Step 01
Mechanistic Modeling
Raw data decomposition into Quantum waves produces efficient and reliable tokenization, reducing noise while maintaining accuracy.
Technology
How the ECG agent works
What is specific to WaveMedix is the front end: Q-PSI signal encoding. From there the case follows the Ammonix architecture.
Step 02
Quantitative Features
All numerical features extracted from the mechanistic model flow into standard ML classifiers, creating a rich multidimensional feature space trained on many labeled real-world examples.
Step 03
Qualitative Context
All qualitative information, such as names, free text, categorical data, colors, is preserved and positioned within the same space, forming a Knowledge Universe where every point is a complete patient case.
Step 04
Two-LLM RHO-VR System
This dual-LLM architecture is composed of M1, the decision maker, and M2, the skill maker. M1 assesses each case, and M2 reviews M1's decisions and improves the system's Skills through learning with verifiable rewards (RHO-VR).
Step 05
Skill Dictionary
Accumulates verified Skills across cases, serving as structured memory for the system.
Continuous
Learning