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Yes, but only in one configuration. In a small benchmark published on 30 September 2026, a local model called Laya returned typed answers with a 16 ms median latency and a 19 ms p95 on an NVIDIA DGX Spark. That is a real measurement for that setup. It does not show that typed AI decisions are always fast, always correct, or free of hallucinations. Laya was also the least accurate of the four systems tested, and the sub-35 ms result applies to that one system only.
What a typed decision is
A typed decision is an answer with a fixed shape: a yes or no, a choice from a closed list of options, or a score on an ordinal scale. The model does not write a sentence that a program must then interpret. Application code reads the value directly. The benchmark’s author, Mohamed Fathir, put the practical benefit simply:
“There is no text generation, so there is nothing to parse.”
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In the benchmark’s design, the model receives a state (a message, a document, or a JSON record) plus one or more typed questions. It returns a probability distribution for each question in a single forward pass. The benchmark’s example questions include “Is this true?” (a yes/no question), “Which of these options?” (a fixed-option choice), and ordinal score questions.
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This describes an output interface and a computation pattern. It does not mean the input needs no language understanding, and it does not mean the model cannot be wrong. The typed format fixes what the answer looks like. It says nothing about whether the answer is true.
Three designs that are often confused
“Typed,” “single-token,” and “structured” are used loosely in AI writing, and they describe different systems. Keep them separate when you evaluate a product or a paper.
No text generation: typed decision outputs
This is the design described above. The model returns probability distributions over the answer options for each typed question, so no prose answer is produced and nothing needs to be parsed out of free text.
Single-token classification
The Koa-action paper, by Shenghong Dai and colleagues (arXiv, 28 September 2026), maps each atomic label to a special token and fine-tunes the model so that the decision appears as a single output token. That cuts multi-token decoding, but the system still emits a token. It does not emit no output at all. A single-token approach and a no-generation approach are therefore different designs, even though both avoid long answers.
Constrained structured generation
Here the model still generates tokens, but the decoder is restricted to a grammar or schema, such as a JSON structure. The output is guaranteed to be well-formed. Whether it is correct is a separate question, covered below.
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What the practitioner benchmark measured
The comparison covers 84 decisions. The author assigned the correct labels personally, so the set is small and author-labeled. Its results describe that benchmark and nothing broader. The local measurements were run on an NVIDIA DGX Spark. The benchmark uses that hardware as a reference point; it does not present the DGX Spark as a requirement for typed decisions.
| System | Where it ran | Median latency | p95 latency | Accuracy on 84 decisions | Other reported result |
|---|---|---|---|---|---|
| Laya | Local, NVIDIA DGX Spark | 16 ms | 19 ms | 71% (60/84) | Not stated |
| Kev-4B | Local, NVIDIA DGX Spark | 74 ms | 86 ms | 86% (72/84) | 74% of cases automated with zero errors under the author’s act-or-escalate policy |
| Jev (TypeSafe) | Hosted, measured over the network | 355 ms | Not stated | 100% (84/84) | Not stated |
| GPT-5.4-mini structured output (baseline) | Not stated | 888 ms | Not stated | 96% (81/84) | Not stated |
Three things stand out. Only Laya is under 35 ms at the median. Jev, which TypeSafe offers as a hosted model, is the most accurate system in the table, but its median includes network time and it is more than ten times slower than Laya. Jev’s perfect score is also a small-sample result: one miss in 84 decisions would have produced 98.8% rather than 100%.
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The 74% automation figure for Kev-4B depends on the threshold and routing rules the author set. It is not a general rate for any deployment.
Why latency alone does not settle the question
A decision system has to be fast and right. The benchmark shows how much the two can diverge.
The speed and accuracy trade-off
Laya’s median is the lowest in the table, but it got 24 of the 84 decisions wrong. Kev-4B took 74 ms at the median and got 12 wrong. GPT-5.4-mini took 888 ms and got 3 wrong. Jev made no errors on this set but took 355 ms at the median over the network. The right trade-off depends on what a wrong decision costs. A fast system that routes a fifth of its decisions incorrectly needs a different safeguard than a slower one that rarely errs.
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Medians, tail latency, and what is included
A median tells you what a typical request experiences. The p95 tells you what the slowest 5% experience. Laya’s p95 is 19 ms and Kev-4B’s is 86 ms, so Kev-4B’s tail is wider relative to its median. A median below a target does not mean every request meets it. Also check what the timer includes. The benchmark’s hosted figures include network effects. Before comparing any two numbers, confirm whether each one covers only model inference or also the network, serving layer, and parsing step.
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Typed models return probabilities, and it is tempting to treat a high probability as a reliable signal. The benchmark’s own confidence analysis warns that model-provided confidence can be unreliable. Before you set a threshold for automatic action, measure on your own data whether a stated confidence of, say, 0.9 corresponds to being right about nine times in ten.
Speed, correctness, and hallucinations
Removing text generation removes one failure point: a model that writes an explanation you then have to parse. It does not remove the possibility of a wrong answer. The benchmark records errors for every system except Jev, and an error in a typed answer is still an error.
The word “hallucination” also needs a definition before it can be measured. None of the benchmarks discussed here defines it for the decision tasks they test. Without that definition and task-specific evidence, a claim that a system is “hallucination-free” is not supported. A workable definition for a classification task might be a decision made with support that is absent from, or contradicts, the input. You would then count those cases directly.
Constrained decoding has a related limit. The IJCAI 2026 paper StructureBench evaluated 11 on-device language and vision-language models with between 0.5 billion and 8 billion parameters. Its abstract reports that constrained decoding enforces syntactic validity but does not reliably improve semantic accuracy, and that it may reduce accuracy for smaller models or complex grammars. A schema-valid output can still be the wrong answer.
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The benchmarks compare options that differ in where they run, what they return, and where their risks lie.
| Option | Evidence in these sources | Main trade-off |
|---|---|---|
| Hosted decision model (Jev) | 100% on 84 author-labeled decisions; 355 ms median over the network | Network time puts it well outside a 35 ms target in this test |
| Local specialized decision model (Laya) | 16 ms median and 19 ms p95 on an NVIDIA DGX Spark; 71% accuracy | Fastest measured, but the most errors (24 of 84) |
| Local specialized decision model (Kev-4B) | 74 ms median and 86 ms p95 on an NVIDIA DGX Spark; 86% accuracy | Above 35 ms in this test; results depend on the act-or-escalate policy |
| General LLM with structured output (GPT-5.4-mini baseline) | 96% accuracy on 84 decisions; 888 ms median | Slowest measured; output is still generated tokens |
| Single-token classifier (Koa-action approach) | 85.5% accuracy and 0.53 s median end-to-end on a different production intent-routing benchmark | Different task and system, so not directly comparable to the table above |
| Constrained structured generation | Syntactic validity enforced; semantic accuracy not reliably improved (StructureBench, IJCAI 2026) | May reduce accuracy for smaller models or complex grammars |
When you compare options, use the same axes for each: end-to-end p50 and p95 latency with hardware, runtime, concurrency, and network stated; accuracy on your own task and label set; calibration and escalation behavior; the output form; how easily a new task can be added without retraining; and schema validity measured separately from correctness.
Deployment checklist
- Write each decision as a typed question with a closed set of answers, and define every label in writing before you test anything.
- Build a held-out set of your own real decisions, labeled independently of the model’s outputs. A set of 84 items can reveal gross failures, but it leaves wide uncertainty around any accuracy figure.
- Measure end-to-end p50 and p95 under your expected concurrency. Include network, serving, and parsing time, and record the hardware and runtime.
- Report accuracy per label and per error type, not just one overall score.
- Check confidence calibration on your data by comparing stated confidence with observed accuracy in bands, before you choose a threshold.
- Set an act-or-escalate threshold and measure the automation rate and the error rate together. A high automation rate with a rising error rate is a failure.
- Validate output structure separately, by counting schema-valid outputs, and do not read a high valid-output rate as evidence of correct decisions.
- Repeat the full test after any change to the model, prompt, hardware, runtime, or traffic pattern. A result from one configuration does not carry over automatically to another.
Sub-35 ms typed decisions have been measured in a local, configuration-specific benchmark. Whether your decisions can meet that target, and whether they can be trusted at the rate you need, depends on your own latency and accuracy measurements.
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