A double-digit benchmark jump from rewriting the harness alone — no weight updates — is exactly the kind of result that reframes where agent progress comes from: not just bigger models, but smarter scaffolding around them. The Apache-2.0 release is the strategically important part, since harness engineering has largely been proprietary craft so far. In our view the claim deserves cautious optimism: test-time harness rewriting is effectively self-improvement that sidesteps retraining costs, but one benchmark and one model — GLM-5, notably not a Google model — is thin evidence of generality. The open questions are inference cost, since rewriting adds latency and tokens, and whether gains survive harder, less gameable tasks. If it holds up, expect every serious agent stack to grow a harness-optimization layer within months.