Multi-Agent Framework

LongCat Flash utilizes a sophisticated multi-agent synthetic data framework that generates high-difficulty, high-quality training samples. This approach enables the model to handle complex reasoning tasks with exceptional accuracy.

• • Advanced tool usage and integration
• • Multi-step reasoning capabilities
• • Complex environment interaction

Benchmark Performance

LongCat Flash excels in specialized agentic benchmarks, outperforming other open-source models in scenarios requiring sophisticated decision-making and problem-solving abilities.

• • Superior performance on τ²-Bench
• • Excellent results on VitaBench
• • Leading agentic scenario capabilities

Revolutionary MoE Architecture

LongCat Flash employs a cutting-edge Mixture-of-Experts (MoE) architecture with 560B total parameters, dynamically activating 18.6B-31.3B parameters per token (average ~27B). This innovative approach maximizes efficiency while maintaining exceptional performance.

The LongCat Flash architecture represents a significant leap forward in large language model design. By utilizing dynamic expert activation, LongCat Flash achieves remarkable efficiency without compromising on quality or capabilities.

Key Innovations:

• • Zero-computation Experts: Intelligently bypasses unnecessary computation for simple tokens, optimizing resource utilization
• • Shortcut-connected MoE: Reorders computation and communication steps, enabling overlapping of dense layer calculations and MoE layer communication
• • Dynamic Resource Allocation: Adapts computational resources based on token complexity, ensuring optimal performance

The architecture supports single-batch overlap (SBO) for low-latency, high-throughput inference, making LongCat Flash ideal for real-time applications.

Advanced Training Methodology

LongCat Flash's training process represents a significant advancement in large-scale model preparation, utilizing sophisticated techniques developed through extensive research and experimentation.

Training Highlights:

• • Massive Dataset: Trained on over 200 trillion tokens across diverse domains and languages
• • Multi-stage Approach: Progressive training focusing on reasoning, coding, and agentic capabilities
• • Hyperparameter Transfer: Optimal configurations discovered on smaller models and scaled using theoretical alignment

The training process incorporates a comprehensive stability suite including routing, activation, and optimizer controls to ensure robust training across thousands of accelerators.