Policy-as-logic for robust reasoning over rules

2026-08-12

AI Summary

This work addresses the challenge of ensuring that generative AI systems adhere strictly to written policies when responding to natural language queries, a task where existing approaches struggle to simultaneously achieve accuracy, interpretability, and robustness. The authors propose a hybrid symbolic method that formalizes policies as logical rules, leverages large language models to extract factual predicates from user inputs, and employs an Answer Set Programming (ASP) solver for structured, rule-based reasoning. By decoupling fact extraction from logical inference, this approach significantly enhances robustness against input perturbations while guaranteeing auditability and interpretability of decisions. Experimental results demonstrate that the method outperforms baseline strategies—such as policy-as-prompt and policy-as-code—in accuracy across most scenarios and reduces token consumption by approximately an order of magnitude.

Abstract

In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect written policies or rules. We present a hybrid symbolic approach that expresses policies in formal logic and at inference time exploits the representation power of language models for fact extraction to ground predicates, and an answer set solver for reasoning such that responses are interpretable, auditable, and as we show, accurate and robust under input perturbations. Specifically, we show this separation of extraction and reasoning steps outperforms policy-as-prompt and policy-as-code methods in most cases with ~10x reduction in token usage. The results point to the value of structured reasoning and symbolic solvers in conjunction with generative models to make robust decisions involving objective criteria.

Problem

• policy compliance
• robust reasoning
• generative AI
• rule-based systems
• natural language queries

Innovation

• policy-as-logic
• symbolic reasoning
• answer set programming
• fact extraction
• robust inference