A paper titled ‘Vosti: Specifying, Implementing, and Verifying Deterministic LLM Inference’ is listed on arXiv under identifier 2609.38981. By its title, the work concerns deterministic inference for large language models, and it reportedly surfaced only within the last few hours.
The listing gives only the title and a link, so BriefFlash is describing the problem the paper addresses, not its results. The question this article answers is why identical prompts can give different outputs, and what it would take to prevent that. For developers who test and audit LLM-based systems, that is a practical question, not an academic one.
What is the Vosti deterministic LLM inference paper about?
What is confirmed is narrow: the paper is called Vosti, it is on arXiv, and its title names three activities, specifying, implementing and verifying deterministic inference for large language models. The arXiv listing is the primary source for all of that, and nothing beyond the title is established here.
The authors, their institution, the abstract, the models and hardware covered, and any results are all absent from the material we have. So we do not say what Vosti is, whether anyone has tested it, or whether any model or framework supports it. A paper title is not evidence that an approach works or is practical.
What does deterministic LLM inference mean?
Deterministic LLM inference means that the same input and settings always produce exactly the same output. Inference is the process of generating output from a trained model, so the guarantee applies every time a model answers a prompt.
That is stricter than many developers assume. Turning the sampling temperature to zero removes deliberate randomness, but it is not the same thing as deterministic inference. Even with sampling randomness switched off, the same prompt can yield different text on different runs, and determinism is about removing the variation that remains.
Why do identical prompts give different LLM outputs?
The usual suspect is the order of arithmetic, not randomness. Floating-point addition is not perfectly associative, so adding the same numbers in a different order can change tiny results. This is background explanation, not a finding of the Vosti paper.
Inference spreads work across many parallel GPU operations, and servers often group requests into batches. Both can change the order in which numbers are summed. The resulting differences are tiny, but a tiny difference can flip which token the model picks, and the output then differs from an earlier run. The graphic below traces that chain.
There is a consequence worth reasoning through. Batching is one of the factors that can change the order of operations, so it follows that the same prompt might behave differently depending on what else a server is processing at that moment. That is our inference from the mechanism, not a claim about any specific system, and we hold it with moderate confidence.
What would specifying, implementing and verifying involve?
Read in general terms, the three words in the title describe three separate jobs. This is a general reading of the vocabulary, not a description of the paper’s method, which we have not seen.
- Specifying: defining precisely what the correct output is for a given input and settings.
- Implementing: building a system that meets that definition.
- Verifying: checking that the system actually does, instead of assuming it does.
In our view, verification is where claims are easiest to overstate. Getting the same answer twice is a weak test. A stronger claim fixes the definition first and then checks behaviour against it, across the conditions a developer actually cares about.
Why do developers want reproducible LLM outputs?
Developers want reproducible outputs because systems whose results vary between runs are hard to test, debug and audit. Deterministic inference would make results repeatable and easier to verify, which matters in several concrete situations:
- Reproducing a bug that appeared in one run and vanished in the next.
- Running regression tests on a prompt after changing a model or a setting.
- Comparing models fairly, so a score difference reflects the models and not run-to-run noise.
- Auditing system behaviour, including in regulated settings.
The reasoning is straightforward. If output can change with nothing else changing, a failing test cannot be told apart from a real regression, and an audit cannot confirm that the system that was reviewed is the system that ran. Whether Vosti delivers any of this is not yet known.
What we do not know yet about Vosti
Almost everything about the paper’s substance is open. The table lists what the available material does and does not establish.
| Question | Status in the available material |
|---|---|
| Authors and institution | Not stated |
| Abstract, methods and results | Not stated |
| Models, hardware and frameworks covered | Not stated |
| Cost in speed or memory | Not stated |
| Independent assessment or developer reaction | None available |
Two caveats apply to any determinism claim. Strict determinism can slow inference or restrict hardware and batching choices, a trade-off the listing does not address. And determinism on one hardware setup does not guarantee identical results on another.
What should readers check once the paper can be examined?
Readers should check four things once the paper’s claims can be read in full: the cost in speed and memory, how much the result depends on specific hardware, which models and frameworks it covers, and whether anyone outside the authors has verified it.
BriefFlash has applied the same test to other recent LLM papers, including what a drunk-language study claims and leaves open and a separate paper reporting higher jailbreak risk. Until the details are available, a developer who needs repeatable outputs today should treat the Vosti listing as something to read, not as a solution to adopt.
Frequently asked questions
Why do identical prompts sometimes give different LLM outputs?
Often because of arithmetic order, not randomness. Floating-point addition is order-sensitive, and parallel GPU kernels and request batching can change the order in which numbers are summed. The tiny differences that result can flip which token the model chooses, so the same prompt on the same model can produce different text even with sampling randomness turned off.
What does deterministic LLM inference mean?
It means the same input and settings always produce exactly the same output. Inference is the process of generating output from a trained model. A deterministic system gives identical text on every run, which is a stronger guarantee than removing sampling randomness, because other sources of variation, such as the order of arithmetic, can remain.
Does setting temperature to zero make an LLM deterministic?
Not by itself. Setting temperature to zero removes deliberate sampling randomness, but the same prompt can still yield different text on different runs because of effects such as floating-point order sensitivity, GPU parallelism and batching. Deterministic inference is a broader goal: identical output for identical input and settings, every time.
Why would developers need reproducible LLM outputs?
Reproducible outputs make it possible to reproduce a bug, run regression tests on a prompt, compare models fairly and audit system behaviour. If output varies with nothing else changing, a failed test could be noise or a real regression, and an audit cannot confirm that the reviewed system is the one that ran.
What would verifying deterministic inference involve?
In general terms, it would mean first defining precisely what the correct output is, then checking that a system actually produces it. This describes the idea generally, not the Vosti paper’s method, which the available material does not describe. Readers should look for independent verification, hardware coverage and a stated speed cost.
