What changed
A preprint posted on August 15, 2026 bets on harness scaling: improve the execution system around an agent without changing its model weights 1. It introduces StateM, an agent-native runtime organized around durable states, phase-local context, checked transitions, recoverable runbooks and versioned procedural practices 1. It reports Terminal-Bench 2.1 runs for GPT-5.5 xhigh, GPT-5.6 Sol xhigh and GPT-5.6 Luna 1, the three runs this brief argues from.
What survives a model upgrade
A runbook that still works after the model underneath it changes is what I would put in version control this week. The runbook transfers unchanged to GPT-5.6 1, and the frozen profile raises GPT-5.6 Luna from 76.7 to 85.4 percent 1. The published reuse spans one model generation and the tiers inside it.
| Run | Reported accuracy |
|---|---|
| StateM with GPT-5.6 Sol xhigh 1 | 95.3 percent across 445 trials |
| GPT-5.6 Luna under the frozen profile 1 | 76.7 to 85.4 percent |
The case against reading this as a portability result is that it is one lab’s arithmetic on its own runs. The Terminal-Bench 2.1 leaderboard reports the accuracy with a confidence interval, 83.8 percent plus or minus 1.2 for the rank-1 run, Claude Code with Fable 5 2; StateM reports single point estimates from its own runs 1. I think the design stays retrofittable whether or not those estimates hold, which is why Impact asks you to reuse the structure and re-measure on your own tasks.
Impact on your team
Do not rebuild your runtime on a five-day-old preprint. What is retrofittable this week is the design: durable state, phase-local context, checked transitions, and a runbook you version like code 1.
Record how much of your harness survives your next model upgrade: re-run the runbook unchanged and write down what you had to rewrite.
Try it on your own task set first. The transfer evidence covers one model generation, so a general guarantee beyond that is, I think, your bet to place.