Toward Weight-level Self-improving Agents with Meta-knowledge Discovery

Despite the great success of large language models (LLMs), foundationmodel-based agents still face challenges in achieving autonomous selfimprovement: current iterative self-training paradigms are prone to capability collapse, while self-evolving coding agents lack the capacity to drive foundation model evolution, thereby limiting their potential for selfimprovement. To address these problems, an open-ended approach for neural network weight modification is promising. Therefore, a question is how a foundation-model-based agent can learn neural network weights via scaling compute at training and inference time, making it possible to leverage implicit meta-knowledge for self-improvement. In this paper, we argue that the autonomous self-improving AI systems should learn weights from diverse neural networks, e. g., LLMs, and treat their own weights as the real environment for exploring. We propose Generative Agent as Scientist (GAS), a self-evolving framework that aims to realize weight evolution in foundation models by applying open-ended approaches including, e. g., weight generation and weight regularization. During training stage, GAS is trained with large-scale neural network weight data via self-supervised learning. During inference stage, GAS reasons over its own weight patterns and implements continuous weight modification with meta-knowledge discovery, following a scientific paradigm that requires collecting its own internal weight states for diagnosis, formulating and testing falsifiable hypotheses, and then modifying its weights and knowledge base after evaluation. In order to avoid significant deviations, the neural network weights before and after weight modification are merged for regularization.