Technical Report Analysis
LongCat-Flash-Thinking-2601 Technical Report Analysis
This report introduces LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability...
Executive Impact Summary
LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models across a wide range of agentic benchmarks...
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Relative Gain in Training Reward
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Inference Speedup with Zigzag Attention
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Faster RL Training (vs. Synchronous)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Architectural Innovations
Training & Optimization
Reasoning & Performance
560B
Total Parameters (MoE)
27B
Activated Parameters per Token
Mixture-of-Experts (MoE) Architecture
LongCat-Flash-Thinking-2601 employs a 560-billion-parameter Mixture-of-Experts (MoE) architecture, with 27 billion activated parameters per token. This design allows for superior agentic reasoning capabilities while maintaining computational efficiency. The unified training framework combines domain-parallel expert training with subsequent fusion, enabling robust behavior under diverse conditions.
Enterprise Process Flow
High-Level Domain Spec
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Synthesize Tools & DB Schemas
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Construct Tool Dependency Graph
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Sample Seed Tool Chain
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Generate Task & Env
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Progressive Environment Expansion
Robust Training under Noisy Environments
| Training Strategy | VitaBench (Avg@4) | VitaBench-Noise (Avg@4) |
|---|---|---|
| ColdStart | 10.0 | 6.3 |
| Training w/o Noise | 28.6 | 13.3 |
| Training w/ Noise | 29.3 | 20.5 |
Training with noise significantly improves robustness in imperfect environments, as shown by the VitaBench-Noise results. This systematic approach incorporates environmental imperfections through a curriculum-based strategy.
Scalable Asynchronous Agentic RL Framework (DORA)
LongCat-Flash-Thinking-2601 extends the DORA (Dynamic ORchestration for Asynchronous Rollout) framework for stable and efficient large-scale multi-environment training. This enables scalable and stable training with up to 32,000 environments executing concurrently across more than 20 domains, improving training efficiency by 2 to 4 times faster than synchronous training.
Heavy Thinking Mode: Jointly Expanding Reasoning Depth and Width
The Heavy Thinking Mode enables effective test-time scaling of reasoning. It decomposes challenging problems into complementary stages, allowing the model to explore diverse solution paths while progressively refining its reasoning. This mode consistently outperforms self-consistency methods, with performance advantages increasing significantly as computational budgets grow. It leverages parallel trajectory exploration and iterative reasoning refinement.
- AIME-25 (Avg@16): 100.0%
- IMO-AnswerBench (Avg@4): 86.8%
State-of-the-Art Agentic Reasoning
LongCat-Flash-Thinking-2601 achieves state-of-the-art performance among open-source models on a wide range of agentic benchmarks, including agentic search, agentic tool use, and tool-integrated reasoning. It attains 73.1% on BrowseComp, 77.7% on RWSearch, 88.2% on T²-Bench, and 29.3% on VitaBench, establishing it as the leading open-source model for these tasks.
Quantify Your AI Advantage
Our AI solutions significantly enhance operational efficiency, leading to substantial cost savings and reclaimed employee hours.
Your AI Transformation Roadmap
A structured approach to integrating LongCat-Flash-Thinking-2601 into your enterprise.
Phase 1: Discovery & Strategy
Initial consultation, needs assessment, and AI strategy alignment for your enterprise.
Phase 2: Data Integration & Model Customization
Secure data integration, model fine-tuning, and custom agent development.
Phase 3: Pilot Deployment & Iteration
Small-scale deployment, performance monitoring, and iterative refinement based on feedback.
Phase 4: Full-Scale Integration & Support
Enterprise-wide rollout, comprehensive training, and ongoing optimization with dedicated support.
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