Decision-1.0-Lux-9B

Lux, Latin for light.

Give Lux evidence, questions and possible answers. It returns decisions and probabilities for labels you define at runtime.

Decision family

Type	Use it for	Output
Choice	Route a request or choose among 2–255 actions.	Selected ID + probability distribution
Noul	Judge a condition against supplied evidence.	P(true)
Score	Apply 2–10 ordered rubric descriptions.	Expected index + probability distribution

Measured capability

77.40% weighted accuracy across 3,766 decisions and 54 tasks: +5.51 points over Kev-9B and +4.32 over Nox-4B on the same benchmark.

Model	Size	Decisions	Composition	Reading	Inference	Transfer	Overall
Lux-9B	9B	84.38	52.75	90.16	91.46	77.72	77.40
Nox-4B	4B	83.00	51.79	79.06	86.25	69.60	73.09
Kev-9B	9B	76.75	45.75	86.72	83.54	79.25	71.89
Kev-4B	4B	71.90	48.54	81.88	84.58	76.10	70.09
Qwen3.5-9B	9B	73.91	44.62	89.84	79.58	73.23	69.73
Decider	2B	64.01	46.58	92.03	84.38	69.31	67.71
Qwen3.5-4B	4B	69.89	43.33	87.97	79.79	68.83	67.29
Sol-2B	2B	73.75	46.08	76.56	84.17	57.07	66.32
Eos-0.8B	0.8B	65.94	46.04	70.31	81.67	52.01	61.89
Kev-0.8B	0.8B	60.14	42.29	67.81	68.75	61.19	58.28
Qwen3.5-2B	2B	57.12	39.00	73.75	72.29	56.31	57.24
Kai-0.6B	0.6B	57.96	40.83	54.69	69.79	48.37	53.52
Laya · English	0.421B	56.54	35.33	51.41	63.75	53.06	51.03
Laya · Multilingual	0.322B	47.25	38.92	50.78	57.29	47.13	47.19
Jev	—	79.10	66.38	94.53	89.79	87.19	81.05

Accuracy (%), using the same five-panel decision benchmark. General decisions contribute 30%; composition contributes 25%; reading, inference and external transfer each contribute 15%. Bold marks a Decision model strictly above every external open reference in that column; Jev and other Decision models are excluded from this threshold. Full tasks, uncertainty and comparator identities.

All 54 tasks · Probability quality, order and missing evidence

Use Lux-9B

Use the official TypeSafe Python SDK with your SystemOne-compatible endpoint, configured to serve Decision-1.0-Lux-9B. Replace the example URL and API key with your own.

pip install typesafe-sdk

from typesafe_sdk import Choice, Noul, TypeSafeClient

with TypeSafeClient(
    api_key="YOUR_ENDPOINT_API_KEY",
    base_url="https://your-decision-endpoint.example",
    model="Decision-1.0-Lux-9B",
) as client:
    result = client.system_one(
        state="Customer reports a duplicate charge and asks for a refund.",
        questions={
            "route": Choice(
                instructions="Which team should handle this request?",
                criteria={"billing": "Payments and refunds", "technical": "Product faults"},
            ),
            "refund_requested": Noul(instructions="Did the customer request a refund?"),
        },
    )
    print(result.choices["route"].choice)
    print(result.nouls["refund_requested"].noul)

The same request with curl:

curl -X POST 'https://your-decision-endpoint.example/v1/systemone' \
  -H 'Authorization: Bearer YOUR_ENDPOINT_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
  "model": "Decision-1.0-Lux-9B",
  "state": "Customer reports a duplicate charge and asks for a refund.",
  "questions": {
    "route": {
      "type": "choice",
      "instructions": "Which team should handle this request?",
      "criteria": {
        "billing": "Payments and refunds",
        "technical": "Product faults"
      }
    },
    "refund_requested": {
      "type": "noul",
      "instructions": "Did the customer request a refund?"
    }
  }
}'

Typed request and response guide · Model runtime requirements

More questions, measured

Latency uses the same architecture and runtime, measured with earlier weights.

Distinct Choice questions, fixed at 499 input tokens per question. Thirty measured requests per point across six fresh processes on an otherwise idle AMD GPU. Python latency includes tokenization, inference and response construction; loading and network are excluded. p95, memory and hardware.

Architecture

A causal Qwen3.5 text backbone combines Gated DeltaNet and full attention. A shared candidate head reads contextual candidate endpoints and the final query vector, producing one probability per supplied answer.

Candidate head · Vector architecture · Model details

The full state, question and candidates must fit 16,384 tokens; overflow is rejected. AMD gfx942 is validated; other hardware requires separate qualification. Lux judges supplied evidence without live retrieval, so confidence does not guarantee factual correctness.

License · Attributions · Runtime