The Jebadiah project releases v2.1 of its 27B and 9B open-weight decision models that score fixed option sets from candidate-label logits without text generation, achieving competitive Decision Index scores and establishing logit-based scoring as a viable alternative for agent routing and tool selection.

state: seedheat: lowuncertainty: mediumconvergesscott: highjebadiah-decision-models logit-based-routing decision-indexWebDevToday

What is this?

The Jebadiah project (by WebDevToday) has released v2.1 of two open-weight decision models (27B and 9B parameters) that perform classification/routing by scoring fixed candidate-label sets directly from logits rather than generating text. The project self-reports competitive scores on a 'Decision Index' benchmark. Web search returned no independent coverage — only the case's own evidence title and unrelated results — so the claims rest entirely on the project's self-published release and benchmark numbers.

Why it matters to Scott

Jebadiah v2.1 is another independent arrival at the logit-based decision model pattern Scott's frameworks treat as load-bearing: a cheap, open-weight front-door model (9B/27B) that scores fixed candidate sets from logits instead of generating text, self-benchmarked on a Decision Index. This directly extends his cheap-model-front-door doctrine, code-first architecture's logit-routing argument, model-barbell economics for small local models, and sovereign-software-assurance for agent infrastructure. The self-run Decision Index scores add a data point to the benchmark lineage the radar already tracks (Jevman Pac-Man, Nonobench, GoBench).

dev:concept.cheap-model-front-doorip:framework.code-first-architectureip:concept.model-barbellip:framework.sovereign-software-assurancedev:concept.hardware-aware-local-inferenceip:framework.decision-navigation-uidev:concept.answers-not-contentradar:intern-decision-one-pass-decisionsradar:typesafe-jev-structured-decisionsradar:verdict-local-jev-compatible-decisionsradar:routed-zero-token-skill-routerradar:jevman-pacman-decision-model-benchmarkradar:qwen38-27b-local-agent-capability

queries asked of Scott's wikis

logit-based routing vs generation for agent tool selection
decision index benchmark criteria and prior art
open-weight model sovereignty for agent infrastructure
small-model (9B-27B) local inference economics for routing
candidate-label logit scoring patterns in agent frameworks

Measured heat

now 0 pts/hpeak 1 pts/hcomments 0/hpeers p62momentum: steady1 platformsage 6h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

10-11 09:33	⭐ origin directly observed	[P] Pecision models that score every allowed label from the logits: Jebadiah v2.1 (27B, 9B), open weights and self-run benchmark results [P] WebDevToday on r/MachineLearning	—
10-11 09:33	amplified on r/MachineLearning 👑	reddit.post.1x33wgz WebDevToday	peak 1 · 0 comments · 109% of case engagement
10-11 11:30	our radar first saw it · +1.9h	discovery anchor: reddit.post.1x33wgz	—

pace: p14 vs 907 stories at the 6h mark (now 6h old) — behind addom-local-coding-harness (0.5x)

Evidence (1) — ⭐ canonical anchor

source	object	author	score	comments
🟠 reddit ⭐	[P] Pecision models that score every allowed label from the logits: Jebadiah v2.1 (27B, 9B), open weights and self-run benchmark results [P] MachineLearning	WebDevToday	1	0

Interpretation history

2026-10-11T13:32:01Z
grounded: converges/high — Jebadiah v2.1 is another independent arrival at the logit-based decision model pattern Scott's frameworks treat as load-bearing: a cheap, open-weight front-door
2026-10-11T12:03:24Z
case created — Independent open-weight decision model release with self-run Decision Index results.

Decision trace

10-12 00:32groundJebadiah v2.1 is another independent arrival at the logit-based decision model pattern Scott's frameworks treat as load-bearing: a cheap, open-weight front-door model (9B/27B) that scores fixed c
10-11 23:22attention_routeFits the next briefing as a further data point in the open decision-model ecosystem (alongside llama.cpp /v1/systemone, Bespoke Nimble, Liquid d1, Microsoft Decision-1, Laya/Vega). Self-run benchmarks
10-11 23:18attention_candidatecreate
10-11 23:03createIndependent open-weight decision model release with self-run Decision Index results.