AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents
AgentOdyssey is a novel evaluation framework that procedurally generates open-ended text games to test agents' ability to learn continuously during deployment. It challenges the traditional ML assumption of no learning at test time, interleaving learning and inference throughout. The framework measures world knowledge acquisition, episodic memory, exploration, action diversity, and model cost. Experiments show even the strongest agents fall far below human performance, with short-term memory emerging as a key beneficial mechanism.
[2606.24893] AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents
[Submitted on 29 May 2026]
Title:AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents
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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 learning does not occur at test time by placing agents in a continuous, long-horizon setting that interleaves learning and inference throughout deployment. We further propose a multifaceted evaluation methodology that measures not only game progress but also offers diagnostic tests on world knowledge acquisition, episodic memory, object and action exploration, action diversity, and model cost. We evaluate diverse agent paradigms in the generated games. Our experimental results reveal critical limits in agents' key abilities, as well as factors that influence their meaningful horizon. Although performance scales with stronger base models, even the top agent remains far below human performance, leaving substantial headroom for improvement. Among agent mechanisms, we find that short-term memory benefits multiple agent paradigms and is an important component of agent test-time training.
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Computation and Language (cs.CL)
Cite as: arXiv:2606.24893 [cs.CL]
(or arXiv:2606.24893v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.24893
arXiv-issued DOI via DataCite
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From: Zheyuan Zhang [view email] [v1] Fri, 29 May 2026 22:40:51 UTC (4,002 KB)
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