The World Won't Stay Still: Programmable Evolution for Agent Benchmarks
arXiv:2603.05910v1 Announce Type: new Abstract: LLM-powered agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks assume static environments with fixed schemas and toolsets, neglecting the evolutionary nature of the real world and agents' robustness to environmental changes. In this paper, we study a crucial problem: how to evolve the agent environment in a scalable and controllable way, thereby better evaluating agents' adaptability to real-world dynamics. We propose ProEvolve, a graph-based framework that makes environment evolution programmable. At its core, a typed relational graph provides a unified, explicit representation of the environment: data, tools, and schema. Under this formalism, adding, removing, or modifying capabilities are expressed as graph transformations that coherently propagate updates across tools, schemas, and data access. Building on this, ProEvolve can (1) prog
arXiv:2603.05910v1 Announce Type: new Abstract: LLM-powered agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks assume static environments with fixed schemas and toolsets, neglecting the evolutionary nature of the real world and agents' robustness to environmental changes. In this paper, we study a crucial problem: how to evolve the agent environment in a scalable and controllable way, thereby better evaluating agents' adaptability to real-world dynamics. We propose ProEvolve, a graph-based framework that makes environment evolution programmable. At its core, a typed relational graph provides a unified, explicit representation of the environment: data, tools, and schema. Under this formalism, adding, removing, or modifying capabilities are expressed as graph transformations that coherently propagate updates across tools, schemas, and data access. Building on this, ProEvolve can (1) program the evolutionary dynamics as graph transformations to generate environments automatically, and (2) instantiate task sandboxes via subgraph sampling and programming. We validate ProEvolve by evolving a single environment into 200 environments and 3,000 task sandboxes, and benchmark representative agents accordingly.
Executive Summary
This article proposes a novel framework, ProEvolve, designed to programmatically evolve agent environments in a scalable and controllable manner. The framework's graph-based structure allows for explicit representation of environments, tools, and schema, facilitating the creation of diverse and dynamic environments. By enabling the programming of evolutionary dynamics, ProEvolve can automatically generate environments and instantiate task sandboxes, enhancing the evaluation of agents' adaptability to real-world dynamics. The authors validate ProEvolve by evolving a single environment into 200 environments and 3,000 task sandboxes, and benchmarking representative agents accordingly.
Key Points
- ▸ ProEvolve is a graph-based framework for programmatically evolving agent environments.
- ▸ The framework provides a unified, explicit representation of environments, tools, and schema.
- ▸ ProEvolve enables the programming of evolutionary dynamics for automatic environment generation and task sandbox instantiation.
Merits
Strength in Representational Power
ProEvolve's graph-based structure offers a robust and flexible representation of environments, tools, and schema, allowing for explicit modeling of complex relationships and interactions.
Scalability and Controllability
The framework's ability to program evolutionary dynamics enables the generation of diverse and dynamic environments in a scalable and controllable manner, facilitating the evaluation of agents' adaptability to real-world dynamics.
Demerits
Implementation Complexity
The development and deployment of ProEvolve may require significant expertise in graph theory and programming, potentially limiting its adoption by researchers and practitioners without extensive experience in these areas.
Data Requirements
The framework's reliance on explicit representations of environments, tools, and schema may require substantial amounts of data, potentially posing challenges for domains with limited or noisy data availability.
Expert Commentary
The proposed framework, ProEvolve, represents a significant advancement in the field of agent evaluation and benchmarking. By providing a programmable and scalable approach to environment evolution, ProEvolve addresses a critical limitation of existing benchmarks and enables the evaluation of agents' adaptability to real-world dynamics. While the framework's complexity and data requirements may pose challenges for implementation, the potential benefits of ProEvolve make it an exciting and promising area of research. As AI continues to permeate various domains, the development of more robust and adaptive AI systems will be crucial, and ProEvolve's contributions to this effort are timely and significant.
Recommendations
- ✓ Future research should focus on applying ProEvolve in diverse domains and evaluating its effectiveness in real-world scenarios.
- ✓ The development of tools and methodologies to facilitate the implementation and deployment of ProEvolve would be beneficial, particularly for researchers without extensive expertise in graph theory and programming.