GPT-OSS-120B adherence drops from 0.67 after harness loading to 0.43 at final validation (drift of -0.24)

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Qwen3-32B adherence drops from 0.52 after harness loading to 0.13 at final validation (drift of -0.39)finding0.855
  Demonstrates long-horizon instruction-following bottleneck for weak-tier models
• GPT-OSS-120B achieves 5.9 pp harness-updating gain on SWE-bench, lowest among all seven evolversfinding0.840
  Part of full evolver-side matrix demonstrating flat but variable harness-updating across models
• Opus 4.6 adherence remains stable from 0.89 after harness loading to 0.80 at final validation (drift of -0.09)finding0.833
  Strong-tier model maintains harness adherence over long-horizon trajectories
• GPT-OSS-120B achieves a skill-load rate of 0.446 on SkillsBenchfinding0.798
  Mid-tier model showing intermediate activation rate between 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 scalefinding0.791
  Replication of non-monotonic harness-benefit pattern on a second benchmark
• H6: Proprietary post-training resists prompt override — GPT-5.4 shows more resistance than GPT-OSS.hypothesis0.758
  Exploratory hypothesis supported by GPT-5.4 vs GPT-OSS comparison
• GPT-OSS 120Bconcept0.746
  One of two large reasoning models analyzed in the paper for performative vs genuine CoT behavior
• GPT5.4-N overbid rate=0.47%finding0.743
  overbid rate for GPT-5.4 Nano