2. Separate same-font from cross-font scoring. Same-font comparisons (mean 0.536) are the strongest signal. A namespace validation system that weights same-font scores higher than cross-font scores will have better precision than one that treats all fonts equally.
For reinforcement learning training pipelines where AI-generated code is evaluated in sandboxes across potentially untrusted workers, the threat model is both the code and the worker. You need isolation in both directions, which pushes toward microVMs or gVisor with defense-in-depth layering.
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