arXiv:2604.27960v2 Announce Type: replace 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 langua
This research addresses fundamental limitations of current LLMs, which are increasingly deployed in situations requiring complex reasoning, pushing the frontier of their utility.
Improving LLM reasoning capabilities, particularly for nonmonotonic logic, expands their applicability to more complex, real-world problems that involve defeasible reasoning.
The ability for LLMs to program and utilize Answer Set Programming (ASP) for self-correction creates a pathway to more robust and less error-prone AI systems, reducing reliance on monotonic logic constraints.
- · AI developers
- · Enterprises adopting AI
- · Neuro-symbolic AI research
- · LLM-only reasoning approaches
- · Current purely data-driven AI
LLMs demonstrate improved logical consistency and handle higher-complexity problems with greater accuracy.
This framework could enable more sophisticated AI agents capable of nuanced decision-making and problem-solving in dynamic environments.
Robust, defeasible reasoning within AI systems could accelerate automation in fields requiring expert-level judgment and adaptive planning.
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Read at arXiv cs.AI