| Thank you for your feedback. I have reconsidered the structure and revised it as follows. Please take a look. ## The Heart of Gatekeeper: "6-Axis Three-Dimensional Cross Structure (JCross 6-Axis IR)" Gatekeeper is the "absolute gatekeeper" standing between the user's local environment and the external cloud LLM. Its core technology is the metamodel *"JCross IR (Intermediate Representation)"*, which reinterprets source code not merely as "text," but as a "spatial structure." JCross IR decomposes source code into the following six dimensions (axes): | Axis | Name | Role/Content |
| :--- | :--- | :--- |
| *X-axis* | *Control Flow* | Axis of time and sequence. if branches, loops, exceptions, etc. |
| *Y-axis* | *Data Flow* | Axis of dependency and propagation. Variable assignment, argument passing, etc. |
| *Z-axis* | *Type Constraints* | Boundary axis. Classes, type definitions, generics, etc. |
| *W-axis* | *Memory & Lifecycle* | Lifespan axis. Scope lifespan, memory allocation/deallocation, etc. |
| *V-axis* | *Scope & Hierarchy* | Inclusion axis. Modules, class nesting, etc. |
| *U-axis* | *Semantics & Meaning* | * Most Important* Intent and concrete value axis. Specific variable names, function names, strings, etc. | ### Absolute Security through Physical Extraction of the "U-axis" Simply encrypting variables uniformly prevents LLM from correctly interpreting even the logical structure of the code (loop scope and variable scope). Therefore, Gatekeeper uses a rule-based parser to analyze the AST (Abstract Syntax Tree) and physically extracts only the "U-axis (semantics)" from this six-axis cross, isolating (masking) it in a local vault (JCrossIRVault). Only the "pure logic and structural framework" consisting of the remaining five axes (X, Y, Z, W, V) is sent to the external cloud LLM. ### Actual Transformation Image (Before / After) Let's look at how the code is specifically hidden. *[Before] Raw Source Code (as it is on the user's machine)*
```swift
func calculateTax(price: Double) -> Double {
let taxRate = 1.21
return round(price * taxRate, 2)
} [After] Masked JCross IR (as it is sent to Cloud LLM)
When sent to LLM, business logic and sensitive information (U axis) are replaced with unique hash tokens, but the *topology (X/Y/Z/V/W axes)* where "function arguments are multiplied and returned as a value" is completely maintained. func FUNC_0xA1B2(VAR_0x3C4: TYPE_DOUBLE) -> TYPE_DOUBLE {
let VAR_0x9D5 = CONST_0xF1
return CALL_0xE5A(VAR_0x3C4 * VAR_0x9D5, CONST_0x02)
}
LLM looks at this symbolized structure puzzle, understands it at the structure level, and returns a patch. Finally, Verantyx restores the original complete code by reinjecting the U axis from the local Vault.
Overall Flow: From Rule-Based Analysis to Inverse Transform
This "extract" to "restore" pipeline is executed by robust rule-based processing, which does not tolerate inference errors.
graph TD
A[Raw Source Code] -->|1. AST Analysis| B[Rule-Based 6-Axis Parser]
B -->|2. 6-Axis Decomposition| C{Gatekeeper Engine} C -->|3. U-Axis Isolation| D[(Local Vault<br>JCrossIR Vault)]
C -->|4. Extraction of Remaining 5 Axis| E[Masked IR<br>FUNC_0x...] E -->|5. Submission| F((Cloud LLM<br>Claude / GPT-4)) F -->|6. Structural Inference| G[GraphPatch JSON<br>Structural Patch]
G -->|7. Patch Application| H{Reverse Transpilation Engine<br>Reverse Transpilation} D -->|8. U-Axis Reinjection| H
H -->|9. Restoration Complete| I[Modified Source Code] |