From Language to Logic: A Bi-Level Framework for Structured Reasoning
Keying Yang, Hao Wang, Kai Yang

TL;DR
This paper introduces a bi-level framework that bridges natural language and formal logic through a two-stage process, improving reasoning accuracy and transparency across various AI tasks.
Contribution
The novel bi-level framework enables structured reasoning by combining high-level task abstraction with low-level logic generation, optimized jointly for better performance.
Findings
Achieves up to 40% accuracy improvement on reasoning benchmarks.
Enhances transparency and error traceability in AI reasoning.
Supports modular reasoning across multiple domains.
Abstract
Structured reasoning over natural language inputs remains a core challenge in artificial intelligence, as it requires bridging the gap between unstructured linguistic expressions and formal logical representations. In this paper, we propose a novel \textbf{bi-level framework} that maps language to logic through a two-stage process: high-level task abstraction and low-level logic generation. At the upper level, a large language model (LLM) parses natural language queries into intermediate structured representations specifying the problem type, objectives, decision variables, and symbolic constraints. At the lower level, the LLM uses these representations to generate symbolic workflows or executable reasoning programs for accurate and interpretable decision making. The framework supports modular reasoning, enforces explicit constraints, and generalizes across domains such as mathematical…
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