LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning
Lets language models write Answer Set Programs, then uses feedback from a symbolic solver to iteratively fix their code. Shows this combo handles default rules and exceptions better than standard constraint solvers on diverse logic tasks. If you are building reasoning-heavy agents, this is a concrete recipe for bolting on symbolic reliability. ([arxiv.org](https://arxiv.org/abs/2604.27960))
Adam Ishay, Joohyung Lee