By skyforbes 
AGENTIC AI | LLMs | AGENTS | SELF LEARNING | NO FINE-TUNING NEEDED
What if machines could reshape their own thinking to grow smarter over time? This review dives into a bold new idea that challenges how we train and trust AI.
Recently, I came across a research paper titled Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models. At first, the title sounded a bit technical, but as I read more, I found the idea quite interesting.
The paper was written by a team of researchers from Stanford University, UC Berkeley, and SambaNova Systems. The lead authors are Qizheng Zhang and Changran Hu, who contributed equally to the work. Other contributors include Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou, and Kunle Olukotun.
Together, they explore how AI models can improve themselves by evolving the way they use context moving beyond just following instructions to actively shaping their own learning process.