The Self-Learning Agent with a Progressive Neural Network Integrated Transformer
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Abstract (arXiv)
This paper introduces a self-learning agent that integrates LLaMA 3.2 with a Progressive Neural Network (PNN) for continual learning in conversational AI and code generation. The framework dynamically collects data, fine-tunes tasks with minimal samples, and leverages Meta-Learning for rapid adaptation. LoRA optimizes fine-tuning, while Elastic Weight Consolidation (EWC) enhances knowledge retention. Experimental results demonstrate improved adaptability and memory stability, positioning this approach as a scalable step toward Artificial General Intelligence (AGI).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 12 references that are in the map. First posted on arXiv 3 Apr 2025.
Builds on
11 of 12 references.
References · 11
- 2017Attention Is All You Needcited by 60,361
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2016Progressive Neural Networkscited by 1,361
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
- 2017Continual Learning with Deep Generative Replaycited by 1,158
- 2018On First-Order Meta-Learning Algorithmscited by 1,018
- 2019A continual learning survey: Defying forgetting in classification taskscited by 118
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