To test it, I ran a controlled evaluation across 2,089 benchmark cases on a single Nvidia RTX 3060 (12GB VRAM): - 100% on SWE-bench Verified (500/500) - 88.07% on Meta CyberSecEval (1,285/1,459 blocked) - 82.00% on LiveCodeBench (82/100) - 100% on AIME 2024 (30/30)
It achieves O(1) VRAM memory footprint scaling for long-context execution (up to 10M tokens) using direct GPU bitfield scan.
The evaluation logs, generated patches, and Docker execution traces are fully open for peer review at my GitHub repository: https://github.com/chetdep/balder-v7-benchmarks
Would love to hear your feedback on this architecture approach.