Say goodbye to calculation errors and slow responses from Large Language Models. Our innovative "Reasoning-Calculation Separation" architecture, combined with professional calculation engines, provides absolutely reliable and efficient intelligent computing power for your critical business scenarios.
Current Large Language Models have insurmountable flaws in mathematical calculations, hindering their application in critical business operations.
Even for basic math problems (like GSM8K), LLMs cannot guarantee 100% accuracy. In finance, healthcare, and industrial scenarios, even minor errors can lead to severe consequences.
Enhancement methods like Chain-of-Thought (CoT) and reward models significantly increase token consumption and response time (several times to tens of seconds), drastically raising computational costs.
Integrating code interpreters (like Python) introduces security vulnerabilities (injection attacks) and increases the complexity for the model to understand and generate code.
These shortcomings prevent enterprises from trusting and widely adopting LLMs for core business processes requiring precise calculations.
We innovatively separate the "reasoning" capability of LLMs from the "calculation" capability of professional computing engines, achieving complementary strengths.
This architecture fundamentally solves the calculation dilemma of LLMs, paving the way for critical business applications.
The "Reasoning-Calculation Separation" architecture perfectly aligns with the four core enterprise demands for LLM computing capabilities.
Ensures 100% accurate, reliable, and consistent results via external professional engines, eliminating probabilistic model uncertainty.
Calculation errors are clearly pinpointed to the LLM's mathematical expression generation, simplifying diagnosis of model understanding or reasoning biases.
Allows targeted improvements to the model's expression generation ability via fine-tuning, prompt adjustments, etc., for rapid issue resolution.
Fast response time (avg. 2s), 50% token reduction, controllable costs, easy large-scale deployment, and smooth user experience.
Based on Qwen2.5-72B and local professional computing engines, we achieved breakthrough results on the GSM8K benchmark.
>97% First-pass Accuracy
>98% Second-pass Accuracy
Up to 100% with manual adjustment
Average response time: 2 seconds
Token consumption reduced by 50%
Elementary Algebra
Matrix Operations
Differential Equations
Convolution Calculations, etc.
Discover how "Reasoning-Calculation Separation" can help you leverage LLMs safely, reliably, and efficiently in your critical business operations. Contact us now for customized solutions or to request a product demo.