Download PDFOpen PDF in browserRepresentation Abstraction and Proof Search for English-to-Logic Question Answering13 pages•Published: October 23, 2026AbstractWe describe the knowledge representation and proof search choices behind llmpipe, an English-to-logic question-answering pipeline. The system maps English assertions and questions to a controlled-predicate first-order representation, compiles it into prover-ready clauses, and uses GK, a saturation-based first-order prover extended for commonsense reasoning, to derive answers and render proofs back into English.We distinguish between machinery that requires dedicated prover support, such as contradiction-tolerant search, numeric confidence handling, and defeasible reasoning with prioritized default blocks, and first-order encodings, such as context terms, Davidsonian events, modal classifiers, biconditional answer definitions, and canonicalized measurements. These choices affect clause shape, term depth, proof search branching, axiom selection, and other proof search strategies. We evaluate the pipeline on nlformtasks, a 1600-case English question-answering test set, across four LLM backends. The goal is to identify which representations are useful for different classes of English questions and how they affect automated reasoning performance. Keyphrases: autoformalization, automated theorem proving, commonsense reasoning, defeasible reasoning, english to logic translation, knowledge representation, large language models, natural language, question answering In: Erika Ábrahám, Nikolaj Bjørner and Michael Rawson (editors). Proceedings of the 26th Conference on Logic for Programming, Artificial Intelligence, and Reasoning, vol 112, pages 535-547.
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