ViRectify: A Challenging Benchmark for Video Reasoning Correction with Multimodal Large Language Models

TL;DR

ViRectify is a new benchmark designed to evaluate and improve multimodal large language models' ability to identify and correct complex video reasoning errors through step-wise error correction and evidence grounding.

Contribution

We introduce ViRectify, a comprehensive dataset and correction framework that enables detailed evaluation and enhancement of MLLMs' video reasoning correction capabilities.

Findings

GPT-5 achieves 31.94% correction accuracy on ViRectify.

Qwen2.5-VL-7B outperforms 72B variants on the benchmark.

The framework reveals systematic asymmetries in error correction across models.

Abstract

As multimodal large language models (MLLMs) frequently exhibit errors in complex video reasoning scenarios, correcting these errors is critical for uncovering their weaknesses and improving performance. However, existing benchmarks lack systematic evaluation of MLLMs' ability to identify and correct these video reasoning errors. To bridge this gap, we propose ViRectify, a comprehensive benchmark to evaluate their fine-grained correction capability. Through an AI-assisted annotation pipeline with human verification, we construct a dataset of over 30K instances spanning dynamic perception, scientific reasoning, and embodied decision-making domains. In ViRectify, we challenge MLLMs to perform step-wise error identification and generate rationales with key video evidence grounding. In addition, we further propose the trajectory evidence-driven correction framework, comprising step-wise error trajectory and reward modeling on visual evidence-grounded correction. It encourages the model to explicitly concentrate on error propagation and key timestamps for correction. Extensive evaluation across 16 advanced MLLMs demonstrates that our ViRectify serves as a challenging testbed, where GPT-5 achieves only 31.94% correction accuracy. Our framework enables a Qwen2.5-VL-7B to consistently outperform the variants of 72B on ViRectify, showing the effectiveness of our approach. Further analysis uncovers systematic asymmetries in error correction across models, and our dataset is also a valuable data resource to perform reflection learning. We believe ViRectify provides a new direction for comprehensively evaluating the advanced MLLMs in video reasoning.