DynaWorld: Towards Self-Evolving Multi-Agent Collaboration in Dynamic Multimodal Environments

Autonomous agents powered by large language models (LLMs) are increasingly deployed in long-horizon interactive environments that evolve over time. However, most existing agents are developed and evaluated on **_isolated tasks_** under static conditions, which limits their ability to remain effective as environments evolve. To address this challenge, we introduce **DynaWorld**, a dynamic multimodal environment derived from **_real-world_** customer-service interactions, comprising eight long-horizon scenarios and 1,400 user-agent interaction episodes. By connecting successive episodes through controlled environmental changes, DynaWorld enables systematic evaluation of **_how agents adapt as the environment evolves_**. To further strengthen agent adaptation under such dynamics, we propose **DynEvo**, a self-evolving multi-agent framework that continually refines externalized experience and strategies from environmental feedback without updating model parameters. Extensive results on DynaWorld show that existing agents struggle under changing environments, whereas **DynEvo** consistently achieves stronger task performance than standard LLM agents and strong self-evolving baselines, while remaining interaction-efficient with fewer dialogue turns. Further analyses reveal shift-specific degradation and recovery patterns that static settings cannot capture, highlighting the need to evaluate and evolve multimodal agents in dynamic environments.