Neuro-Symbolic Reconstruction of Philosophical and Formal Arguments: Automated Propositional Mapping and Verdict Verification via LLMs and SMT Solvers

نوع مقاله : مقاله علمی- پژوهشی

نویسندگان

1 گروه مهندسی کنترل، دانشگاه تبریز، تبریز، ایران.

2 استادیار گروه مهندسی کنترل، دانشگاه تبریز، تبریز، ایران

چکیده

“To test whether an end-to-end neuro-symbolic pipeline (deterministic segmentation, LLM formalisation into SMT-LIB2, automated symbolic repair, and three-way Z3 SMT verification with countermodel generation) can reconstruct classical philosophical arguments, and to quantify where scaling changes the character of formalisation failures.” Five-stage pipeline evaluated on 31 curated philosophical and explicit-logic arguments plus the full 204-item FOLIO validation split under a three-way protocol, replicated across three independently pinned backends (Qwen3-4B-Instruct; Gemini 3.8 Flash; Nemotron-3-Ultra-550B). An independent audit script re-derives every reported number from the archived raw records. Explicit logic vs. philosophical prose: 68.8%/27.3% (Qwen) and 93.8%/63.6% (Gemini); replicated effect sizes of −41.5 and −30.2 percentage points (Fisher p = 0.054/0.125, not significant for the larger model). Enthymemes: 0.0% vs 50.0%; a warrant-eliciting prompt repairs the barrier only for the larger model (7/11 to 10/11) at a cost on explicit items (8/8 to 6/8). FOLIO: a refutation asymmetry replicates in all three families and decided-item refutation never exceeds 58.1% (36/62); the direct no-solver baseline is 66.7% for the small model. LLMs transcribe fluently but reconstruct poorly; the deficit on classical prose is necessary rather than technical. The pipeline's value is inspectable judgment — solver-checked verdicts, per-stage diagnosis, and countermodels as automated thought experiments.

کلیدواژه‌ها


عنوان مقاله [English]

Neuro-Symbolic Reconstruction of Philosophical and Formal Arguments: Automated Propositional Mapping and Verdict Verification via LLMs and SMT Solvers

نویسندگان [English]

  • Arian Esmailinasab 1
  • Amir Aminzadeh Ghavifekr 2
1 Department of Control Engineering, University of Tabriz, Tabriz, Iran
2 Assistant Professor of Control Engineering Department, University of Tabriz, Tabriz
چکیده [English]

“To test whether an end-to-end neuro-symbolic pipeline (deterministic segmentation, LLM formalisation into SMT-LIB2, automated symbolic repair, and three-way Z3 SMT verification with countermodel generation) can reconstruct classical philosophical arguments, and to quantify where scaling changes the character of formalisation failures.” Five-stage pipeline evaluated on 31 curated philosophical and explicit-logic arguments plus the full 204-item FOLIO validation split under a three-way protocol, replicated across three independently pinned backends (Qwen3-4B-Instruct; Gemini 3.8 Flash; Nemotron-3-Ultra-550B). An independent audit script re-derives every reported number from the archived raw records. Explicit logic vs. philosophical prose: 68.8%/27.3% (Qwen) and 93.8%/63.6% (Gemini); replicated effect sizes of −41.5 and −30.2 percentage points (Fisher p = 0.054/0.125, not significant for the larger model). Enthymemes: 0.0% vs 50.0%; a warrant-eliciting prompt repairs the barrier only for the larger model (7/11 to 10/11) at a cost on explicit items (8/8 to 6/8). FOLIO: a refutation asymmetry replicates in all three families and decided-item refutation never exceeds 58.1% (36/62); the direct no-solver baseline is 66.7% for the small model. LLMs transcribe fluently but reconstruct poorly; the deficit on classical prose is necessary rather than technical. The pipeline's value is inspectable judgment — solver-checked verdicts, per-stage diagnosis, and countermodels as automated thought experiments.

کلیدواژه‌ها [English]

  • Argument Reconstruction
  • Enthymemes
  • First-order Logic
  • Large Language Models
  • Neuro-Symbolic Systems
  • Z3 SMT solver
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