mlsanityData to 14 Oct 2026

Delta1 with LLM: symbolic and neural integration for credible and explainable reasoning

arXiv 2603.12953 ↗First posted 13 Mar 2026cs.LO cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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Abstract (arXiv)

Neuro-symbolic reasoning increasingly demands frameworks that unite the formal rigor of logic with the interpretability of large language models (LLMs). We introduce an end to end explainability by construction pipeline integrating the Automated Theorem Generator Delta1 based on the full triangular standard contradiction (FTSC) with LLMs. Delta1 deterministically constructs minimal unsatisfiable clause sets and complete theorems in polynomial time, ensuring both soundness and minimality by construction. The LLM layer verbalizes each theorem and proof trace into coherent natural language explanations and actionable insights. Empirical studies across health care, compliance, and regulatory domains show that Delta1 and LLM enables interpretable, auditable, and domain aligned reasoning. This work advances the convergence of logic, language, and learning, positioning constructive theorem generation as a principled foundation for neuro-symbolic explainable AI.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 11 of its 36 references that are in the map. First posted on arXiv 13 Mar 2026.

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  1. 2025VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning24 Jan
  2. 2024LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations22 Jun
  3. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning20 May

Other references · 8

  1. 2022Autoformalization with Large Language Modelscited by 263
  2. 2022Formal Mathematics Statement Curriculum Learningcited by 142
  3. 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 38
  4. 2024Large Language Models Are Neurosymbolic Reasonerscited by 31
  5. 2024Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Modelscited by 23
  6. 2025LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Provingcited by 8
  7. 2025MuSLR: Multimodal Symbolic Logical Reasoningcited by 7
  8. 2023Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applicationscited by 6

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  1. 2026Logic-Parametric Neuro-Symbolic NLI: Controlling Logical Formalisms for Verifiable LLM Reasoning
  2. 2023Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs
  3. 2026SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification
  4. 2025Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations
  5. 2024Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach
  6. 2025Current Practices for Building LLM-Powered Reasoning Tools Are Ad Hoc -- and We Can Do Better
  7. 2025A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning
  8. 2024Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning