Workshop on Towards Test-Time Continual Learning Agents

Today's frontier models are frozen at deployment: once costly pre- and post-training ends, their knowledge, skills, and reasoning are effectively fixed, and any apparent adaptation comes from prompting, retrieval, memory, or external tools rather than genuine internal learning. Humans do the opposite, continuously acquiring knowledge, refining representations, and reorganizing beliefs through interaction. Today's agents cannot: they fail to internalize new information after deployment, do not improve from repeated mistakes, and erase prior skills when updated naively, while even state-of-the-art robotic systems assume a train/deploy split untenable in dynamic, partially observed, long-horizon, socially situated environments. We define Test-Time Continual Learning Agents as systems that continuously acquire, consolidate, and refine knowledge and capabilities during deployment, without catastrophic forgetting or repeated large-scale retraining. Such agents sit at a triple intersection studied today in isolation: continual learning targets mitigating forgetting and enabling knowledge transfer in a supervised setting, test-time adaptation handles distribution shift, LLM work emphasizes retrieval and fine-tuning, and embodied and agentic research prioritizes planning and tool use, none of which centers how a deployed agent should learn sample-efficiently through interaction, how to evaluate it, or how to integrate new skills safely. TTCL 2026 is the first workshop to place this test-time, continual, and agentic intersection at its center, convening these siloed communities around shared terminology, rigorous long-horizon benchmarks, and a hands-on challenge. We aim to catalyze next-generation cognitive agents that learn continually, consolidate experience, and remain reliable, rethinking the boundaries between training and inference, and between memory and learning.