MMInA: Benchmarking Multihop Multimodal Internet Agents

TL;DR

MMInA introduces a realistic, evolving benchmark for evaluating multimodal, multihop web agents on complex, real-world tasks, revealing current limitations and proposing memory augmentation to improve performance.

Contribution

The paper presents MMInA, a novel benchmark with real-world evolving websites and multihop tasks, along with a memory augmentation method to enhance agent performance.

Findings

Multihop web tasks are easy for humans but challenging for state-of-the-art agents.

Agents tend to fail early in multihop tasks, reducing success rates.

Memory replay significantly improves agent performance on multihop tasks.

Abstract

Autonomous embodied agents live on an Internet of multimedia websites. Can they hop around multimodal websites to complete complex user tasks? Existing benchmarks fail to assess them in a realistic, evolving environment for their embodiment across websites. To answer this question, we present MMInA, a multihop and multimodal benchmark to evaluate the embodied agents for compositional Internet tasks, with several appealing properties: 1) Evolving real-world multimodal websites. Our benchmark uniquely operates on evolving real-world websites, ensuring a high degree of realism and applicability to natural user tasks. Our data includes 1,050 human-written tasks covering various domains such as shopping and travel, with each task requiring the agent to extract multimodal information from web pages as observations autonomously; 2) Multihop web browsing. Our dataset features naturally compositional tasks that require information from or actions on multiple websites to solve, to assess long-range reasoning capabilities on web tasks; 3) Holistic evaluation. We propose a novel protocol for evaluating an agent's progress in completing multihop tasks. We experiment with both standalone (multimodal) language models and heuristic-based web agents. Extensive experiments demonstrate that while long-chain multihop web tasks are easy for humans, they remain challenging for state-of-the-art web agents. We identify that agents are more likely to fail on the early hops when solving tasks with more hops, which results in lower task success rates. To address this issue, we propose a simple memory augmentation approach that replays past action trajectories to reflect. Our method significantly improves the performance of both the single-hop and multihop web browsing abilities. Our code and data are available at github.com/shulin16/MMInA.