aiqbench analysis
This research addresses the inherent logical inconsistencies in Large Language Models by dynamically interleaving formal symbolic verification with natural language generation during inference. By actively penalizing intermediate fallacies rather than relying solely on post-hoc validation, this neuro-symbolic approach significantly improves performance on mathematical and general reasoning benchmarks. The study validates that integrating rigorous logic checks into training pipelines offers a scalable path to enhancing the reliability of advanced AI systems.
Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification
arXiv:2601.22642v2 Announce Type: replace Abstract: Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid. To bridge this gap, we introduce a formal logic verification-guided framework that dynamically interleaves formal symbolic verification with the natural language generation process, providing real-time feedback to detect and rectify errors as they occur. Distinguished from previous neuro-symbolic methods limited by passive post-hoc validation, our approach actively penalizes intermediate fallacies during the reasoning chain. We operationalize this framework via a novel two-stage training pipeline that synergizes formal logic verification-guided supervised fine-tuning and policy optimization. Extensive evaluation on six benchmarks spanning mathematical, logical, and general reasoning demonstrates that our 7B and 14B models outperform state-of-the-art baselines by average margins of 10.4% and 14.2%, respectively. These results validate that formal verification can serve as a scalable mechanism to significantly push the performance boundaries of advanced LLM reasoning.
Abstract:Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic systems avoid. To bridge this gap, we introduce a formal logic verification-guided framework that dynamically interleaves formal symbolic verification with the natural language generation process, providing real-time feedback to detect and rectify errors as they occur. Distinguished from previous neuro-symbolic methods limited by passive post-hoc validation, our approach actively penalizes intermediate fallacies during the reasoning chain. We operationalize this framework via a novel two-stage training pipeline that synergizes formal logic verification-guided supervised fine-tuning and policy optimization. Extensive evaluation on six benchmarks spanning mathematical, logical, and general reasoning demonstrates that our 7B and 14B models outperform state-of-the-art baselines by average margins of 10.4% and 14.2%, respectively. These results validate that formal verification can serve as a scalable mechanism to significantly push the performance boundaries of advanced LLM reasoning.
Machine Learning (cs.LG)
arXiv:2601.22642 [cs.LG]
(or arXiv:2601.22642v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.22642 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 240 words
