arXiv:2605.28705v1 Announce Type: new Abstract: In-context learning (ICL) derives its power from enabling Large Language Models to adapt to new tasks via prompt-based reasoning alone, entirely bypassing the need for parameter updates. Existing theories primarily study ICL in single-task settings, while real-world prompts often contain sequences of heterogeneous tasks, leaving a gap in understanding whether Large Language Models implicitly perform continual learning during inference. To bridge this gap, we propose the first theoretical framework for in-context continual learning, modeling how a
The rapid advancement and widespread deployment of Large Language Models (LLMs) necessitate a deeper theoretical understanding of their learning mechanisms beyond single-task settings, particularly as real-world applications involve continuous, heterogeneous data streams.
This research provides a foundational theoretical framework for understanding 'in-context continual learning' in LLMs, which could unlock more efficient and adaptive AI systems that perform better in dynamic environments.
The ability of LLMs to implicitly perform continual learning without parameter updates changes how we conceptualize and develop AI adaptability, moving beyond traditional catastrophic forgetting concerns.
- · AI researchers
- · LLM developers
- · Generative AI sector
Improved performance and broader applicability of LLMs in diverse, real-world deployment scenarios.
Accelerated development of more robust and less resource-intensive AI agents.
Potentially reduces the need for frequent and expensive retraining of large models, decentralizing AI development.
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Read at arXiv cs.LG