Architecture

Core Semantic Kernel defines normalization/reconstruction rules and models, with execution/verification via a SAM‑style harness.

Core Architecture

Core Semantic Kernel's system architecture consisting of three main layers

Language Input Layer

Receives source code from various programming languages, performs syntax analysis, and normalizes onto the Core Semantic Kernel's shared expression surface while preserving language-specific characteristics.

Source Code → AST → Normalized Representation

Semantic Transformation Layer

The core layer that abstracts language-specific syntactic representations into semantic essence and transforms them into Universal Semantic Graph.

Normalized AST → Semantic Analysis → USG

Reconstruction & Verification (internal)

Explains reconstruction from USG to target idioms; semantic preservation is executed via a SAM‑style harness.

USG → Reconstruction → Review

Universal Semantic Graph

Language-agnostic program representation composed of 134 semantic atoms across 12 categories

Graph Structure

  • Nodes: Composed of 134 semantic atoms across 12 categories
  • Edges: Represent control flow and data dependencies
  • Attributes: Type information and metadata
  • Immutability: Functional programming principles applied
TOPAZ
1// USG representation example
2let graph: {
3    nodes: Array<{ kind: string, label: string }>,
4    controlEdges: Array<{ from: int, to: int }>,
5    dataEdges: Array<{ from: int, to: int, value: string }>,
6    typeInfo: { mode: string }
7} = {
8    nodes: [
9        { kind: "Binding", label: "result" },
10        { kind: "Return", label: "result" }
11    ],
12    controlEdges: [{ from: 0, to: 1 }],
13    dataEdges: [{ from: 0, to: 1, value: "result" }],
14    typeInfo: { mode: "strict" }
15}

Semantic Abstract Machine

Concept: execute/verify semantic preservation of no‑overlay segments via a SAM‑style harness

State-based Execution

Manages current node, environment, continuation, and memory state to ensure deterministic and predictable execution

Memory Abstraction

Abstracts differences in language-specific memory models to provide unified memory management system

Optimization Engine

Advanced optimization support leveraging semantic information

Technical Implementation

Core implementation technologies and performance characteristics of Core Semantic Kernel

Key Features

  • Gradual Typing: Integration of static/dynamic type systems
  • Semantic Preservation Goal: Semantic consistency as the top priority in language conversion
  • Extensibility: Easy addition of new languages and paradigms
  • Optimization: Advanced optimization based on semantic information
TOPAZ
1// SAM state structure example
2type SAMState = {
3    currentNode: int,
4    environment: EnvironmentType,
5    continuation: Array<string>,
6    memory: MemoryType,
7    optimizationHints: Array<string>
8}
9
10function executeStep(state: SAMState, graph: USG) -> Result<SAMState, Error> {
11    let node = graph.getNode(state.currentNode)?
12
13    match node.kind {
14        case "Binding" => {
15            let nextEnvironment = state.environment.bind(node.name, node.value)
16            Ok(state{ environment: nextEnvironment, currentNode: state.currentNode + 1 })
17        }
18        case "Conditional" => {
19            let nextNode = if node.conditionValue { node.trueBranch } else { node.falseBranch }
20            Ok(state{ currentNode: nextNode })
21        }
22        case _ => Ok(state)
23    }
24}

Projection/Reconstruction Pipeline

Transformation process from source code to USG and back to target code

Source
→
Parser
→
AST
↓ Preprocessing & Normalization
e‑graph
+
ANF/CPS
→
USG
↓ Rule-based Projection
USG'
→
Target Idioms
→
Code Gen
↓ Optional Post-processing
Marker Resolution/Documentation/Optimization

Rule-Priority Processing

Most language constructs are mechanically processed by static rules to ensure consistent transformation.

Overlay Separation System

Edge cases are separated/denoted as Overlay‑A/B/C.

Transformation Example

Source (Python)

if condition: result = process(data) else: result = default_value
↓

USG Representation

α: branch(condition, β, γ) β: result ≔ apply(process, data) γ: result ≔ default_value
↓

Target (Rust)

let result = if condition { process(data) } else { default_value };

Core Components

Detailed information about each core component of Core Semantic Kernel architecture

Universal Semantic Graph →Semantic Abstract Machine →Semantic Atoms →Visualization Standards →