用符号逻辑生成高保真数据，解决大模型幻觉与逻辑错误问题。
CircuitSynth: Reliable Synthetic Data Generation
- 将大模型推理能力转化为可验证的逻辑结构，强制约束生成内容
- 在复杂逻辑题中实现100%模式有效性，远超基线的12.4%
- 适合需要高可信数据生成的场景，如金融、医疗等安全敏感领域
高保真合成数据是现代机器学习的核心，但大型语言模型在结构化生成任务中常出现幻觉、逻辑矛盾和模式崩溃。现有方法如提示工程或检索增强生成，缺乏在语言表达力与生成有效性、覆盖率之间取得平衡的机制。为此，我们提出CircuitSynth，一种新颖的神经符号框架，将语义推理与表面实现解耦。通过将教师大模型的推理能力提炼为概率命题决策图（PSDD），CircuitSynth构建了一个可计算的语义先验，从结构上强制满足硬逻辑约束。此外，引入凸优化机制以严格满足软分布目标。在多个基准测试中的实证评估表明，CircuitSynth在复杂逻辑谜题中实现了100%的模式有效性，而无约束基线仅为12.4%，同时在罕见组合覆盖方面显著优于当前最优方法。
原文摘要 · Abstract (English)
The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from hallucinations, logical inconsistencies, and mode collapse when tasked with structured generation. Existing approaches, such as prompting or retrieval-augmented generation, lack the mechanisms to balance linguistic expressivity with formal guarantees regarding validity and coverage. To address this, we propose CircuitSynth, a novel neuro-symbolic framework that decouples semantic reasoning from surface realization. By distilling the reasoning capabilities of a Teacher LLM into a Probabilistic Sentential Decision Diagram (PSDD), CircuitSynth creates a tractable semantic prior that structurally enforces hard logical constraints. Furthermore, we introduce a convex optimization mechanism to rigorously satisfy soft distributional goals. Empirical evaluations across diverse benchmarks demonstrate that CircuitSynth achieves 100% Schema Validity even in complex logic puzzles where unconstrained baselines fail (12.4%) while significantly outperforming state-of-the-art methods in rare-combination coverage.
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