mlsanityData to 14 Oct 2026

A Comprehensive Study of Implementation Bugs in Multi-modal Agents

arXiv 2607.04974 ↗First posted 6 Jul 2026cs.SEcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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 Outdated perception, untrustworthy planning and inapplicable execution could cause traffic accident and financial loss.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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 39 of its 60 references that are in the map. First posted on arXiv 6 Jul 2026.

Builds on

39 of 60 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Where LLM Agents Fail and How They can Learn From Failures29 Sep
  2. 2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories23 Jun
  3. 2025Can Agents Fix Agent Issues?27 May
  4. 2025Why Do Multi-Agent LLM Systems Fail?17 Mar
  5. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  6. 2024Agentless: Demystifying LLM-based Software Engineering Agents1 Jul

Other references · 33

  1. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 668
  2. 2023AppAgent: Multimodal Agents as Smartphone Userscited by 415
  3. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 398
  4. 2023GPT-Driver: Learning to Drive with GPTcited by 347
  5. 2024AutoCodeRover: Autonomous Program Improvementcited by 278
  6. 2023You Only Look at Screens: Multimodal Chain-of-Action Agentscited by 185
  7. 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Modelscited by 171
  8. 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Modelscited by 160
  9. 2023GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigationcited by 137
  10. 2024GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editingcited by 133
  11. 2025Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Toolscited by 106
  12. 2023On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Drivingcited by 80
21 more
  1. 2023Octopus: Embodied Vision-Language Programmer from Environmental Feedbackcited by 73
  2. 2023MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perceptioncited by 67
  3. 2023Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorldcited by 65
  4. 2023Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automationcited by 58
  5. 2023See and Think: Embodied Agent in Virtual Environmentcited by 54
  6. 2023DroidBot-GPT: GPT-powered UI Automation for Androidcited by 47
  7. 2023LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editingcited by 47
  8. 2023ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automationcited by 45
  9. 2024Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactionscited by 43
  10. 2025CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memorycited by 32
  11. 2023WavJourney: Compositional Audio Creation with Large Language Modelscited by 27
  12. 2023MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Modelscited by 20
  13. 2025Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Taskscited by 20
  14. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  15. 2023Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editingcited by 15
  16. 2025MARS: toward more efficient multi-agent collaboration for LLM reasoningcited by 13
  17. 2019Analysis and Detection of Information Types of Open Source Software Issue Discussionscited by 9
  18. 2025Large Language Model for Verilog Code Generation: Literature Review and the Road Aheadcited by 8
  19. 2022CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformerscited by 5
  20. 2025CCFQA: A Benchmark for Cross-Lingual and Cross-Modal Speech and Text Factuality Evaluationcited by 5
  21. 2025EU-Agent-Bench: Measuring Illegal Behavior of LLM Agents Under EU Lawcited by 1

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