Policy-as-logic for robust reasoning over rules
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...
| Authors | Rahul Nair, Bastian Lipka, Elizabeth Daly |
|---|---|
| Primary category | cs.AI |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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