E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs

TL;DR

This paper introduces E-VAds, a new benchmark for understanding e-commerce short videos, addressing the complexity of multi-modal signals and commercial intent reasoning, and proposes a novel RL-based model that significantly improves performance.

Contribution

It presents the first dedicated benchmark for e-commerce video understanding and develops a specialized RL-based reasoning model with a multi-grained reward system.

Findings

E-VAds exhibits higher information density than general datasets.

E-VAds-R1 achieves over 109% performance gain in commercial intent reasoning.

The benchmark covers diverse product categories and reasoning tasks.

Abstract

E-commerce short videos represent a high-revenue segment of the online video industry characterized by a goal-driven format and dense multi-modal signals. Current models often struggle with these videos because existing benchmarks focus primarily on general-purpose tasks and neglect the reasoning of commercial intent. In this work, we first propose a multi-modal information density assessment framework to quantify the complexity of this domain. Our evaluation reveals that e-commerce content exhibits substantially higher density across visual, audio, and textual modalities compared to mainstream datasets, establishing a more challenging frontier for video understanding. To address this gap, we introduce E-commerce Video Ads Benchmark (E-VAds), which is the first benchmark specifically designed for e-commerce short video understanding. We curated 3,961 high-quality videos from Taobao covering a wide range of product categories and used a multi-agent system to generate 19,785 open-ended Q&A pairs. These questions are organized into two primary dimensions, namely Perception and Cognition and Reasoning, which consist of five distinct tasks. Finally, we develop E-VAds-R1, an RL-based reasoning model featuring a multi-grained reward design called MG-GRPO. This strategy provides smooth guidance for early exploration while creating a non-linear incentive for expert-level precision. Experimental results demonstrate that E-VAds-R1 achieves a 109.2% performance gain in commercial intent reasoning with only a few hundred training samples.