Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact

• URL: http://arxiv.org/abs/2608.13926v1
• Date: Fri, 14 Aug 2026 03:53:21 GMT
• Title: Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact
• Abstract summary: Large language models have made natural language interfaces to databases newly credible, but calibrated text-to-aggregated systems fail in a way that matters for deployment.<n>We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant.<n>A generative shell interprets under input and phrases replies; a deterministic kernel matches fully specified questions against a bounded set of answerable question shapes.<n>The two meet at a confirmation the user reads before any value is computed, and requests the kernel cannot express are declined rather than approximated.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at the point of use from a right one. Where the consumer cannot inspect the generated query, as in enterprise AI deployments and operational dashboards, and increasingly where the consumer is a tool-using agent rather than a person, accuracy alone is insufficient: nothing marks which answers to distrust. This is a reliability problem before it is an accuracy problem. We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns. A generative shell interprets underspecified input and phrases replies; a deterministic kernel matches fully specified questions against a bounded set of answerable question shapes and compiles them to queries by deterministic execution. The two meet at a confirmation the user reads before any value is computed, and requests the kernel cannot express are declined rather than approximated. We call this structural abstention, and distinguish it from the statistical abstention of selective prediction and calibrated confidence: refusal here needs no confidence estimate, because unanswerable requests are unrepresentable. We specify the pattern implementation-independently, give a five-decision recipe and work it across three domains, extend the invariant from returned values to the actions of agentic systems, and report a two-year production case study alongside two generative alternatives, a fine-tuned parser and a tool-retrieval agent. We close against enterprise and reliability benchmarks published since.

Related papers

• ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices [48.36933831982682]
  We introduce ADeptS-Bench, a trustworthiness benchmark grounded in the ADEPTS capability framework.<n>The Safety stream provides paired benign/malicious tasks with threats embedded in the visual interface.<n>The Disambiguation stream evaluates whether agents seek clarification when intent is ambiguous.
  arXiv  Detail & Related papers  (2026-08-25T23:21:57Z)
• AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering [0.0]
  AB-RAG is a training-free and backbone-agnostic framework that generates an answer.<n>It estimates its confidence from a combination of three signals.<n>It then decides whether to stop or to retrieve more evidence.
  arXiv  Detail & Related papers  (2026-06-27T21:08:14Z)
• TopBench: A Benchmark for Implicit Prediction and Reasoning over Tabular Question Answering [80.93487993878836]
  A common class of real-world queries is implicitly predictive, requiring the inference of unobserved answers from historical patterns rather than mere retrieval.<n>These queries introduce two challenges: recognizing latent intent and reliable predictive reasoning over massive tables.<n>We introduce TopBench, a benchmark consisting of 779 samples across four sub-tasks, ranging from single-point prediction to decision making, treatment effect analysis, and complex reasoning.
  arXiv  Detail & Related papers  (2026-04-30T16:22:51Z)
• Controllable Evidence Selection in Retrieval-Augmented Question Answering via Deterministic Utility Gating [0.0]
  This paper presents a deterministic evidence selection framework for retrieval-augmented question answering.<n>The approach introduces Meaning-Utility Estimation (MUE) and Diversity-Utility Estimation (DUE)<n>In the prototype, a unit is accepted only if it explicitly states the fact, rule, or condition required by the task.
  arXiv  Detail & Related papers  (2026-02-23T18:20:15Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Variability Need Not Imply Error: The Case of Adequate but Semantically Distinct Responses [7.581259361859477]
  Uncertainty quantification tools can be used to reject a response when the model is uncertain'<n>We estimate the Probability the model assigns to Adequate Responses (PROBAR)<n>We find PROBAR to outperform semantic entropy across prompts with varying degrees of ambiguity/open-endedness.
  arXiv  Detail & Related papers  (2024-12-20T09:02:26Z)
• Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty [47.95943057892318]
  Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight.<n>We introduce DiverseAgentEntropy, a novel, theoretically-grounded method employing multi-agent interaction for uncertainty estimation.
  arXiv  Detail & Related papers  (2024-12-12T18:52:40Z)
• Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries [85.81295563405433]
  We present a protocol that synthetically constructs context surrounding an under-specified query and provides it during evaluation.<n>We find that the presence of context can 1) alter conclusions drawn from evaluation, even flipping benchmark rankings between model pairs, 2) nudge evaluators to make fewer judgments based on surface-level criteria, like style, and 3) provide new insights about model behavior across diverse contexts.
  arXiv  Detail & Related papers  (2024-11-11T18:58:38Z)
• CONFLARE: CONFormal LArge language model REtrieval [0.0]
  Retrieval-augmented generation (RAG) frameworks enable large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses. RAG does not guarantee valid responses if retrieval fails to identify the necessary information as the context for response generation. We introduce a four-step framework for applying conformal prediction to quantify retrieval uncertainty in RAG frameworks.
  arXiv  Detail & Related papers  (2024-04-04T02:58:21Z)
• Uncertainty-aware Language Modeling for Selective Question Answering [107.47864420630923]
  We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs. Our approach is model- and data-agnostic, is computationally-efficient, and does not rely on external models or systems.
  arXiv  Detail & Related papers  (2023-11-26T22:47:54Z)
• R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents [14.455036827804541]
  Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output. We propose Randomized Utility-driven Synthesis of Uncertain REgions (R-U-SURE) R-U-SURE is an approach for building uncertainty-aware suggestions based on a decision-theoretic model of goal-conditioned utility.
  arXiv  Detail & Related papers  (2023-03-01T18:46:40Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.