Introduces diversity of extensions in argumentation frameworks via symmetric difference and gives a systematic complexity classification for deciding existence of k-diverse extensions and computing maximum diversity.
Maximum Satisfiability , volume =
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Backtrackable Inprocessing enables sound inprocessing at arbitrary decision levels in incremental SAT solving and solves about 1.5 times as many difficult bounds on 2017 BMC benchmarks compared to global-level preprocessing.
LLMs generate verifiable MaxSAT encodings from natural language, achieving over 80% acceptance rates on preference tasks where direct LLM reasoning fails.
citing papers explorer
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Diversity of Extensions in Abstract Argumentation
Introduces diversity of extensions in argumentation frameworks via symmetric difference and gives a systematic complexity classification for deciding existence of k-diverse extensions and computing maximum diversity.
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Backtrackable Inprocessing
Backtrackable Inprocessing enables sound inprocessing at arbitrary decision levels in incremental SAT solving and solves about 1.5 times as many difficult bounds on 2017 BMC benchmarks compared to global-level preprocessing.
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Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
LLMs generate verifiable MaxSAT encodings from natural language, achieving over 80% acceptance rates on preference tasks where direct LLM reasoning fails.