GPT-OSS-120B achieves 5.9 pp harness-updating gain on SWE-bench, lowest among all seven evolvers

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• GPT-OSS-120B adherence drops from 0.67 after harness loading to 0.43 at final validation (drift of -0.24)finding0.840
  Mid-tier model shows moderate adherence drift compared to weak and strong tiers
• On MCP-Atlas, harness-benefit peaks at GPT-OSS-120B (7.0 pp), with lower gains at both ends of the base-capability scalefinding0.832
  Replication of non-monotonic harness-benefit pattern on a second benchmark
• Qwen3-235B leads as evolver on SWE-bench with 8.2 pp harness-updating gain but ranks last on MCP with 0.6 ppfinding0.827
  Illustrates benchmark-dependent reshuffling of evolver rankings, no evolver dominates across all substrates
• GPT-OSS-120B achieves a skill-load rate of 0.446 on SkillsBenchfinding0.810
  Mid-tier model showing intermediate activation rate between weak and strong tiers
• On SWE-bench, harness-benefit peaks at Qwen3-235B (19.3 pp), while weaker Qwen3-32B gains only 4.4 pp and stronger Opus 4.6 gains only 2.6 ppfinding0.809
  Core finding demonstrating non-monotonic relationship between base capability and harness-benefit
• Qwen3.5-9B evolver achieves highest harness-updating gain on SkillsBench (3.8 pp), exceeding Claude Opus 4.6 (2.3 pp) and Qwen3-235B (1.5 pp)finding0.803
  Case demonstrating that model scale does not predict harness-updating quality
• harness-updating is flat in base capability: models across capability tiers produce updates that yield similar gains, and even the Qwen3.5-9B evolver induces gains comparable to Claude Opus 4.6quote0.791
  Verbatim summary of first major finding from conclusion
• On SWE-bench, Claude Opus 4.6 and Claude Sonnet 4.6 both achieve 7.4 pp harness-updating gain; Claude Haiku 4.5 achieves 8.0 ppfinding0.786
  Full evolver-side SWE results showing comparable performance across Claude family tiers