Provenance Is Not Currency: The Conflation at the Heart of Legal AI Grounding

Ask any legal AI vendor whether their system hallucinates, and you will get some version of the same answer: no, because every claim is grounded in a retrieved source, with a citation you can click and verify. This has become the industry's working definition of solved. Retrieval-augmented generation replaced free-form generation, citations replaced bare assertions, and the hallucination problem was declared, if not solved, at least contained.

It is not contained. It has been narrowed to exactly one of two properties a grounded legal claim needs, while the second, arguably harder, property is left almost entirely unaddressed. The industry has solved provenance. It has not solved currency. And because these two properties look identical from the outside, a system that only has the first produces answers that are indistinguishable, on the page, from answers that have both.

This is the conflation this article is about, and it is worth being precise about it, because the fix for one problem does nothing for the other.

Two claims wearing one word

When a legal AI system says an answer is "grounded," it is implicitly making two separate factual claims, and it is worth separating them completely.

Provenance is the claim that a cited source exists and says what is being attributed to it. This is a claim about the present state of a document: does Article 1124 of some civil code exist, and does its text actually support the proposition the model attached to it. Provenance failures are what people mean when they say "hallucinated citation": a case that was never decided, a statute that was never enacted, or a real source stretched to support a claim it does not actually make.

Currency is a different claim entirely: that the cited source is still valid law, right now, or at whatever date the analysis is pinned to. Not repealed. Not superseded by a later act. Not amended in a way that changes the operative text. Not overruled, distinguished into irrelevance, or limited by subsequent authority. Currency is not a claim about the document. It is a claim about the document's relationship to everything that has happened to the law since the document was written.

These are independent axes. A citation can have perfect provenance and zero currency: the source is real, the quotation is accurate, and none of it matters because the provision was repealed eighteen months ago. A citation can, in principle, have imperfect provenance and still gesture at something with currency, though this case is rarer and less interesting. The two failure modes require entirely different detection strategies, and this is the part the industry has not absorbed.

Why RAG-with-citations only buys you the first one

Retrieval-augmented generation earned its reputation as the fix for hallucination because it directly targets provenance. Embed the query, embed the corpus, retrieve the nearest passages, generate an answer conditioned on those passages, and attach the passage as a citation. This is a genuinely effective architecture for making sure the model is talking about something that exists rather than inventing it from parametric memory. It is a real advance, and it is not what this article is arguing against.

But look at what the retrieval step is actually optimizing for: semantic similarity between the query and a passage in the corpus. Nothing about that similarity score encodes whether the passage is still in force. A repealed article and the amendment that repealed it can sit a few tokens apart in the same consolidated text, or in adjacent database rows, and their embeddings will be close to identical, because they describe the same subject matter in similar language. A precedent that was the controlling authority for twenty years and the appellate decision that overruled it last year will frequently share more vocabulary with each other than either shares with an unrelated but currently valid provision. Embedding distance measures aboutness. It does not measure validity. There is no mechanism in a standard vector retrieval pipeline that would cause a stale source to score lower than a current one, because staleness is not a semantic property of the text. It is a property of the text's position in a timeline the embedding never sees.

This is the structural reason RAG systems can pass every provenance check and still confidently serve dead law. The citation is real. The passage does say what is claimed. The retrieval worked exactly as designed. And the answer is wrong, in a way that no amount of re-reading the cited passage would reveal, because the passage itself gives no indication that it has been superseded. Superseding is not written into the superseded text. It is written into a different document, possibly in a different register, official gazette, or reporter, that the retrieval step had no particular reason to also surface.

A worked illustration

Consider, illustratively, a civil code provision on contractual limitation periods. Suppose it originally set a term of fifteen years, and a legislative reform some years later shortened it to five, with a transitional rule for contracts predating the reform. (This pattern is not hypothetical: Spain's Ley 42/2015 shortened the general limitation period for personal actions under Article 1964 of the Código Civil from fifteen years to five, with exactly such a transitional regime.) A corpus that ingested the consolidated code correctly will contain the current five-year text. But if the corpus, or the embedding pipeline, or a cached intermediate representation, retained a superseded version, a chunk from an older scrape, an academic commentary quoting the old rule, a lower court decision applying it before the reform, then a retrieval system has no structural reason to prefer the current text over the superseded one. Both chunks are topically identical: both are, verbatim, about the limitation period for this class of contract. The similarity score cannot distinguish them, because the thing that distinguishes them, one is in force and one is not, is not encoded in either passage's text. It is encoded in a fact external to both: the date of the reform, the date of entry into force, and the transitional regime governing which contracts each version applies to.Sources: Ley 42/2015 (BOE)

Now extend this to case law, where the equivalent failure is sharper because there is no consolidated text to fall back on. A precedent is overruled not by editing the original opinion but by a separate, later opinion saying, in whatever the jurisdiction's convention is, that the earlier case no longer represents the law. The original opinion's text does not change. It sits in the corpus, fully citable, reading exactly as persuasively as it did the day it was decided, with nothing on its face to indicate that a court eighteen months later took the opposite view. A retrieval system built only for provenance will surface it, cite it accurately, and quote it faithfully, all while serving law that a first-year associate with access to a citator would catch in thirty seconds.

That thirty seconds is doing more work than it looks like. It is the entire currency problem, compressed into a single manual check that almost no automated pipeline performs.

The infrastructure that already solves half of this, and the half it does not touch

Legal informatics has spent decades building identifier and structure standards, and it is worth being fair to what they actually cover. ELI (the European Legislation Identifier) and ECLI (the European Case Law Identifier) give every piece of legislation and every judicial decision a stable, resolvable identity. Akoma Ntoso gives legal documents a structured XML representation, distinguishing preamble from operative text, articles from paragraphs, amendments from the base text. EUR-Lex's consolidation service, surfaced through dated CELEX identifiers, does something closer to what currency requires: it tracks consolidated versions of EU legislation and exposes, for a given date, which version of a regulation or directive was in force.Sources: ELI (EUR-Lex) · ECLI (Council conclusions, 2011) · Akoma Ntoso · EUR-Lex consolidated texts

This is real infrastructure, and it solves provenance excellently. An identifier resolves to a document. A structured representation tells you which part of the document you are looking at. None of this, on its own, tells a consuming system whether that specific provision, resolved at that specific identifier, was still good law on the date the question is being asked.

Where this problem has been solved, historically, it has been solved by proprietary, manually curated citator services. Shepard's Citations and KeyCite exist precisely because currency does not fall out of having the text; it requires a separate graph of subsequent treatment, built by an army of editors reading every new decision and classifying its relationship to every earlier one it cites: followed, distinguished, limited, criticized, overruled. This has worked, for over a century, in a handful of common-law markets, at enormous ongoing editorial cost, and it does not exist in a form that scales to sixty jurisdictions of legislation and case law simultaneously. Nobody has built the equivalent of Shepard's for legislative currency across sixty legal systems, because nobody has needed to until an AI system started confidently answering questions across all sixty at once.

What a currency-aware architecture actually requires

If provenance is solved by an identifier resolving to a document, currency is solved by a graph, and it needs two structurally different graph types, because legislation and case law are superseded through different mechanisms.

For legislation, the graph is a versioned amendment chain. Each node is a version of a provision, valid over a specific interval. Edges represent amendment (this version replaces that one, effective on this date), derogation (this provision is suspended, not repealed, under these conditions), and repeal (this version terminates the provision's validity entirely). Transitional regimes are a third kind of edge, because they specify which version applies to facts arising before a given cutoff, which is exactly the case that trips up a system that only knows the current text. A currency query against this graph is not a search. It is a point-in-time lookup: given a provision and a date, which version was in force, and what does that specific version say. This is the same information EUR-Lex's consolidation apparatus already tracks for EU law. It does not yet exist, in any systematic form, for most national legal systems, and it does not exist at all as a queryable API most retrieval pipelines can call.

For case law, the graph is a citator graph, and the edges carry a harder problem: they require classifying the type of subsequent treatment, not merely its existence. A later case that cites an earlier one might be following it, distinguishing it on the facts, limiting its holding to a narrower class of cases, questioning its reasoning without formally overruling it, or overruling it outright. These are not equivalent, and treating "cited by a later case" as a single undifferentiated signal is nearly as unhelpful as ignoring subsequent treatment altogether. Automatically classifying this relationship is a genuinely hard natural language inference problem: it requires reading the later opinion's actual treatment of the earlier one, not just detecting that a citation exists. Entailment models can be trained to do a reasonable first pass on the more overt signals of overruling and distinguishing, in the language courts conventionally use to say so, but this is a genuinely unsolved research problem in the general case, and any production system should say so honestly rather than pretending a citation graph plus a similarity score constitutes a validity check.

The result, when both of these exist, is that a currency check stops being a research task and becomes a graph traversal: given a claim citing source S, is there an active edge, dated before the query date, that removes S's validity, whether by amendment, repeal, or overruling. That traversal is a fast, deterministic, machine-checkable operation. Without the graph, it is not a slow version of the same check. It is not performed at all. The system has no data structure in which the question "is this still good law" is even representable, so it defaults, silently, to assuming yes.

Why this compounds worse than fabrication

There is a reason to treat currency failures as more dangerous than provenance failures, not less, and it is worth stating directly because the industry's attention runs the other way.

A fabricated citation is, at least in principle, checkable by existence. Does this case exist. A single lookup against any case reporter answers the question, and a diligent reviewer, or an automated existence check, catches it. It is an embarrassing, sanctionable, well-publicized failure mode, and it is also, structurally, the easier of the two to defend against, because the check is binary and the ground truth (does this case exist in this reporter) is unambiguous.

A stale citation passes that exact check. The case exists. It was decided by the court it says it was decided by, on the date it says, saying exactly what is quoted. Every provenance check available to a reviewer who does not also run a currency check will return a clean result. The only way to catch it is to already know, or to look up, whether something happened to that authority afterward, which is precisely the piece of information that does not live inside the cited document itself. This is why an experienced lawyer's instinct to reach for a citator is not habit; it is a recognition that provenance and currency are different questions requiring different tools, and that the first tool cannot answer the second question no matter how carefully it is applied.

It gets worse under composition. Legal reasoning is rarely a single claim; it is a chain, where a risk assessment or a drafting recommendation rests on three or four premises, each citing its own authority. If one premise in the chain is stale, provenance-clean but currency-dead, the defect does not stay local. Every downstream inference built on that premise inherits the error silently, because each individual inferential step, examined on its own, looks locally sound. The chain is only as good as its weakest link, and a currency failure is a weak link that gives no visible sign of weakness anywhere in the finished output. A reviewer reading the final memo, checking that each citation is real and says what it is claimed to say, will find nothing wrong, because nothing about provenance is wrong. The defect lives entirely in the layer the review never touched.

What "grounded" should actually mean

If provenance and currency are genuinely separate properties, a system's claim to be grounded should decompose into at least three checkable statements, not one: this source exists, at this identifier, resolvable independent of the model's say-so. This source's text supports this specific claim, at the level of the cited passage, not the general topic. And this source was valid law at the relevant date, verified against a structured record of everything that has happened to it since it was written, not inferred from the absence of contrary signal in the passage itself.

Most of what the market currently calls a grounded legal AI system satisfies the first two and is silent on the third, not because the third is unimportant, but because it requires infrastructure that does not yet exist for most of the world's legal systems: point-in-time versioned legislation graphs and automatically or semi-automatically maintained citator graphs, spanning jurisdictions at a scale no single proprietary service has ever attempted. Building that infrastructure is a categorically different project from building a better retriever or a better citation formatter. It requires treating legal validity as a first-class, temporal, graph-structured fact, tracked continuously as law changes, rather than as a property that can be read off the text of the law itself.

Until that exists, every grounded legal AI system is making a claim it cannot actually verify. It can tell you where an answer came from. It cannot yet tell you, with the same confidence, whether where it came from is still where the law lives.