LongShu · Architect-V3
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
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