The Short Answer
Several computability-theory papers argue LLM hallucination is inevitable rather than a bug awaiting a fix, so a contract specifying a hallucination rate of zero buys an impossibility. The leading paper also proposes the mitigation in its own title: retrieval modelled as an oracle. On ibl.ai you own all the code and the data, so grounding, abstention and the audit trail are yours to specify.
Two things need separating here, because the claim circulating is stronger and newer than the research it rests on.
What does the research actually say about hallucination being unfixable?
The paper being described is "Hallucination as a Computational Boundary: A Hierarchy of Inevitability and the Oracle Escape", by Wang Xi, Quan Shi, Zenghui Ding, Jianqing Gao and Xianjun Yang.
It formalizes a large language model as a probabilistic Turing machine and constructs what the authors call a computational necessity hierarchy. On that basis it argues hallucination is inevitable at three boundaries: diagonalization, incomputability, and information theory.
It is not an isolated result. Banerjee and colleagues argued in "LLMs Will Always Hallucinate, and We Need to Live With This" that hallucination follows from the mathematical structure of these models, drawing on Gödel's first incompleteness theorem.
A third paper reduces hallucination detection to the halting problem, which is undecidable.
So the direction of the research is real, and it is a body of work rather than a single viral finding.
Is this new research from this week?
No, and this is the first correction worth making plainly.
The paper was submitted to arXiv on 10 August 2025 and revised on 8 December 2025. At the time of writing it is well over a year old in its first version.
Descriptions calling it new, or saying it "went viral this week", are attaching recency to something that has been in the literature for a while.
The engagement counts circulating alongside it are a measure of attention, not of evidence, and a post on X is not a source for a claim about what a paper proves.
None of this weakens the finding. It does change what you should conclude from the fact that it is being discussed now, which is that the procurement implications are finally being noticed, not that the mathematics just landed.
Does the research really say hallucination can never be fixed?
This is the second correction, and it is the one that matters for anyone making a decision.
Read the paper's title to the end. "The Oracle Escape."
The same authors who argue inevitability propose the mitigation: model retrieval-augmented generation as an oracle machine, and continuous learning as an internalized oracle. The paper is structured as a limit and a way to work within it.
"Inevitable" and "unmitigable" are different claims. A system that cannot be made perfect can still be made accountable, bounded and cheap to check. That is the ordinary condition of every safety-critical system ever fielded.
So "researchers proved hallucinations can NEVER be fixed" overstates a paper that offers an escape in its own title. The defensible version is narrower and more useful: no model-level accuracy threshold is purchasable, so stop writing contracts that specify one.
Why do government AI contracts specifying accuracy thresholds fail?
Because they name a quantity no vendor controls and no test can certify for the queries that matter.
The pattern to watch for, stated here as illustrative shapes rather than quotations from any particular solicitation, looks like this:
- A maximum hallucination rate, expressed as a percentage
- A minimum factual accuracy figure on domain-specific queries
- A zero-tolerance clause on citations to statute or regulation
Each one has the same three problems. The threshold is unachievable in the limit the research describes. It is measured against a benchmark that is not the agency's actual query distribution. And it places the burden of proof on a number rather than on an inspectable record.
There is a practical failure mode too. A vendor who accepts a zero-hallucination clause has either not read the literature or has decided the clause is unenforceable. Neither is the counterparty an agency wants.
A related procurement blind spot, where security certifications crowd out any test of whether the system can do the work, is in Government AI Procurement's Blind Spot.
What should an agency specify instead of an accuracy threshold?
Specify the architecture that makes an error visible, attributable and correctable. Four requirements, all testable at acceptance.
| Requirement | How you test it at acceptance |
|---|---|
| Grounding in approved sources | Every response carries the retrieved passage and its source document, from a corpus the agency controls |
| Citation at passage level | A reviewer can open the cited statute or policy and read the sentence relied on, without a second search |
| An abstention path | Ask questions the corpus cannot answer; "not answerable from the record" must be a success, not a penalty |
| Complete audit trail | Query, retrieval set, model version and response are reproducible months later, by the agency, unaided |
Add a fifth that is commercial rather than technical: model portability. The error floor differs between models and moves with every release, so the ability to switch without rewriting the system is how an agency responds to that without re-procuring.
Abstention deserves emphasis because it is the requirement most often missing. A system rewarded only for answering will answer, and the research above says the answer will sometimes be wrong. A system permitted to decline converts an invisible error into a visible gap.
The same audit-trail argument applied to regulatory filings is in When Compliance AI Hallucinates, Who Audits the Filing?.
Where does ibl.ai fit when hallucination cannot be eliminated?
On ibl.ai you own all the code and the data. The retrieval layer, the evaluation harness and the audit log run inside your own perimeter, model-agnostic across any LLM, with no per-seat pricing.
The reason that follows from the research rather than being bolted onto it: if hallucination is a property of the architecture, then your real control is the layer around the model. Grounding, citation, abstention and logging are all that layer.
A vendor-hosted system can offer each of those as a feature. It cannot offer them as something you administer, and at acceptance the difference is whether you can reproduce last quarter's answer yourself.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
Want a grounded AI stack you can audit yourself?
We deploy the retrieval layer, abstention policy and audit trail as source code you keep, in your cloud, on-premise, GovCloud, or fully air-gapped. Book a 30-minute demo or talk to the ibl.ai team. ibl.ai is family-owned and operated from New York, NY.
Sources: the paper's title, authors, probabilistic-Turing-machine formalization, the three inevitability boundaries, the submission date of 10 August 2025, the 8 December 2025 revision and the oracle-escape mitigation are from arXiv:2508.07334. The Gödel-based argument is from arXiv:2409.05746. The reduction of hallucination control to the halting problem is from arXiv:2506.06382.