Models & training Benchmark
Jeeves reports higher decision accuracy than Jev-like baselines on held-out data
Jeeves combines a fine-tuned 9B model, a pointer head and a diffusion drafter, reporting 0.889 accuracy on unseen test data and 0.935 on JevBench public tiers; code and data are available. Its results and latency figures give engineers a concrete basis for evaluating reasoning-based decision models.
- WhatJeeves is a 9B Jev-like classifier that reasons before deciding, built from Qwen3.5-9B with LoRA, a pointer head, and a block-4 diffusion drafter.
- HowTrained with CISPO, it scores 0.889 on held-out test data versus 0.822 for Kev-9B and 0.857 for Jev; thinking lifts its score from 0.804 to 0.840.
- Why it mattersIts Jev-compatible API handles yes/no, multiple-choice, and rating questions in one request, offering a reasoning-based alternative to fallback pipelines.
