mlsanityData to 7 Oct 2026

Elenchus: Generating Knowledge Bases from Prover-Skeptic Dialogues

arXiv 2603.06974 ↗First posted 7 Mar 2026cs.CL cs.AI cs.LOcited by 0

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Abstract (arXiv)

We present Elenchus, a dialogue system for knowledge base construction grounded in inferentialist semantics, where knowledge engineering is re-conceived as explicitation rather than extraction from expert testimony or textual content. A human expert develops a bilateral position (commitments and denials) about a topic through prover-skeptic dialogue with a large language model (LLM) opponent. The LLM proposes tensions (claims that parts of the position are jointly incoherent) which the expert resolves by retraction, refinement, or contestation. The LLM thus serves as a defeasible derivability oracle whose unreliability is structurally contained by the expert's authority. Our main technical contribution is a mapping from Elenchus dialectical states to material bases in Hlobil and Brandom's NonMonotonic MultiSuccedent (NMMS) logic, satisfying Containment and enabling the elaboration of logical vocabulary that makes explicit the inferential relationships negotiated in the dialectic. We demonstrate the approach on the W3C PROV-O provenance ontology, where a single dialogue session elicits and structures design tensions that a domain expert can articulate, corresponding to decisions documented in a retrospective analysis of the ontology's design. Using pyNMMS, an automated NMMS reasoner, we verify that the structural properties of the resulting material base (nontransitivity, nonmonotonicity, and independence) correspond to specific PROV design rationales, demonstrating end-to-end integration from dialogue through formal reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 40 references that are in the map. First posted on arXiv 7 Mar 2026.

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References · 5

  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  2. 2018AI safety via debatecited by 384
  3. 2024Debating with More Persuasive LLMs Leads to More Truthful Answerscited by 255
  4. 2024Improving Multi-Agent Debate with Sparse Communication Topologycited by 103
  5. 2024Improving Ontology Requirements Engineering with OntoChat and Participatory Promptingcited by 3

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