MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning
This provides a scalable evaluation tool for researchers and developers working on multi-modal reasoning systems, though it is incremental as it builds on existing benchmark efforts.
The paper tackles the lack of standardized benchmarks for evaluating reasoning capabilities in multi-modal large language models, introducing MMLU-Reason, a benchmark with 1,083 questions and a pipeline that reveals models like Claude-3.7-Sonnet and Gemini-2.5 Pro still suffer from reasoning pathologies despite outperforming non-thinking counterparts.
Recent advances in Multi-Modal Large Language Models (MLLMs) have enabled unified processing of language, vision, and structured inputs, opening the door to complex tasks such as logical deduction, spatial reasoning, and scientific analysis. Despite their promise, the reasoning capabilities of MLLMs, particularly those augmented with intermediate thinking traces (MLLMs-T), remain poorly understood and lack standardized evaluation benchmarks. Existing work focuses primarily on perception or final answer correctness, offering limited insight into how models reason or fail across modalities. To address this gap, we introduce the MMLU-Reason, a new benchmark designed to rigorously evaluate multi-modal reasoning with explicit thinking. The MMLU-Reason comprises 1) a high-difficulty dataset of 1,083 questions spanning six diverse reasoning types with symbolic depth and multi-hop demands and 2) a modular Reasoning Trace Evaluation Pipeline (RTEP) for assessing reasoning quality beyond accuracy through metrics like relevance, consistency, and structured error annotations. Empirical results show that MLLMs-T overall outperform non-thinking counterparts, but even top models like Claude-3.7-Sonnet and Gemini-2.5 Pro suffer from reasoning pathologies such as inconsistency and overthinking. This benchmark reveals persistent gaps between accuracy and reasoning quality and provides an actionable evaluation pipeline for future model development. Overall, the MMLU-Reason offers a scalable foundation for evaluating, comparing, and improving the next generation of multi-modal reasoning systems.