Where Did the Reasoning Go Wrong? A Benchmark of Puzzle-Based Visual Tasks with CoT Error Detection

Yusu Qian, Cheng Wan, Chao Jia, Yinfei Yang, Qingyu Zhao, Zhe Gan

Keywords: multimodal large language models, visual reasoning, puzzle-based benchmark, chain-of-thought

TL;DR: This paper introduces a diagnostic benchmark of puzzle-based visual reasoning tasks that evaluates both final answers and first-error detection, revealing gaps between multimodal LLMs’ fluent reasoning style and their actual reasoning substance.

Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress on vision–language tasks, yet their reasoning processes remain sometimes unreliable. We introduce PRISM-Bench, a benchmark of puzzle-based visual challenges designed to evaluate not only whether models can solve problems, but how their reasoning unfolds. Unlike prior evaluations that measure only final-answer accuracy, PRISM-Bench introduces a diagnostic task: given a visual puzzle and a step-by-step chain-of-thought (CoT) containing exactly one error, models must identify the first incorrect step. This setting enables fine-grained assessment of logical consistency, error detection, and visual reasoning. The puzzles in PRISM-Bench require multi-step symbolic, geometric, and analogical reasoning, resisting shortcuts based on superficial pattern matching. Evaluations across state-of-the-art MLLMs reveal a persistent gap between fluent generation and faithful reasoning: models that produce plausible CoTs often fail to locate simple logical faults. By disentangling answer generation from reasoning verification, PRISM-Bench offers a sharper lens on multimodal reasoning competence and underscores the need for diagnostic evaluation protocols in the development of trustworthy MLLMs.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 9861