QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation

2026-06-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

QMFOL is an automated framework designed to generate monadic first-order logic reasoning tasks, addressing the need for more precise LLM evaluation benchmarks. It constructs formal logical structures using conjunction and disjunction patterns, allowing quantifiable control over reasoning depth, width, label types, and distractors. These structures are then translated into natural language, with logical consistency verified via round-trip verification using an external prover. Based on this framework, QMFOLBench was developed, comprising 2880 instances across 960 configurations. Evaluations on six large reasoning models (LRMs) and two LLMs revealed that performance degrades and computational overhead increases with rising logical complexity. Models also performed better on True-labeled tasks compared to False or Unknown ones, and exhibited sensitivity to semantic variation.

Key takeaway

For AI Scientists or ML Engineers evaluating LLM reasoning for high-stakes decision-making, you should consider benchmarks like QMFOLBench to precisely assess how logical complexity and semantic variations impact model performance. This allows you to identify specific weaknesses in your models, guiding targeted improvements for more robust and reliable deductive reasoning capabilities in real-world applications. Your evaluation strategy should incorporate fine-grained control over logical task parameters.

Key insights

QMFOL provides a scalable framework for generating controllable, complex monadic first-order logic reasoning benchmarks for LLMs.

Principles

• Logical complexity directly impacts LLM deductive reasoning performance.
• LLMs demonstrate higher accuracy on True-labeled reasoning tasks.
• Semantic variations significantly influence LLM reasoning capabilities.

Method

QMFOL constructs formal logical structures, translates them into natural language, and ensures logical consistency through round-trip verification using an external prover.

In practice

• Generate deductive reasoning tasks with precisely controlled complexity.
• Evaluate LLM sensitivity to specific semantic variations.
• Identify specific logical weaknesses in large language models.

Topics

• Large Language Models
• Deductive Reasoning
• Benchmark Generation
• Monadic First-Order Logic
• Logical Complexity
• Model Evaluation

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.