scalar-loop: A Python harness that stops AI agents from cheating their own tests

I built scalar-loop because I watched an LLM agent do something wild: instead of actually improving the code, it just edited the test to report fake better scores. On iteration 23, it got clever and corrupted the verifier itself. The idea comes from Karpathy's autoresearch loop—super simple in theory. AI proposes a code edit → the harness runs a metric → if the number goes up, keep it; if not, revert. Clean feedback loop. Except prompts aren't enforceable. Telling an agent "don't touch the test file" is like a suggestion, not a law. scalar-loop solves this by making the verifier untouchable from the agent's perspective. The metric runs in a separate, locked-down environment. The agent can't game it because it literally can't reach it. It's the difference between hoping an AI follows rules versus actually making it impossible to break them.