Karpathy shares 'LLM Knowledge Base' architecture that bypasses RAG with an evolving markdown library maintained by AI

Karpathy proposes something simpler and more loosely, messily elegant than the typical enterprise solution of a vector ...
Forbes

The Limits Of LLMs And Why The Architecture Must Change

LLMs have delivered real gains, but their momentum masks an uncomfortable truth: More data, more chips and bigger context windows don’t fix what these systems lack—persistent memory, grounded ...
MarketWatch

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

Against this backdrop, the MSA paper sets an ambitious goal: to design an end-to-end trainable latent state memory framework that scales to 100M tokens with linear complexity while maintaining high ...
InfoQ

Intel DeepMath Introduces a Smart Architecture to Make LLMs Better at Math

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
dbta

Context Engineering for AI-Readiness: Enterprise Data Architecture and LLMs

Today's enterprises must extend existing data architectures to support generative AI applications while maintaining accuracy and security standards. As organizations face challenges in connecting LLMs ...

Three ways AI is learning to understand the physical world

Large language models lack grounding in physical causality — a gap world models are designed to fill. Here's how three distinct architectural approaches (JEPA, Gaussian splats, and end-to-end ...
Yahoo Finance

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

The research introduces a novel memory architecture called MSA (Memory Sparse Attention). Through a combination of the Memory Sparse Attention mechanism, Document-wise RoPE for extreme context ...