Minki Kang

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.