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If you run coding agents that commit autonomously, this shows a practical pattern for cutting both false negatives and false-positive noise in secret scanning — small fine-tuned classifiers bracketing a deterministic regex scanner, at lower cost and latency than calling a frontier model, with the weights released so you can run it yourself.

Factory.ai's research post details an upgrade to its autonomous-coding secret-detection system, adding two fine-tuned models that sit on either side of a deterministic pattern scanner: one 'risk' model catches secrets the scanner misses, and a 'downgrade' model clears false-positive flags by reasoning over a scrubbed code window.

Trained and evaluated on Samsung's CredData benchmark, the models reportedly match or beat frontier models on both tasks at much lower cost and latency, and Factory is releasing the model weights publicly.