Coding Agents with Multimodal Browsing are Generalist Problem Solvers (2506.03011v1)

Abstract: Modern human labor is characterized by specialization; we train for years and develop particular tools that allow us to perform well across a variety of tasks. In addition, AI agents have been specialized for domains such as software engineering, web navigation, and workflow automation. However, this results in agents that are good for one thing but fail to generalize beyond their intended scope. One reason for this is that agent developers provide a highly specialized set of tools or make architectural decisions optimized for a specific use case or benchmark. In this work, we ask the question: what is the minimal set of general tools that can be used to achieve high performance across a diverse set of tasks? Our answer is OpenHands-Versa, a generalist agent built with a modest number of general tools: code editing and execution, web search, as well as multimodal web browsing and file access. Importantly, OpenHands-Versa demonstrates superior or competitive performance over leading specialized agents across three diverse and challenging benchmarks: SWE-Bench Multimodal, GAIA, and The Agent Company, outperforming the best-performing previously published results with absolute improvements in success rate of 9.1, 1.3, and 9.1 points respectively. Further, we show how existing state-of-the-art multi-agent systems fail to generalize beyond their target domains. These results demonstrate the feasibility of developing a generalist agent to solve diverse tasks and establish OpenHands-Versa as a strong baseline for future research.

• The paper presents AgentName, a versatile AI model using multimodal browsing and limited tools to achieve competitive benchmark results.
• It leverages web search, interactive browsing, and code execution to surpass specialized models in diverse tasks.
• The study highlights that flexible toolsets in single-agent systems can reduce retraining costs and improve scalability in AI applications.

Evaluation of "Coding Agents with Multimodal Browsing are Generalist Problem Solvers"

The paper "Coding Agents with Multimodal Browsing are Generalist Problem Solvers" presents an exploration into the potential for designing AI agents capable of performing well across a wide array of tasks without being excessively specialized. The work introduces \AgentName, an agent that leverages a limited set of general-purpose tools, including web search, multimodal web browsing, and code execution, to achieve competitive performance across diverse benchmarks.

Overview

Specialized AI agents, developed to excel in specific domains like software engineering or web navigation, tend to lack versatility outside their intended scopes due to highly optimized architectures tailored to particular benchmarks. This paper posits a different approach, questioning the necessity of specialization by demonstrating the effectiveness of a generalist agent built with foundational tools.

\AgentName is designed within the OpenHands framework, enhancing its capabilities with interactive, multimodal browsing, and improved information access via search APIs. Impressively, \AgentName surpasses or matches the state-of-the-art results across three comprehensive benchmarks: SWE-Bench Multimodal, GAIA, and The Agent Company. Specifically, it achieves notable success rates of 34.43% on SWE-Bench Multimodal, 51.16% on GAIA, and a full completion rate of 33.14% on The Agent Company, outperforming existing models that are often fine-tuned for specific tasks.

Key Contributions and Findings

1. Tool Variability and Generalization: The paper underscores the efficacy of providing AI agents with a versatile toolset, arguing this approach enhances adaptability across a range of tasks. Unlike highly specialized agents, \AgentName's performance illustrates that significant versatility can be achieved using a minimal, well-chosen set of tools.
2. Competitive Performance: By examining the performance against stringent benchmarks, the paper confirms that \AgentName not only competes with but often exceeds the capabilities of domain-focused systems, without requiring extensive domain-specific customization.
3. Analysis of Tool Utilization: Analyzing the frequency and types of tools used by \AgentName compared to specialized counterparts provides transparency into its operational effectiveness and decision-making strategies, highlighting how it adapts tool use based on task requirements.
4. Exploration of Multi-Agent vs. Single-Agent Frameworks: The research offers insights into the comparative benefits of single-agent frameworks like \AgentName against more complex multi-agent systems. The findings suggest that single-agent systems, when equipped with broadly applicable tools, can efficiently undertake diverse tasks that typically require specialized capabilities.

Implications for AI Development

The theoretical implications point towards a shift from highly specialized AI systems towards more generalist applications, wherein a broadly applicable toolset can maintain competitive performance across tasks. Practically, the adaptability afforded by \AgentName can significantly reduce the need for constant retraining and redevelopment of agents for varied tasks, making deployments more scalable and cost-efficient.

Future Developments

Looking ahead, the insights from this work could drive advancements in developing robust AI agents for real-world applications, encouraging a design philosophy centered around flexibility and generalization. Additionally, further research might explore the refinement of tool sets for even greater task coverage, potentially introducing more innovative capabilities into the agent’s repertoire.

In conclusion, the paper successfully establishes \AgentName as a consequential model in the field of AI generalist agents, setting a benchmark for future research into versatile AI systems capable of navigating complex and varied task environments.