Kimi Linear: How This Hybrid Attention Architecture Masters Million-Token Contexts
1 months ago 高效码农Kimi Linear: Revolutionizing Efficient Attention Architecture for Long Context Processing The Core Challenge in Modern Language Models How can we process million-token contexts while maintaining performance and efficiency? Kimi Linear presents a groundbreaking hybrid attention architecture that successfully addresses this fundamental challenge. As large language models evolve into sophisticated agents capable of complex tool usage and multi-step reasoning, the computational limitations of traditional attention mechanisms have become increasingly apparent. The quadratic time complexity and linearly growing memory requirements of standard softmax attention create significant bottlenecks for real-world applications. Kimi Linear emerges as a comprehensive solution that not only maintains but …
MemAgent: How Reinforcement Learning Solves AI’s Million-Token Memory Crisis?
5 months ago 高效码农MemAgent: Revolutionizing Long-Context Processing with Reinforcement Learning Introduction: The Challenge of Long-Text Processing In the field of artificial intelligence, processing ultra-long text remains a core challenge for language models. Imagine reading a 5,000-page novel and answering a question about a detail from Chapter 3 – traditional models either require massive “memory windows” (causing computational costs to skyrocket) or gradually forget early information as they read. The recently released MemAgent technology proposes a novel approach: by simulating human reading habits, AI can dynamically update its memory like taking notes, maintaining linear computational complexity (O(n)) while achieving near-lossless long-text processing capabilities. This …