arXiv:2606.24901v1 Announce Type: new Abstract: Continual learning capability is critical for Industrial LLMs, as deployed models must be continuously updated to meet evolving requirements and environments, rather than repeatedly retrained from scratch. However, most existing research focuses on improvements on static benchmarks, failing to capture real industrial needs. In this survey, we reformulate Industrial Continual Learning (ICL) for LLMs as a closed-loop update-and-release problem in a versioned ecosystem, where updates propagate hierarchically to industrial, application-specific model
The rapid deployment of large language models (LLMs) in industrial settings is revealing the practical limitations of static, one-off training paradigms, necessitating continuous adaptation.
This research addresses a critical need for industrial LLMs to remain relevant and effective over time, moving beyond academic benchmarks to real-world operational challenges.
The shift from isolated model training to a 'closed-loop update-and-release' ecosystem for LLMs fundamentally alters their lifecycle management and maintenance.
- · Industrial AI solution providers
- · Companies with mature MLOps pipelines
- · Enterprises leveraging custom LLMs
- · Researchers in continual learning
- · Companies relying on static, infrequently updated LLMs
- · Open-source models without robust update mechanisms
Industrial LLMs will become more resilient and adaptive to changing operational environments and data distributions.
The development of specialized tools and platforms for managing continual learning in complex LLM ecosystems will accelerate.
This approach could lead to highly specialized, self-evolving AI agents integrated deeply into enterprise workflows, indistinguishable from the underlying systems over time.
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Read at arXiv cs.LG