Independent, hardware-grounded evaluation across doctoral academic reasoning and autonomous production code synthesis. Real telemetry without unanchored mocks or synthetic heuristics.
Designed by the Center for AI Safety (CAIS) to measure frontier academic problem-solving at the Ph.D. level. Problems require multi-step formal mathematical reasoning, theoretical physics derivations, and code-emulated symbolic computation.
Evaluating autonomous bug resolution against authentic, human-authored GitHub issues across standard open-source Python repositories (Django, SymPy, scikit-learn, Sphinx).
Native Rust Tree-sitter parsers pre-cognitively validate syntax trees before applying diffs. 100% of generated patches apply cleanly without syntax failures.
ReDoS-immune sentinels reject destructive whole-file rewrites, ensuring minimal surgical modifications that preserve existing type signatures and docstrings.
Direct C-ABI FFI bindings eliminate secondary LLM roundtrips by healing indentation and syntax errors deterministically in under 5 microseconds.