Topic
Stanford University
2 episodes
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RecursiveMAS cuts multi agent AI costs by 75%: researchers
Justy and Cody dig into RecursiveMAS, a research framework that lets multi-agent systems pass latent embeddings instead of text, cutting token usage and speeding up inference while keeping base model weights frozen.
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Agentic Context Engineering Ace Self Improving LLMs via Evolving Contexts Not Fine Tuning
Tech News AI Paper Summary Technology Artificial Intelligence Editors Pick Machine Learning Staff Agentic Context Engineering (ACE): Self-Improving LLMs via Evolving Contexts, Not Fine-Tuning By Asif Razzaq - October 10, 2025 TL;DR : A team of researchers from Stanford University, SambaNova Systems and UC Berkeley introduce ACE framework that improves LLM performance by editing and growing the input context instead of updating model weights. Context is treated as a living “playbook” maintained by three roles— Generator, Reflector, Curator —with small delta items merged incrementally to avoid brevity bias and context collapse.