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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA typed decision model can return a yes/no probability, a choice among fixed options, or an ordinal score without writing a sentence for application code to parse. In one published benchmark of 84 author-labeled decisions, a local model posted a 16 ms median latency. That is a result for one model on one hardware setup. It does not show that typed decisions run under 35 ms in general, and it does not show that they are free of hallucinations. Speed and output format are separate properties from correctness.
What a typed decision is
In the benchmark’s description, a typed decision takes a state, such as a chat message, a document or a JSON record, plus a set of questions that each have a fixed answer type. The model returns a probability distribution for each question in one forward pass, and application code reads those values directly. The benchmark’s examples fall into three forms:
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- Yes/no: a question such as “Is this true?” returns a probability for each answer.
- Fixed-option choice: a question such as “Which of these options?” returns a distribution across the listed options.
- Ordinal score: a rating on an ordered scale returns a distribution across the levels.
This describes the output contract, not the input. The model still has to understand the state it receives, and it can still choose the wrong option. The benchmark’s author, Mohamed Fathir, puts the interface this way: “There is no text generation, so there is nothing to parse.” That sentence describes what the application receives. It says nothing about whether the answer is right.
Three approaches that are often grouped together
“No text generation,” “single-token output” and “constrained JSON” describe different designs. Each removes a different cost and leaves different risks in place.
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| Approach | What the model emits | What it removes | What it leaves in place | Evidence cited here |
|---|---|---|---|---|
| Typed-decision model | A probability distribution for each typed question, in one forward pass | The prose answer and the parsing step | Semantic errors; confidence values that may be miscalibrated | Fathir, practitioner benchmark, 2026-09-30 |
| Single-token classifier | One special token per atomic label | Multi-token decoding | A generated token is still the decision representation, and the model requires fine-tuning on the label set | Koa-action paper, arXiv, 2026-09-28 |
| Constrained structured generation | A generated object forced to match a grammar or schema | Syntax errors and malformed output | Semantic errors; accuracy may fall for smaller models or complex grammars | StructureBench, IJCAI 2026 |
The three are not interchangeable. A system that emits one token is still a generative model, and a schema-constrained generator still produces text, just text that is forced to fit a shape.
What the practitioner benchmark measured
Fathir’s benchmark, published 2026-09-30 as “Typed Decisions Without Text Generation: Benchmarking Jev, Kev-4B, Laya and an LLM Baseline,” compares three typed-decision models against a general-purpose LLM with structured output. The author describes the set as small, and the labels are his own. Local runs used an NVIDIA DGX Spark. Jev is a hosted model from TypeSafe, and its figure was measured over the network.
| Model | Deployment, as reported | Median latency | p95 latency | Accuracy on 84 decisions |
|---|---|---|---|---|
| Laya | Local, NVIDIA DGX Spark | 16 ms | 19 ms | 71% (60/84) |
| Kev-4B | Local, NVIDIA DGX Spark | 74 ms | 86 ms | 86% (72/84) |
| Jev | Hosted by TypeSafe, measured over the network | 355 ms | Not stated | 100% (84/84) |
| GPT-5.4-mini structured output | General-purpose LLM baseline; deployment not stated | 888 ms | Not stated | 96% (81/84) |
Only Laya’s median falls below 35 ms. Its p95 of 19 ms means 95% of its requests in this run finished within 19 ms. Kev-4B’s p95 of 86 ms means roughly one request in twenty took longer than that. Concurrency and request mix are not reported for these figures, so they should not be read as throughput numbers.
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With 84 cases, each wrong answer moves accuracy by about 1.2 percentage points, and the uncertainty around each percentage is wide. Treat the ordering as indicative of this test set rather than as a stable ranking of the models.
Why latency alone does not answer the question
The fastest model in the table is also the least accurate, with 24 of 84 decisions wrong. Jev was correct on all 84 but was measured at 355 ms over the network. A latency figure tells you how quickly a decision arrives, not whether it is the decision you needed.
Act-or-escalate policies
The benchmark’s more useful result is operational. Under the author’s act-or-escalate policy, Kev-4B automated 74% of cases with zero errors, and the remaining cases were escalated rather than acted on. That figure depends on the confidence threshold and routing rules described in the benchmark, and it should not be assumed to transfer to another task. The policy only works if the confidence value it reads is trustworthy.
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Confidence values need their own test
The benchmark’s confidence analysis warns that model-provided confidence can be unreliable. A confidence of 0.95 is a model output, not a measured probability of being right, until it has been checked against labeled cases. Group predictions by stated confidence, compare each group with its observed accuracy on held-out data, and only then set a threshold.
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What “hallucination” means for typed outputs
Removing generated text removes one failure channel: invented explanations, extra prose, and malformed output that breaks parsing. It does not remove wrong labels. A label can be correctly formatted and still be false for the input. The benchmark does not define hallucination, so define it for your task before making any claim about it. For a fraud-flag decision, for example, it could mean a flag that cites a fact absent from the record.
| Failure | Largely prevented by typed output? | How to check it |
|---|---|---|
| Unparseable or malformed output | Yes, by design, since no prose is generated | Schema check on every response, with failures counted |
| A valid label that is wrong for the input | No | Labeled evaluation set, with accuracy reported per question |
| An overconfident wrong probability | No | Calibration groups and threshold tests on held-out cases |
Constrained decoding enforces form, not truth
StructureBench (IJCAI 2026) evaluated 11 on-device language and vision-language models spanning 0.5B to 8B parameters. Its abstract reports that constrained decoding enforces syntactic validity but does not reliably improve semantic accuracy, and that it may degrade accuracy for smaller models or complex grammars. A schema-valid object can still be semantically wrong.
Latency includes more than the model’s tokens
The Koa-action paper (Shenghong Dai et al., arXiv, 2026-09-28) uses single-token outputs, yet reports 85.5% accuracy and a 0.53-second median end-to-end latency on a production intent-routing benchmark. Its authors are from Salesforce AI and the University of Wisconsin–Madison. That is a different system and task from the practitioner benchmark, so it does not corroborate a 35 ms figure. It does show that output token count is only one part of total latency. The measured path can also include network transfer, serving protocol, queueing and application code.
Deployment checklist
- Write the decision contract. List every question, its answer type (yes/no, fixed choice or ordinal score), and the action each answer triggers.
- Label a set drawn from your own traffic. Include ambiguous and edge cases, and record who assigned each label. A set of 84 is a pilot size, not a standard.
- Measure end-to-end latency at p50 and p95. Start the timer when the request arrives and stop it when application code has a usable decision. Record the hardware, runtime, concurrency, and whether network time is included.
- Report accuracy for each question rather than one overall number, so that a weak question cannot hide behind strong ones.
- Test calibration on held-out labeled cases before setting any act-or-escalate threshold.
- Record both the automation rate and the error rate among automated cases. An automation rate means little without the error rate beside it.
- Count malformed outputs separately, using a schema check in the application.
- Repeat steps 3 to 7 after any change to the model, runtime, hardware, input format or traffic mix.
The Laya and Kev-4B figures above show what a local typed-decision setup can do on one benchmark. Whether your own decisions can meet the same speed, accuracy and error targets is a question only your labeled data can answer.
The Bottom Line
Treat the sub-35 ms result as a hypothesis to test on your own hardware and decisions. The benchmark supports the speed of one local typed-decision model in one configuration. It does not establish accuracy, confidence calibration or hallucination rates for your application.
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