# Model distillation

The work introduces GenoTrace, a codon-aware extension of green-list watermarking that modulates a teacher model's generation bias with two token-level factors—codon position and organism-specific codon usage—so that the resulting synthetic sequences remain auditable after distillation into a smaller student; in a three-seed GenomeOcean-500M-to-100M experiment the joint configuration achieves a mean audit score of 17.88 and 94.5% detection at a fixed threshold, retaining 49.0% detection after key-aware token substitution (versus 0% for the available single-seed plain-watermark comparator) and 47.0% after combined mechanism-targeted nucleotide edits.

The work introduces GenoTrace, a codon-aware extension of green-list watermarking that modulates a teacher model's generation bias with two token-level factors—codon position and organism-specific codon usage—so that the resulting synthetic sequences remain auditable after distillation into a smaller student; in a three-seed GenomeOcean-500M-to-100M experiment the joint configuration achieves a mean audit score of 17.88 and 94.5% detection at a fixed threshold, retaining 49.0% detection after key-aware token substitution (versus 0% for the available single-seed plain-watermark comparator) and 47.0% after combined mechanism-targeted nucleotide edits.

The work introduces Mid-Harness, which samples and verifies candidate actions at the model-harness boundary before forwarding one for execution while keeping the generator and harness unchanged, and studies action-level test-time compute scaling: on TerminalBench-Lite a GPT-5.6 Sol verifier raises TMAX-9B Pass@1 from 50.00% to 68.03% with 8 sampled actions, wider sampling yields little benefit under weak verification, pairwise verification performs best among the evaluated self-verification mechanisms, distilling the stronger verifier's responses into TMAX-9B further improves Pass@1, and combining action scaling with trajectory scaling (Best-of-N and Sequential Refine) reaches higher success at lower estimated token cost.

The work introduces Mid-Harness, which samples and verifies candidate actions at the model-harness boundary before forwarding one for execution while keeping the generator and harness unchanged, and studies action-level test-time compute scaling: on TerminalBench-Lite a GPT-5.6 Sol verifier raises TMAX-9B Pass@1 from 50.00% to 68.03% with 8 sampled actions, wider sampling yields little benefit under weak verification, pairwise verification performs best among the evaluated self-verification mechanisms, distilling the stronger verifier's responses into TMAX-9B further improves Pass@1, and combining action scaling with trajectory scaling (Best-of-N and Sequential Refine) reaches higher success at lower estimated token cost.