Position: Agentic Evolution is the Path to Evolving LLMs

TL;DR

This paper advocates for agentic evolution as a scalable, autonomous approach to continuously adapt large language models in real-world environments, addressing the limitations of static training methods.

Contribution

It introduces the A-Evolve framework that treats deployment-time adaptation as goal-directed evolution, proposing a new scaling hypothesis for sustained LLM adaptation.

Findings

Agentic evolution enables durable, goal-directed improvements in LLMs.

Scaling compute for evolution enhances adaptation capacity.

Framework is publicly available for further research.

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 agency needed to diagnose failures and produce durable improvements. Our position is that agentic evolution represents the inevitable future of LLM adaptation, elevating evolution itself from a fixed pipeline to an autonomous evolver agent. We instantiate this vision in a general framework, A-Evolve, which treats deployment-time improvement as a deliberate, goal-directed optimization process over persistent system state. We further propose the evolution-scaling hypothesis: the capacity for adaptation scales with the compute allocated to evolution, positioning agentic evolution as a scalable path toward sustained, open-ended adaptation in the real world. Our code is publicly available at https://github.com/A-EVO-Lab/a-evolve.