A comprehensive survey of agents for computer use: Foundations, challenges, and future directions

Analysis

Why This Paper Matters

This survey addresses a critical gap in the rapidly evolving field of AI agents designed for computer use. As foundation models become more capable, the need for agents that can interact with digital environments in a general-purpose, adaptive manner grows. The paper's focus on neuro-symbolic methods is particularly timely, as purely neural approaches often struggle with reasoning, interpretability, and sample efficiency. By synthesizing current work and outlining challenges, the survey provides a roadmap for researchers aiming to build more robust computer use agents.

The paper matters because it consolidates disparate research threads—reinforcement learning, symbolic reasoning, and large language models—into a coherent framework. This is valuable for practitioners seeking to understand the state of the art and identify promising research directions. The emphasis on neuro-symbolic integration suggests a move beyond end-to-end deep learning toward hybrid systems that combine the strengths of both paradigms.

Technical Contributions

The paper's main technical contributions include:

• A comprehensive taxonomy of computer use agents, categorizing approaches by their underlying architectures and learning paradigms.
• Identification of key challenges such as generalization across diverse software environments, handling of long-horizon tasks, and integration of symbolic reasoning with neural perception.
• A proposed framework for neuro-symbolic agentic foundation models that combine the flexibility of foundation models with the structured reasoning of symbolic AI.
• Discussion of reinforcement learning as a key training paradigm for these agents, highlighting issues like reward sparsity and exploration.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from existing literature, noting that current computer use agents often achieve high performance on narrow benchmarks but fail to generalize. The paper does not provide specific metrics or comparisons, but it references common evaluation tasks such as web navigation, GUI interaction, and software testing. The lack of empirical results is a limitation, but the survey's value lies in its conceptual contributions.

Significance

The broader impact of this work is in shaping future research agendas. By highlighting the potential of neuro-symbolic methods, the survey encourages a shift away from purely data-driven approaches toward more structured, interpretable AI systems. This could lead to agents that are not only more capable but also safer and more trustworthy. For the AI field, this survey reinforces the importance of hybrid architectures and suggests that the next generation of computer use agents may require a synthesis of neural learning and symbolic reasoning.