LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning

• URL: http://arxiv.org/abs/2604.27960v1
• Date: Thu, 30 Apr 2026 14:55:48 GMT
• Title: LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning
• Abstract summary: We present "LLM+ASP," a framework that translates natural language into Answer Set Programming (ASP)<n>Our framework operates without any per-task engineering and applies uniformly across diverse reasoning tasks.
• Score: 6.77760769532626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp performance degradation on high-complexity problems. While neuro-symbolic methods attempt to mitigate these issues by coupling LLMs with symbolic reasoners, existing approaches typically rely on monotonic logics (e.g., SMT) that cannot represent defeasible reasoning -- essential components of human cognition. We present "LLM+ASP," a framework that translates natural language into Answer Set Programming (ASP), a nonmonotonic formalism based on stable model semantics. Unlike prior "LLM+ASP" approaches that require manually authored knowledge modules, domain-specific prompts, or evaluation restricted to single problem classes, our framework operates without any per-task engineering and applies uniformly across diverse reasoning tasks. Our system utilizes an automated self-correction loop where structured feedback from the ASP solver enables iterative refinement. Evaluating across six diverse benchmarks, we demonstrate that: (1) stable model semantics allow LLMs to naturally express default rules and exceptions, outperforming SMT-based alternatives by significant margins on nonmonotonic tasks; (2) iterative self-correction is the primary driver of performance, effectively replacing the need for handcrafted domain knowledge; (3) compact in-context reference guides substantially outperform verbose documentation, revealing a "context rot" phenomenon where excessive context hinders constraint adherence.

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