Enterprise decision-makers increasingly rely on large language models (LLMs) for high-stakes reasoning tasks, from supply chain risk analysis to trade compliance. Yet evaluating how well these models handle deductive reasoning remains a challenge: existing benchmarks lack fine-grained control over logical complexity and struggle to balance semantic diversity with logical consistency, according to a paper published on arXiv (accession 2606.20227).
QMFOL Framework
To address these issues, researchers (Zheng, Xinyi; Shi, Ling; Yu, Tianlong; Zhao, Yongxin; Goette, Lorenz; and Wang, Kailong) propose QMFOL, an automated framework for generating monadic first-order logic reasoning tasks with quantifiable and controllable complexity. The framework constructs formal logical structures using conjunction and disjunction patterns, enabling precise control over reasoning depth, width, label types, and distractors. These structures are then translated into natural language via LLMs, with logical consistency ensured through round-trip verification using an external prover.
Benchmark Composition
Based on the framework, the team built QMFOLBench, a benchmark comprising 2,880 instances with 960 configurations across diverse logical and semantic dimensions. The benchmark covers a range of logical complexity levels, allowing systematic testing of model reasoning capabilities.
Evaluation Results
Evaluations on six large reasoning models (LRMs) and two LLMs revealed several key findings:
- Performance degrades and computational overhead increases with rising logical complexity.
- Models perform better on True-labeled tasks than on False or Unknown ones.
- Models exhibit sensitivity to semantic variation.
The results highlight that current models struggle as logical demands increase, a critical insight for enterprises deploying LLMs for decision support.
Implications for Enterprise Decision-Making
For technology leaders evaluating AI for supply chain, logistics, or trade finance, the QMFOL framework offers a scalable and reliable approach for constructing deductive reasoning benchmarks with controllable complexity. The ability to precisely evaluate reasoning capabilities can help organisations select models that maintain accuracy under complex logical conditions, reducing risk in automated decision-making. The benchmark's fine-grained control over reasoning depth, width, and distractors mirrors real-world complexity in trade documentation, customs classification, and contract analysis.
As the researchers note, QMFOL enables more precise evaluation of reasoning capabilities in modern language models, which is essential for high-stakes enterprise applications. Procurement teams should consider these dimensions when evaluating LLM vendors for logic-intensive tasks.
