arXiv: cs.AI preprintsInternational6 November 2026
Position: Agentic Evolution is the Path to Evolving LLMs
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arXiv:2602.00359v3 Announce Type: replace Abstract: As Large Language Models (LLMs) move from curated training sets into open-ended real-world environments, a fundamental limitation emerges: static training cannot keep pace with continual deployment environment change. Scaling training-time and inference-time compute improves static capability but does not close this train-deploy gap. We argue that addressing this limitation requires a new scaling axis-evolution. Existing deployment-time adaptation methods, whether parametric fine-tuning or heuristic memory accumulation, lack the strategic age
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