control and grid games.

    ModelresearchPython
  • Open benchmark for System One decision models on Choice, Score and Noul, reporting accuracy, calibration and robustness separately.

    BenchmarkproductionPython
  • Convex's eval repo with a 108-question multiple-choice benchmark comparing Jev's Choice answers with 14 LLMs on accuracy, latency and cost.

    BenchmarkproductionTypeScript