State of AI Code Quality 2026
We formally verified code from Bolt, Lovable, v0, and Replit.
Here's what we found.
Every tool in your pipeline checks syntax. None check semantics.
Ask the AI to fix itself
Flat at 87%Ask another AI to check
Regresses at k=5Prove it mathematically
100% at k=3Pass rate by number of verification-remediation iterations
“More iterations made it worse.”
LLM-as-Judge introduces false positives. The model “fixes” correct code based on incorrect feedback, causing regression from 99.4% to 97.2% at k=5.
Before (correct)
def factorial(n):
if n <= 1: return 1
return n * factorial(n-1)After LLM-judge “fix”
def factorial(n):
if n <= 1: return n
return n * factorial(n-1)Without a formal oracle, you can't distinguish signal from noise.
7
Missing Implementation
33%5
Security / Auth
24%4
Configuration
19%3
Fake / Mock Data
14%2
Performance
10%300 real GitHub bug-fix tasks. Not synthetic benchmarks.
AI code quality degrades sharply with complexity. The verification gap grows with every dependency, every edge case, every integration.
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View on GitHubDeadline: August 2, 2026. Formal verification documentation for AI-generated code.
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