Luwu.AI Artificial Intelligence Research Lab

Like the guardian of Kunlun, exploring the profound mysteries of AI
陆吾 - Human face, tiger claws, tiger body with nine tails

About Us Explore Projects

Research Areas

🧠

Natural Language Processing

Deep understanding of human language and intelligent dialogue systems

👁️

Computer Vision

Empowering machines with visual perception to understand the world

🎯

Reinforcement Learning

Enabling AI to learn through exploration and autonomous decision-making

🔬

Multimodal Learning

Integrating multiple information sources for comprehensive AI systems

Latest Projects

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LuwuLLM - Lightweight Language Model

2025-12-20

Project Overview

LuwuLLM is a lightweight large language model project focused on providing high-quality Chinese language understanding capabilities in resource-constrained environments.

Core Features

Lightweight Design: Optimized model parameters suitable for edge device deployment
Chinese Optimization: Deep training on Chinese corpora for more accurate understanding
Fast Inference: Optimized inference engine with quick response times
Easy Integration: Simple API interface

Tech Stack

PyTorch
Transformers
ONNX Runtime
FastAPI

Use Cases

Intelligent customer service
Text summarization
Question answering systems
Content generation

Project Status

🚧 In Development - Beta version expected Q1 2026

NLP LLM Open Source

VisionPlus - Multimodal Vision System

2025-12-15

Project Overview

VisionPlus is an advanced multimodal visual understanding system capable of simultaneously processing images, videos, and text information to provide comprehensive visual intelligence solutions.

Core Functions

Image Understanding

Object detection and recognition
Scene understanding
Image caption generation

Video Analysis

Action recognition
Event detection
Video summarization

Multimodal Fusion

Image-text matching
Visual question answering
Cross-modal retrieval

Technical Highlights

✨ Unified Architecture - Single model handles multiple vision tasks
⚡ Efficient Inference - Optimized model structure for real-time processing
🎯 High Accuracy - Achieves SOTA performance on multiple benchmark datasets
🔧 Flexible Deployment - Supports both cloud and edge devices

Computer Vision Multimodal Deep Learning

RLAgent - Universal Reinforcement Learning Framework

2025-12-10

Project Overview

RLAgent is a universal reinforcement learning research framework providing a complete toolchain from algorithm research to practical deployment.

Framework Features

🧩 Modular Design

Standardized environment interface
Pluggable algorithm components
Configurable training pipeline

🎮 Rich Algorithm Library

DQN / DDPG / SAC
PPO / A3C
Multi-agent algorithms
Model-based RL

📊 Visualization Tools

Real-time training monitoring
Policy visualization
Performance analysis

🚀 Distributed Training

Multi-GPU parallel processing
Distributed sampling
Experience replay management

Supported Environments

OpenAI Gym
MuJoCo
Unity ML-Agents
Custom environments

Typical Applications

Robot Control - Robotic arm manipulation, drone flight
Game AI - Strategy games, real-time combat
Resource Optimization - Task scheduling, path planning
Automated Trading - Quantitative strategy optimization

Quick Start

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from rlagent import Agent, Environment

env = Environment('CartPole-v1')
agent = Agent('PPO', env)
agent.train(episodes=1000)

Project Status

✅ Stable Release - v1.0 Available

Reinforcement Learning Robotics Automation

Latest Blog Posts

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2025-12-23 Deep Learning

Understanding the Transformer Architecture

Introduction

Since its introduction in 2017, the Transformer architecture has become the cornerstone of natural language processing. This article provides an in-depth yet accessible explanation of Transformer’s core mechanisms.

Why Do We Need Transformers?

Before Transformers, RNNs and LSTMs were the mainstream methods for sequence modeling. However, they had several limitations:

Sequential Computation - Cannot be parallelized, leading to low training efficiency
Long-range Dependencies - Difficulty capturing long-distance contextual information
Gradient Issues - Long sequences prone to vanishing gradients

Transformers elegantly solve these problems through the self-attention mechanism.

2025-12-20 Computer Vision

Diffusion Models: A New Paradigm for AI Image Generation

What are Diffusion Models?

Diffusion Models are a class of powerful generative models that create high-quality images from random noise through a gradual denoising process.

Core Concepts

Diffusion models involve two key processes:

1. Forward Diffusion Process (Adding Noise)

Gradually add Gaussian noise to data until it becomes pure noise:

$$ x_t = \sqrt{\alpha_t} x_0 + \sqrt{1-\alpha_t} \epsilon $$

2. Reverse Denoising Process (Generation)

Train a neural network to learn the reverse process, recovering data from noise:

2025-12-18 Reinforcement Learning

Introduction to Reinforcement Learning: From Zero to AlphaGo

What is Reinforcement Learning?

Reinforcement Learning (RL) is an important branch of machine learning that studies how agents learn optimal policies in an environment through trial and error.

Core Concepts

Agent: The subject that learns and makes decisions
Environment: The world in which the agent operates
State: The current situation of the environment
Action: Operations the agent can perform
Reward: Feedback signal from the environment about actions

Difference Between RL and Supervised Learning

Dimension	Supervised Learning	Reinforcement Learning
Learning Method	Learn from labeled data	Learn from interactions
Feedback	Immediate correct answers	Delayed reward signals
Objective	Fit labels	Maximize cumulative rewards
Exploration	No exploration needed	Need to balance exploration vs exploitation

Mathematical Framework: Markov Decision Process

RL problems are typically modeled as Markov Decision Processes (MDP):