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This work introduces amomaximize, a novel maximization statement that integrates AMO constraints directly into the objective function, and shows that, in specific scenarios, this approach improves performance compared to clingo.
A logic-guided data extraction framework combining LLM-based extraction with answer set programming (ASP), which reduces LLM calls and improves extraction quality by mitigating spurious outputs, demonstrating the value of non-monotonic logic programming for controlled semantic extraction.
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