Relational Response Fields: A General Theory of Black-Box LLM Response Consistency and Recovery

• URL: http://arxiv.org/abs/2608.04552v1
• Date: Wed, 05 Aug 2026 07:43:41 GMT
• Title: Relational Response Fields: A General Theory of Black-Box LLM Response Consistency and Recovery
• Abstract summary: Black-box language-model reliability is commonly pursued by sampling, prompting, voting, verifying, or iteratively revising individual answers.<n>We represent responses to typed transformations of a query as a emphrelational response field (RRF)<n>anchors encode how valid responses must change under paraphrase, scaling, decomposition, or other task symmetries.<n>For relation operator $D$, anchor operator $A$, and at most $k$ corrupted response nodes, we identify $_k(D,A)$ as the intrinsic difficulty of black-box response recovery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Black-box language-model reliability is commonly pursued by sampling, prompting, voting, verifying, or iteratively revising individual answers. We ask a prior question: \emph{what determines whether a collection of black-box responses is recoverable at all?} We represent responses to typed transformations of a query as a \emph{relational response field} (RRF). Edge transports encode how valid responses must change under paraphrase, scaling, decomposition, refactoring, or other task symmetries; anchors encode independently trusted evidence such as execution or a verifier. For relation operator $D$, anchor operator $A$, and at most $k$ corrupted response nodes, we identify $γ_k(D,A)$ as the intrinsic difficulty of black-box response recovery. It is positive exactly when every $k$-node corruption is identifiable; it gives a deterministic stability bound proportional to $1/γ_k$; and a matching two-point minimax lower bound shows that no estimator can improve this dependence. Thus consistency is not truth: relation-only methods are blind to null directions, including shared hallucinations. We derive sparse field-repair algorithms while separating information-theoretic identifiability from the stronger null-space conditions required by convex optimization. Controlled theorem tests and black-box mathematics/code experiments evaluate four theory-fixed consequences: consistency--truth separation, anchor phase transitions, redundancy saturation, and cross-model, cross-task prediction of repair difficulty. The results support $γ_k(D,A)$ as a measurable property of a response-recovery instance, rather than a score attached to one repair heuristic.

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