LocalLLaMA/typed-decisions

Typed Decisions

A benchmark for typed probabilistic decisions. A model gets one piece of unstructured state and answers five typed questions about it at once, and every answer is a probability distribution, not a single label.

The schema follows the System One primitives (noul, choice, score) used by TypeSafe AI, so a row replays against any API with that shape. The benchmark is independent: it is not affiliated with TypeSafe and does not reproduce their Jev model.

Leaderboard

test split: 400 cases, 2,000 decisions. This table lists general models scored zero-shot: they have never seen these workflows or their question schemas. Models that were fitted or fine-tuned on train are in the second table below; the two tables are not comparable.

#	Model	Kind	Accuracy ↑	KL from gold ↓	Brier ↓	ECE ↓	p50 latency	Price / 1M input
1	meraGPT Decider 1 (sd-1)	general, zero-shot	0.768	0.096	0.052	0.180	526 ms	$0.03
2	Liquid AI d1 (d1:free)	general, zero-shot	0.742	0.475	0.155	0.124	525 ms	free tier
3	TypeSafe Jev 1.13.0	general, zero-shot	0.727	1.442	0.148	0.144	710 ms	$0.042
4	Featherless Simple Jev (Qwen3.6-35B-A3B-classifier)	general, zero-shot	0.716	0.488	0.176	–	–	free demo
5	prima-ratio + 12B §	general, zero-shot	0.702	0.564	0.234	0.146	700 ms‡	local GGUF
6	OpenDecider-small §	general, zero-shot, open weights	0.671	0.211	0.117	–	40 ms‡	open weights
7	Bongard-mini §	general, zero-shot, open weights	0.594	0.256	0.132	0.067	225 ms‡	open weights
8	Jeff-Gemma4-E2B	general, zero-shot, open weights	0.561	0.403	0.219	0.188	2,272 ms†	open weights
9	Jeff-Qwen3.5-2B	general, zero-shot, open weights	0.511	0.460	0.237	0.203	1,346 ms†	open weights
10	Jeff-Qwen3.5-0.8B	general, zero-shot, open weights	0.483	0.679	0.313	0.251	662 ms†	open weights
–	Prior (ignores the input)	reference	0.470	0.347	0.189	0.088	–	–
–	Uniform (same probability on every option)	reference	0.308	0.444	0.238	0.169	–	–

Every row was scored by sending the whole case (state plus all five questions) in one request. Request shape matters: in a third-party run Jev's yes/no accuracy was 0.843 with one question per request and 0.788 alongside the others.

Hosted rows are p50 end to end from a client, one request at a time. † Measured on the same machine as the model (M3 Max), not comparable with hosted latency. ‡ Reported by the submitter on their own hardware (a different GPU and a different unit for each), not comparable with the other rows. § Self-reported by the submitter in the linked discussion and not re-run by us. Metric definitions follow the submitter's report; ECE in particular is computed differently by different submitters.

Fitted or fine-tuned on train

These models were trained on this benchmark's train split, so they are not zero-shot and their scores are not comparable with the table above. All were self-reported in the linked discussions and were not re-run by us, except the two specialists marked ours and the three Bekko rows, which we scored ourselves. Scores well above the 0.735 teacher self-agreement mean a model is learning the teacher's quirks.

Model	Kind	Accuracy ↑	KL from gold ↓	Brier ↓	ECE ↓	Reported latency
OpenDecider-large-td (Qwen3-Next-80B-A3B + LoRA)	general, then fine-tuned on train	0.801	0.081	0.044	–	440 ms‡
od1-typed-decisions (Qwen3.5-4B)	specialist	0.797	0.082	0.045	0.156¶	–
OpenDecider-nano (400M encoder)	general, then fine-tuned on train	0.796	0.079	0.043	–	17 ms‡
OpenDecider-small-td (Qwen3-4B + LoRA)	general, then fine-tuned on train	0.792	0.080	0.043	–	40 ms‡
OpenDecider-medium-td (Qwen3-30B-A3B + LoRA)	general, then fine-tuned on train	0.788	0.081	0.044	–	214 ms‡
soft-decider-421m	specialist	0.774	–	–	0.141¶	50 ms‡
Laya typed-decisions	fitted on train	0.766	–	–	–	–
Bekko System One v0 400M, scored by us	trained on train and other data	0.668	0.214	0.113	0.143	808 ms†
ModernBERT-base (149M), ours	specialist, fitted per workflow	0.646	0.223	0.119	0.179	349 ms†
MiniLM-L6 (22M), ours	specialist, fitted per workflow	0.587	0.262	0.143	0.108	22 ms†
Bekko System One v0 68M, scored by us	trained on train and other data	0.537	0.293	0.160	0.116	176 ms†
Bekko System One v0 17M, scored by us	trained on train and other data	0.483	0.344	0.203	0.136	33 ms†

¶ The submitter's own ECE definition, which does not match the one used above.

meraGPT Decider 1 is state of the art among zero-shot models on this benchmark. It leads Jev on every question type (noul 0.840 vs 0.775, choice 0.733 vs 0.720, score 0.739 vs 0.696), its distributions sit far closer to the gold (KL 0.096 vs 1.442), it is faster end to end, and it costs less per token. It answers POST /v1/systemone at meragpt.com, so the typesafe-sdk works against it by setting TYPESAFE_BASE_URL.

To add a model, score it on test with the full distributions and open a discussion with the numbers and the mode (specialist or general) it used.

Notes on the rows

• Liquid AI d1 was measured on 2026-09-30 through Liquid's API (https://api.liquid.ai/v1/systemone, model: d1:free), with the same client code as the Jev row: all 2,000 decisions, zero errors. It ties Decider 1 on noul (0.840) and choice (0.732 vs 0.733) and trails on score (0.677 vs 0.739), and it beats Jev on accuracy and KL. Liquid lists no per-token price yet, so the row shows the free tier.
• Jev 1.13.0 was measured on 2026-09-18 through TypeSafe's API (jev-latest, which reported jev-1.13.0): all 2,000 decisions, zero errors, $0.016 in total. Its accuracy is near the 0.735 ceiling, but it puts nearly all its probability on one answer, which is where the KL gap comes from. Its confidence is not badly calibrated (overconfidence +0.023); the gold is a three-sample spread it does not reproduce.
• Jeff (firelex/jeff, Apache-2.0) was scored on 2026-09-29 through the same client code as Jev, against Jeff's own jeff-serve (commit 2c1bfce) with the calibration each checkpoint ships. All three clear the Prior on accuracy but not on KL. Jeff's own 83.1% comes from a different five-benchmark panel. If there is a better way to serve them, open a discussion and we will rescore.
• Self-reported rows (§ above, and the second table) come from discussions #5 (prima-ratio), #7 (Bongard-mini), #8 (OpenDecider), #4 (od1), #3 (soft-decider) and #2 (Laya). Each submitter states the mode; we have not re-run them. If a number looks wrong, say so in the discussion and we will correct it.
• Bekko System One v0 (hotchpotch) was scored by us on 2026-10-01 on CPU (M3 Max, one call per case with all five questions, pinned revisions b886a1f9 17M, ab7685f2 68M, 1960df56 400M), with the same scorer as the other rows. Its training data includes this benchmark's train split, with our teacher labels, so it sits in the second table; none of our 400 test cases are in its training data. We mapped our option and rubric text onto its candidate format in our option order. Its model cards say the license is not yet finalized. The author invited a score on X; we are glad to re-score with a different input mapping if he prefers.
• Specialists use Adaptive Classifier 0.2.0, one classifier per question on a frozen encoder, tuned on a held-out quarter of train (mean pooling, max_length 512, 30 epochs, prototype_weight 0.3). Each case is entered four times, split across labels in proportion to its gold, so the soft target survives hard-label training; that cut KL by a third.
• Prior answers each question's train label frequencies for every case. It has the best ECE while knowing nothing, which is why KL and Brier are the columns to read, not ECE.

Reading a score

Gold is the mean of three samples from a teacher of roughly 4B-class capability, so a score measures agreement with that teacher, not correctness. A better model can score lower wherever the teacher is wrong (it missed a duplicate invoice whose ID matched an earlier one).

Reference	Accuracy	What it is
Prior	0.470	the floor: label frequencies, ignoring the input
Perfect factor recovery	0.704	a model fitted to the latent factors that generated each case
Teacher self-agreement	0.735	a fresh teacher sample against gold built from the others

Scores well above 0.735 mean a model is learning the teacher's quirks. Per-question ceilings vary from 0.560 (agent_trace/urgency) to 0.937 (customer_service/category), so read scores per question as well as on average.

Specialist and general numbers are not comparable. A specialist is fitted on train for these four workflows and cannot answer anything else. A general model takes any question schema at request time and has never seen these. Say which mode you used; the gap between them is the price of generality, not a quality ranking.

The data

Type	Answer	Shape
noul	yes/no	probability that the statement is true
choice	one of N labels	distribution over labels, plus confidence
score	an ordered rubric	distribution over levels, plus an expected score

Every option carries a written description in criteria, and the descriptions are part of the input.

Workflow	Decision	Train	Test
agent_trace_observability	Does an agent run need human review, and how urgently?	300	100
customer_service	The right response and action for a customer thread and account.	300	100
invoice_processing	Pay, hold or reject a vendor bill against its order and delivery.	300	100
security_incidents	Close, investigate or contain a security alert, given machine history.	300	100

state and questions together are exactly the body of a POST /v1/systemone request. gold holds the full gold distributions, and flat <question>__label / __probabilities / __score / __probability_true columns hold the same answers for convenience. factors and label_agreement describe how the case was built and are not model input.

from datasets import load_dataset
import json

ds = load_dataset("LocalLLaMA/typed-decisions", "customer_service", split="test")
row = ds[0]
state, questions, gold = (json.loads(row[k]) for k in ("state", "questions", "gold"))
print(gold["urgency"]["probabilities"])   # score against the full distribution

Report KL or log loss and Brier next to accuracy; calibration is the point.

How it was built

Each case starts from independently sampled latent factors (topic, tone, severity, discrepancy type and so on), which a model renders into free text where the artefact is textual and keeps structured where it is structured. A teacher labels each case three times at temperature 0.7, and the gold is the mean of those distributions, so it stays soft where a decision is genuinely ambiguous. Before release, an audit checks state diversity, label balance, and that the gold actually tracks the input. train comes from a separate run at a different seed; packaging refuses to build if any case id or state appears in both splits.

The write-up behind the benchmark, including the two bugs it exposed in the classifier library: Typed Decisions on Latent Node.