AI Code Evaluation & Benchmark Sandbox

DataAnnotation Standard RLHF v2.9 Merge Overlapping Intervals
Target Problem Specification Empirical Dynamic Range: N=100 → N=5,000

Task: Given an array of intervals [start, end], merge all overlapping intervals and return non-overlapping intervals in sorted order. Evaluating Model Alpha (Naïve in-place quadratic merger) against Model Beta (Linearithmic pre-sorted sweep).

■ Model Alpha (Naïve Quadratic) Inefficient O(N²)
Click lines to toggle rubric annotation flags Language: JavaScript
Intended Complexity: O(N²) worst-case due to repeated in-place nested splicing inside while loop.
■ Model Beta (Optimal Sorting) Optimal O(N log N)
Production-grade canonical algorithm Language: JavaScript
Intended Complexity: O(N log N) pre-sort + single O(N) sweep; space complexity O(N).
Empirical Asymptotic Complexity Benchmark (Live Browser Execution) Status: Ready (Default Fixture Loaded)
Model Alpha @ N=5,000
134.2 ms
Model Beta @ N=5,000
2.4 ms
Alpha Scaling Curve
Steep Quadratic O(N²)
Beta Scaling Curve
Linearithmic O(N log N)
Throughput Delta
55.9x Faster
Boundary & Edge-Case Stress Suite Pass Rate: 100% Boundary Logic
Test Case Name Input Fixture Model Alpha Model Beta
Both pass boundary correctness tests, but Model Alpha fails on throughput limits at large N.
Reviewer Rubric & Comparative Verdict Standard RLHF Dimension Rubric
Final Assessment Verdict:
Model Beta strongly preferred for production systems due to optimal asymptotic complexity and predictable O(N log N) behavior.
Enjoy this tool? Build your own with Super