Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
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Computer Science > Computation and Language
arXiv:2608.09393 (cs)
[Submitted on 10 Aug 2026]
Title:Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
View a PDF of the paper titled Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law, by Rose Cymbler and 2 other authors
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Abstract:We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the applicable version is an earlier or future one. Standard legal RAG treats the corpus as static; we argue legal question answering is a temporally-indexed retrieval problem. We introduce FiscalQA Pro, pairing a versioned corpus of 32,436 article-versions of the French tax code (93 years, 1938-2031) with an all-model-hard temporal-reasoning track: 209 scored, expert-reviewed questions across 33 CGI articles (221 released; twelve flagged out of the answerable scope). At selection time, no evaluated model recovered its date-applicable answer closed-book in any of four sampling draws, and the currently in-force text lacks the gold value for all but one of the scored questions. Answers are scored deterministically via atomic ground-truth "nuggets" (regex and numeric-with-tolerance), never LLM-as-judge: an LLM judge would inherit the temporal bias it is meant to score. Across eleven models (five frontier closed-API systems plus Gemini 2.5 Pro as a substitute entry, and five open-weight), parametric knowledge yields 3.0% mean strict accuracy and RAG over a static current-version corpus 2.7%. Static RAG retrieves the date-applicable version 0% of the time, confidently citing a real but inapplicable version. Our end-to-end retriever over a multi-version index, with no oracle, reaches 98.3% mean strict; an oracle-article ablation reaches 99.1%, locating the residual gap in first-stage recall, not version selection. We additionally release a version-aware jurisprudence dataset of 69,208 citation links, together with the corpus, benchmark, model responses, and pipeline code.
| Comments: | 13 pages, 1 figure, 4 tables. Accepted at the ICML 2026 Workshop on AI for Law (AI4Law), Seoul. Code and data: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| ACM classes: | I.2.7; H.3.3 |
| Cite as: | arXiv:2608.09393 [cs.CL] |
| (or arXiv:2608.09393v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.09393
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law, by Rose Cymbler and 2 other authors
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