A next-generation multi-agent collaboration core brain for game development, based on Qwen3.5-122B-A10B-MoE architecture with deep optimization, running smoothly on consumer-grade hardware.

🌐 Official Website: www.5e1.com — Experience the latest V3 version online

🎮 Model Positioning

Traditional general-purpose LLMs often suffer from severe "hallucinations" or provide superficial suggestions when facing complex system architectures, deep engine APIs (Unreal/Unity), and state machine logic in real game development scenarios.

LongShu V3 builds upon the solid foundation of V2.2, achieving a leap from 97B to 99B parameters through architectural optimization and expanded expert coverage. We've performed deeper domain-specific fine-tuning to create the next-generation core brain of the Multi-Agent System — the Commander Model (TIANCE).

It doesn't just understand code; it understands game engineering. It's no longer a chatbot — it's a true "Technical Director + Lead Architect" .

🏗️ REAP Ecosystem

LongShu V3 is the central brain of an enhanced game development agent network:

Role	Codename	Positioning	Core Capabilities
Commander	Tiance	Core brain, logic reasoning hub	Global planning, system decomposition, task dispatch
Architect	Xuangou	Code architecture expert	Tech structure analysis, architecture optimization, tech debt identification
Executor	Moxing	Task execution specialist	Coding, debugging, test case generation
Watcher	Zhuzhao	Monitoring & alerting expert	Log analysis, anomaly detection, risk early warning
Scholar	Wenyuan	Knowledge management expert	Documentation understanding, knowledge graphs, intelligent retrieval
Coordinator	Hengshu	Team collaboration expert	Intelligent task allocation, cross-functional coordination

⚡ Core Technical Highlights (V3 Upgrades)

Hybrid Attention Architecture V3

60-layer network with 3:1 alternating Linear + Full Attention
Enhanced attention head allocation for better long-range dependency capture
Optimized KV-cache compression, reducing memory footprint by 18% compared to V2.2

Long Context Support (512K)

Extended native support from 256K to 524,288 token context
Can ingest entire project codebases, design docs, and API documentation in one shot
Improved positional encoding for ultra-long sequences

Extreme MoE Sparsity (V3)

105 experts, only 10 activated per token (expanded to 99B total parameters)
New expert routing mechanism with dynamic load balancing
Inference speed of ~42 tokens/s on Mac mini M4 Pro 64GB (17% faster than V2.2)

Game Engine-Aware Hybrid Quantization V3

Core 20 experts: high-precision IQ4_NL/Q5_K
Non-core 85 experts: extreme compression IQ2_XXS
Refined quantization calibration with 99.1% core reasoning capability retention (up from 98.6%)

New: Multi-Modal Scene Understanding

Support for analyzing game screenshots and UI mockups
Can interpret level layouts, HUD designs, and visual references
Integrated vision encoder for scene-aware code generation

New: Real-Time Code Context Awareness

Enhanced ability to track cross-file dependencies in large projects
Improved understanding of Unreal Engine Blueprint-to-C++ translation
Better support for Unity ECS/DOTS architecture patterns

🎯 Specialized Training Data

Data Type	Scale	Description
Real Game Projects	68+	MMO, FPS, ARPG, Roguelike, Sandbox, Survival genres
Core Source Code	2.8B+ Tokens	UE5 (C++), Unity 6 (C#), Godot 4, Lua hot-reload frameworks
Engineering Docs	420K+ Pages	GDDs, system breakdowns, game design logic, performance analysis
High-Quality Online Data	13.5B+	StackOverflow gamedev, GitHub Issues, graphics papers, shader libraries
New: QA/Testing Data	850K+	Automated test cases, bug reproduction steps, regression analysis

💻 Hardware Requirements

Configuration	Recommendation
Mac	M2/M3/M4 series, 64GB Unified Memory
PC	Dual RTX 3090/4090 (24GB)
Format	MLX 4-bit quantized (optimized for Apple Silicon)
Speed	~42 tokens/s (Mac mini M4 Pro)

🚀 Use Cases

Automated Test Case Generation — Auto-generate test plans based on code logic and game design specs
Daily Build Error Diagnosis — Analyze compile/runtime errors with fix suggestions and root cause analysis
Level Toolchain Dispatch — Understand design requirements, dispatch executors for level implementation
System Architecture Design — Decompose complex requirements into modular, scalable architectures
Code Review — Review code quality, identify potential issues, suggest performance optimizations
Shader & Rendering Pipeline Optimization — Analyze and optimize graphics code for target platforms
AI Behavior Tree Design — Generate and debug NPC AI logic, patrol routes, and decision trees

🎮 Real-World Application (In Development)

Sakura Dream Sea (樱梦海) — An Eastern Fantasy Open-World MMO Adventure

The Sakura Dream Sea development team is actively collaborating with LongShu V3 as one of the core pilot partners, integrating the enhanced LongShu agent capabilities into their game development pipeline:

Leveraging the Tiance/Commander Model V3 for global task planning with extended 512K context support
Utilizing Xuangou/Architect for Unreal Engine 5 system architecture optimization and code review
Accelerating core gameplay development (combat systems, AI behavior trees) through Moxing/Executor with improved cross-file awareness
Monitoring server performance and anomaly logs with Zhuzhao/Watcher

⚠️ Status: In development — not yet launched. The Sakura Dream Sea project is currently in active development, with LongShu V3 being integrated into the production pipeline. More details will be shared as the project progresses.

📄 License

Apache 2.0 License

LongShu V3 · AI-Powered Partner for Game Development

Experience the latest version at www.5e1.com

5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit

LongShu · Architect-V3