finding vulns in a large codebase is a search problem with a huge negative space and what aisle measured is classification accuracy on ground-truth positives, t

finding vulns in a large codebase is a search problem with a huge negative space and what aisle measured is classification accuracy on ground-truth positives, those are different tasks so a model that correctly labels a pre-isolated vulnerable function tells me almost nothing about that model's ability to surface the same function out of a million lines of unrelated code under a realistic triage budget the experiment i'd want to see is running each of the small models as an unsupervised scanner across full freebsd then return the top-k suspicious functions per model and compute precision at recall levels that correspond to real analyst triage budgets, if mythos s findings show up in the small models top 100, i'd call that meaningful but if they only surface under 10k false positives then the cost advantage collapses because analyst triage time is more expensive than frontier model compute to begin with second thing i keep coming back to is the $20k mythos number is a search budget not a model cost, small models at one hundredth the per-token price don't give us one hundredth the total budget when the search process is the same shape, i still run thousands of iterations and the issue for autonomous vuln research is how fast the reward signal converges and the aisle post doesn't touch any of this