A Comprehensive Study of Implementation Bugs in Multi-modal Agents

2026-07-06

AI Summary

Multimodal agents operating in dynamic, complex environments are prone to severe failures due to outdated perception, unreliable planning, or inapplicable execution; however, their implementation-specific defects have not been systematically studied. This work presents the first systematic definition and taxonomy of such defects, derived from an analysis of 34 agents and 1,268 issue reports, introducing a three-tier classification encompassing global symptoms, module-level symptoms, and root causes. To operationalize this taxonomy, we develop MATester, the first automated detection tool for multimodal agent defects, which combines runtime component output analysis with large language model–driven architectural understanding to validate defect instances. Experiments demonstrate that MATester covers 61.4% of known open issues across 12 previously unexamined agents and uncovers 31 new defects, thereby validating both the efficacy of the proposed taxonomy and the practical utility of the tool.

Abstract

Multi-Modal Agents (M-agents), empowered by Large Language Models (LLMs), excel in various complex, open-world scenarios such as autonomous driving and robotics. However, their unique requirements to interact with dynamic and diverse multi-modal environments introduce novel implementation challenges beyond those faced by traditional agents. Outdated perception, untrustworthy planning and inapplicable execution could cause traffic accident and financial loss. Despite growing study on agent issues, there has not been a systematic study focusing on M-agent-specific implementation bugs. To address this gap, we conducted the first systematic study of implementation bugs in M-agents. We collected 34 representative M-agents from diverse sources and, through meticulous filtering,identified 158 M-agent-specific bugs from 1,268 issue reports. Using a top-down strategy, we developed a comprehensive taxonomy that classifies bugs by global symptoms, functionality component-level symptoms, and root causes. We then implemented MATester, an automatic proof-of-concept bug identifier by analyzing runtime inter-component outputs. When applied to 12 extra M-agents, MATester successfully covered 61.4% of known open issues and discovered 31 additional bugs, demonstrating the practical usefulness of our study. Our work provides a comprehensive reference and guideline for classification, prevention and fix of M-agent bugs.

Problem

• Multi-Modal Agents
• Implementation Bugs
• Large Language Models
• Autonomous Systems
• Software Defects

Innovation

• Multi-Modal Agents
• Implementation Bugs
• Bug Taxonomy
• MATester
• Large Language Models