AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents
arXiv:2606.24893v1 Announce Type: new Abstract: For agents to learn continuously from interaction with the world at test time, they must be able to explore effectively, acquire new world knowledge and skills, retain relevant episodic experiences, and plan over long horizons. To evaluate these key abilities of test-time continual learning agents, we introduce AgentOdyssey, a novel evaluation framework that procedurally generates open-ended text games with rich entities, world dynamics, and long-horizon tasks. Critically, AgentOdyssey goes beyond the conventional machine learning assumption that
The continuous drive for more autonomous and adaptable AI systems necessitates robust evaluation frameworks that simulate real-world complexities.
Evaluating test-time continual learning for AI agents is crucial for developing truly general-purpose AI that can adapt and learn independently in dynamic environments.
The introduction of AgentOdyssey provides a new, more sophisticated benchmark for evaluating the long-horizon learning and exploration capabilities of AI agents, moving beyond static datasets.
- · AI Agent Developers
- · Continual Learning Researchers
- · AI Evaluation Frameworks
- · Gaming and Simulation Platforms
- · AI models reliant on static, pre-defined datasets
- · Simplified AI evaluation metrics
This framework will drive innovation in AI agent architectures capable of open-ended exploration and knowledge acquisition.
Improved AI agents could accelerate automation in complex domains, collapsing white-collar workflows and requiring fewer human interventions.
The ability of agents to learn continuously and adapt in open-ended simulations could form a crucial component of future AGI systems, potentially impacting multiple sectors simultaneously.
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Read at arXiv cs.CL