Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge

• URL: http://arxiv.org/abs/2604.14488v2
• Date: Sun, 19 Apr 2026 10:40:12 GMT
• Title: Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge
• Abstract summary: We call this CAR: retrieving the currently active authority for a semantic anchor q, that is, front(cl(A_k(q))).<n>We evaluate three real-world datasets: security advisories, SCOTUS overruling pairs, and FDA drug records.<n>A GPT-4omini experiment shows RAG produces explicit "not patched" claims for 39% of queries where a patch exists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In law, regulatory regimes for pharmaceuticals and software security, newer authorities can revoke older established ones even when semantically distant. We call this CAR: retrieving the currently active authority frontier for a semantic anchor q, that is, front(cl(A_k(q))). This differs from finding the most similar document by relevance score: argmax_d s(q, d). Theorem 4 characterizes when a set R truly covers the active authority set for q with TCA(R, q)=1, providing conditions necessary and sufficient for any retrieved set R: frontier inclusion (front(cl(A_k(q))) contained in R) and no-ignored-superseder (no superseding document exists in the corpus outside R). Proposition 2 shows that TCA@k <= phi(q) * R_anchor(q) in the worst case over any scope-indexed algorithm, proved by an adversarial permutation argument. We evaluated on three real-world datasets: security advisories (Dense TCA@5=0.270, two-stage 0.975), SCOTUS overruling pairs (Dense TCA=0.172, two-stage 0.926), and FDA drug records (Dense TCA=0.064, two-stage 0.774). A GPT-4o-mini experiment shows Dense RAG produces explicit "not patched" claims for 39% of queries where a patch exists; two-stage cuts this to 16%. Four benchmark datasets, domain adapters, and a single-command scorer are released at https://github.com/andremir/car-retrieval.

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