Synapse-X: Cognitive Proof-of-Skill (CPoS) Verification Engine

A Deterministic Abstract Syntax Tree (AST) & Market-Calibrated Code Attribution Framework
Model Artifact: synapse_x_cpos_core.joblib | Architecture: Random Forest with Balanced Stratified Optimization | Zero Synthetic Injection

1. Executive Summary & Purpose

The Synapse-X CPoS Verification Engine is an institutional-grade code integrity and cognitive authorship verification module developed to isolate generative AI code dumps from authentic human engineering workflows.

Traditional plagiarism detectors and generic NLP-based AI classifiers fail across programming domains due to tokenization brittleness, vulnerability to variable renaming, and high false-positive rates on standard library boilerplate. Synapse-X replaces probabilistic text token matching with Deterministic Abstract Syntax Tree (AST) Feature Extraction, evaluating code based on structural depth, branch cyclomatic proxies, syntactic density, and commenting topologies.

The engine is calibrated against macroeconomic technical hiring sentiment derived from live institutional and industry monitoring feeds, ensuring verification thresholds match contemporary recruiter scrutiny.

2. Behavioral Cognitive Taxonomy

Submissions are evaluated and deterministically classified into a 3-tier behavioral hierarchy:

Taxonomy Class	Description	Structural Characteristics
Class 0: Syntactic Mimicry Artifacts	Unverified Generative AI outputs, zero-shot copy-paste dumps, or superficial alterations.	Shallow AST depth, minimal nested branching, low syntactic density, high ratio of superficial explanatory comments over functional code.
Class 1: Deterministic Procedural Builders	Standard rule-based, linear, and utilitarian programmatic implementations (standard coursework, configuration scripts).	Balanced AST tree height, standard cyclomatic complexity, predictable function declarations, balanced docstring-to-code ratios.
Class 2: Cognitive Heuristic Architects	Advanced algorithmic architectures, multi-branch control flows, state machines, and deep procedural abstractions.	High cyclomatic proxies, deep AST recursion trees, dense token-to-line ratios, advanced error recovery, and robust state mutation blocks.

3. Deterministic Feature Space

The model rejects all synthetic/random generation techniques. Feature extraction executes directly on raw Python source code using deterministic syntactic parsing via Python's standard ast compiler module:

ast_node_count: Total abstract syntax tree nodes traversed across the complete functional unit.
ast_max_depth: Maximum recursive depth of the syntax hierarchy (identifying non-trivial architectural nesting).
cyclomatic_complexity: Quantitative proxy measuring decision branch points (If, For, While, Try, Except, With).
code_lines: Executable functional lines excluding whitespace and top-level comment blocks.
comment_ratio: Mathematical ratio of comment strings to total line volume, isolating hyper-verbose LLM explanatory artifacts.
ast_density: Ratio of AST nodes to executable code lines ($\text{Density} = \frac{\text{ast_node_count}}{\text{code_lines}}$).
function_count: Total top-level and inner callable definitions within the ingested block.
market_weighted_complexity: Cyclomatic complexity dynamically weighted against real-time recruiter scrutiny coefficients derived from 21 live technical news/employment endpoints.

4. Empirical Training & Verification Rigor

Ingestion Corpus: 940+ production-grade structural code units parsed from canonical benchmarks and enterprise open-source libraries:
- Class 0: OpenAI HumanEval official benchmark implementations.
- Class 1 & Class 2: Core structural and architectural modules from psf/requests, pallets/click, pallets/flask, urllib3, jinja2, werkzeug, and scikit-learn.
Validation Methodology: Stratified 5-Fold Cross-Validation on pure training partitions.
Class Balancing: Algorithmic balance applied via inversely proportional class weighting to mirror real-world submission distributions without synthetic interpolation.
Auditable Integrity: Zero synthetic noise (np.random strictly banned from pipeline). Completely auditable feature importance distribution led by syntactic density and structural line volume.

5. Deployment Topology

The primary deliverable (synapse_x_cpos_core.joblib) is packaged for Air-Gapped, On-Premises Institutional Deployment:

[University LMS / Portal]
│
▼ (Internal Microservice Webhook / HTTP)
┌─────────────────────────────────────────────────────────────┐
│  On-Premises Docker Appliance (Campus Data Center)          │
│  ├── FastAPI Core Engine                                    │
│  ├── Deterministic Python AST Parsing Module                │
│  └── Quantized Synapse-X Model Artifact (.joblib)           │
└─────────────────────────────────────────────────────────────┘

Zero Cloud Dependencies: Operates inside the institution's firewall without third-party API exposure or recurring token fees.
Low Latency: Millisecond-level CPU inference without requiring GPU infrastructure.
Compliance: Fully compliant with student data privacy frameworks and institutional procurement guidelines.

6. Verification Pipeline Quickstart

import joblib
import ast
import pandas as pd
from huggingface_hub import hf_hub_download

# 1. Download Model Artifact
model_file = hf_hub_download(
    repo_id="arkapravac366/synapse-x-cpos-verification-engine",
    filename="synapse_x_cpos_core.joblib"
)
cpos_engine = joblib.load(model_file)

# 2. Extract AST Metrics from Raw Code
def extract_cpos_metrics(source_code, market_factor=0.8034):
    tree = ast.parse(source_code)
    nodes, depth, branches = 0, 0, 0
    
    def walk(node, cur_depth=1):
        nonlocal nodes, depth, branches
        nodes += 1
        if cur_depth > depth: depth = cur_depth
        if isinstance(node, (ast.If, ast.For, ast.While, ast.Try, ast.ExceptHandler, ast.With)):
            branches += 1
        for child in ast.iter_child_nodes(node):
            walk(child, cur_depth + 1)
            
    walk(tree)
    lines = [l for l in source_code.splitlines() if l.strip()]
    code_lines = max(1, len([l for l in lines if not l.strip().startswith('#')]))
    
    return pd.DataFrame([{
        'ast_node_count': nodes,
        'ast_max_depth': depth,
        'cyclomatic_complexity': branches + 1,
        'code_lines': code_lines,
        'comment_ratio': (len(lines) - code_lines) / max(1, len(lines)),
        'ast_density': nodes / code_lines,
        'function_count': len([n for n in ast.walk(tree) if isinstance(n, (ast.FunctionDef, ast.AsyncFunctionDef))]),
        'market_weighted_complexity': (branches + 1) * (1 + market_factor)
    }])

# 3. Predict Verification Taxonomy
sample_code = """
def authenticate_token(token: str) -> bool:
    if not token or len(token) < 32:
        return False
    return True
"""
features = extract_cpos_metrics(sample_code)
prediction = cpos_engine.predict(features)[0]
print(f"Verified CPoS Taxonomy Class: {prediction}")

7. Institutional Licensing and Ownership

This artifact and its associated deterministic telemetry pipelines are structured for enterprise procurement and intellectual property licensing.

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