Large Language Models (LLMs) have demonstrated impressive progress in complex reasoning tasks, largely driven by the Chain-of-Thought (CoT) paradigm, which decomposes difficult problems into intermediate steps. However, CoT reasoning remains fundamentally constrained by the probabilistic nature of neural generation, leading to unfaithful reasoning chains that undermine reliability. Neuro-symbolic approaches attempt to address these issues by combining LLMs with symbolic solvers, yet they face persistent challenges, including hallucinated translations, the mismatch between natural language and formal logic, and the limited enhancement of the LLM's intrinsic reasoning ability. To overcome these limitations, we propose Symbolic-Neural Soft-Logic Reasoning (SSR), a unified framework that integrates LLMs with symbolic reasoning and improves robustness by relaxing strict logical determinism while preserving verifiability. Our approach improves reasoning performance, automatically generates verifiable and human-like logical thinking chains for training and fine-tuning, and facilitates cross-disciplinary applications such as AI for mathematics. Experiments across multiple models and benchmarks demonstrate that SSR consistently outperforms existing reasoning frameworks, highlighting its effectiveness in enhancing both the robustness and interpretability of LLM reasoning.

翻译：大型语言模型在复杂推理任务中取得了显著进展，这主要得益于思维链范式——通过将难题分解为中间步骤来解决问题。然而，思维链推理从根本上受到神经生成概率性质的制约，导致推理链不可靠，削弱了其可信度。神经符号方法通过将LLM与符号求解器结合来尝试解决这些问题，但仍面临持续性挑战，包括幻觉式翻译、自然语言与形式逻辑之间的不匹配，以及对LLM内在推理能力提升有限等问题。为克服这些局限，我们提出符号-神经软逻辑推理框架，该统一框架通过放宽严格逻辑确定性同时保持可验证性，将LLM与符号推理相结合并提升鲁棒性。本方法在提升推理性能的同时，能自动生成可验证且类人的逻辑思维链用于训练与微调，并促进AI数学等跨学科应用。跨模型与基准测试的实验表明，SSR始终优于现有推理框架，凸显其在增强LLM推理鲁棒性与可解释性方面的有效性。