@inproceedings{yu-etal-2026-solid,
title = "{SOLID}: Infusing Symbolic Logic into {LLM}s via Dual-Verified Backward Reasoning",
author = "Yu, Weijie and
Dong, Yuhang and
Ma, Feifei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1528/",
pages = "34852--34869",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) often struggle with rigorous logical reasoning, suffering from hallucinations and inconsistent deduction. Chain-of-Thought (CoT) prompting relies heavily on the frozen model{'}s inherent capabilities, while neuro-symbolic approaches face a ``translation reliability'' bottleneck in which semantic parsing errors mislead external solvers. Recent work attempts to infuse symbolic capabilities via fine-tuning, but typically mimics forward reasoning, suffering from search-space explosion and irrelevant intermediate facts that dilute attention. We propose SOLID (Symbolic Oriented Logic Infusion via Dual-verification), a framework that synthesizes high-quality supervision through a Dual-Verification Mechanism: bidirectional consistency checks filter out hallucinated translations. Crucially, instead of forward chaining, we execute validated translations to generate backward reasoning traces{---}verifying necessary premises from the goal. By fine-tuning on these focused, goal-oriented paths, SOLID infuses the model with the efficient deduction process of a symbolic engine. Experiments on ProntoQA, LogicNLI, and ProofWriter demonstrate state-of-the-art accuracy, significantly outperforming CoT baselines, solver-based methods, and forward-chaining variants. Code and data are available at: https://github.com/ejie22/SOLID."
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<abstract>Large Language Models (LLMs) often struggle with rigorous logical reasoning, suffering from hallucinations and inconsistent deduction. Chain-of-Thought (CoT) prompting relies heavily on the frozen model’s inherent capabilities, while neuro-symbolic approaches face a “translation reliability” bottleneck in which semantic parsing errors mislead external solvers. Recent work attempts to infuse symbolic capabilities via fine-tuning, but typically mimics forward reasoning, suffering from search-space explosion and irrelevant intermediate facts that dilute attention. We propose SOLID (Symbolic Oriented Logic Infusion via Dual-verification), a framework that synthesizes high-quality supervision through a Dual-Verification Mechanism: bidirectional consistency checks filter out hallucinated translations. Crucially, instead of forward chaining, we execute validated translations to generate backward reasoning traces—verifying necessary premises from the goal. By fine-tuning on these focused, goal-oriented paths, SOLID infuses the model with the efficient deduction process of a symbolic engine. Experiments on ProntoQA, LogicNLI, and ProofWriter demonstrate state-of-the-art accuracy, significantly outperforming CoT baselines, solver-based methods, and forward-chaining variants. Code and data are available at: https://github.com/ejie22/SOLID.</abstract>
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%0 Conference Proceedings
%T SOLID: Infusing Symbolic Logic into LLMs via Dual-Verified Backward Reasoning
%A Yu, Weijie
%A Dong, Yuhang
%A Ma, Feifei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-solid
%X Large Language Models (LLMs) often struggle with rigorous logical reasoning, suffering from hallucinations and inconsistent deduction. Chain-of-Thought (CoT) prompting relies heavily on the frozen model’s inherent capabilities, while neuro-symbolic approaches face a “translation reliability” bottleneck in which semantic parsing errors mislead external solvers. Recent work attempts to infuse symbolic capabilities via fine-tuning, but typically mimics forward reasoning, suffering from search-space explosion and irrelevant intermediate facts that dilute attention. We propose SOLID (Symbolic Oriented Logic Infusion via Dual-verification), a framework that synthesizes high-quality supervision through a Dual-Verification Mechanism: bidirectional consistency checks filter out hallucinated translations. Crucially, instead of forward chaining, we execute validated translations to generate backward reasoning traces—verifying necessary premises from the goal. By fine-tuning on these focused, goal-oriented paths, SOLID infuses the model with the efficient deduction process of a symbolic engine. Experiments on ProntoQA, LogicNLI, and ProofWriter demonstrate state-of-the-art accuracy, significantly outperforming CoT baselines, solver-based methods, and forward-chaining variants. Code and data are available at: https://github.com/ejie22/SOLID.
%U https://aclanthology.org/2026.emnlp-main.1528/
%P 34852-34869
Markdown (Informal)
[SOLID: Infusing Symbolic Logic into LLMs via Dual-Verified Backward Reasoning](https://aclanthology.org/2026.emnlp-main.1528/) (Yu et al., EMNLP 2026)
ACL