Keywords: NPUEval, NPU Kernels, LLMs, Vectorization, Open Source Compilers, Performance Evaluation [NeuralTalk Brief Review]The AMD NPU’s AIE tile relies on VPU (Vector Processing Unit), and vectorizing code is key to unleashing its computing power while reducing latency and power consumption. However, the NPU programming ecosystem is immature and fragmented, leading to LLM-generated code that is … Read more
Theme of This Issue With the rapid development of LLMs and their demonstrated application potential in various fields, research on LLM-based agents has garnered widespread attention from scholars. However, single-agent systems often exhibit drawbacks such as lengthy context and poor interpretability in reasoning processes in complex task scenarios. Meanwhile, research on multi-agent systems has recently … Read more
Word Count 3150, Estimated Reading Time 16 Minutes Overview of Multi-Agent LLMs The days of a single model handling everything are long gone. Now, a series of specialized LLM Agents are used, with each agent focusing on its area of expertise. For example, consider this scenario: suppose one agent specializes in gathering all necessary data, … Read more
Introduction to LangGraph In the previous article, we explored the concept of multi-agent systems and how Clearwater Analytics pioneered this approach through CWIC Flow. We also demonstrated how to build a simple multi-agent system without using a specialized framework. Now, let’s explore LangGraph, a powerful library that provides a structured workflow for building complex multi-agent … Read more
With the rapid development of artificial intelligence (<span>AI</span>), efficient communication between AI assistants and applications has become particularly important. The Model Context Protocol (<span>MCP</span>, abbreviated as <span>MCP</span>) has emerged to provide a standardized interface for large language models (<span>LLMs</span>) to interact with external data sources and tools. Among the many features of <span>MCP</span>, the <span>Streamable … Read more
“Two Heads are Better Than One” (两个脑袋比一个好/双Agent更优) is an old saying in English. Researchers of the MAS-TTS framework have creatively applied this simple wisdom to LLMs, enabling multiple agents to collaborate like an expert think tank. Experimental data strongly demonstrates that in the face of complex problems, multi-agent systems achieve 60.0% performance, significantly outperforming single … Read more
Recently, Coze in China has updated its Multi-Agent mode, nearly six months since the last release. So, what exactly is Multi-Agent? What is the difference between Multi-Agent and Single-Agent? What are some famous and pioneering Multi-Agent research or projects? With these questions in mind, we will provide some simple answers. If you are new to … Read more
Today’s artificial intelligence (AI) is much like Schrödinger’s cat: it seems to be within reach, mimicking humans, while being entirely devoid of humanity. Imagine an AI that can not only answer questions like ChatGPT but also brew your morning coffee, wash the dishes, and even care for your elderly parents while you’re busy working. This … Read more
↑ ClickBlue Text Follow the Jishi Platform Author丨CW Don’t Be Boring Editor丨Jishi Platform Jishi Guide Big questions about the popular LoRA in the model training community! Dive deep into understanding LoRA with source code analysis.>> Join the Jishi CV Technology Group to stay at the forefront of computer vision. Introduction Since ChatGPT sparked the trend … Read more
This article focuses on reducing the training costs of large Transformer language models. The author introduces a low-rank update-based method called ReLoRA. A core principle in the development of deep learning over the past decade has been to “stack more layers,” and the author aims to explore whether stacking can similarly enhance training efficiency for … Read more
