Titans + MIRAS: Empowering AI with Genuine Long-Term Memory Core Question: How Can AI Models Achieve Human-Like Long-Term Memory? In today’s artificial intelligence landscape, we face a fundamental challenge: how can we enable AI models to remember and utilize accumulated knowledge over time, rather than having goldfish-like seven-second memory? This article delves deep into Google’s groundbreaking Titans architecture and MIRAS theoretical framework, which are redefining AI memory mechanisms, enabling models to learn, update, and retain important information in real-time. 1. The Memory Dilemma of Transformer Architecture Core Question: Why Can’t Existing Transformer Models Handle Ultra-Long Sequences? The Transformer architecture revolutionized …
EM-LLM: Mimicking Human Memory Mechanisms to Break Through Infinite Context Processing Barriers Introduction: The Challenge and Breakthrough of Long-Context Processing Modern Large Language Models (LLMs) excel at understanding short texts but struggle with extended contexts like entire books or complex dialogue records due to computational limitations and inadequate memory mechanisms. In contrast, the human brain effortlessly manages decades of experiences—a capability rooted in the episodic memory system’s efficient organization and retrieval. Inspired by this, EM-LLM emerges as a groundbreaking solution. Published at ICLR 2025, this research introduces dynamic segmentation and dual-channel retrieval mechanisms into LLMs, enabling them to process 10 …