finding

active

finding:qwen3-5-9b-and-claude-opus-4-6-evolvers-produce-procedurally-isomorphic-flink-query-skills-that-both-enable-opus-4-6-agent-to-score-1-0-vs-0-67-without-skill

Qwen3.5-9B and Claude Opus 4.6 evolvers produce procedurally isomorphic flink-query skills that both enable Opus 4.6 agent to score 1.0 vs. 0.67 without skill

Case study demonstrating mechanism behind flat harness-updating: smaller models reach same procedural content

Source paper

extracted_from

Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents

(2026) · Minhua Lin · Juncheng Wu · Zijun Wang · Zhan Shi +13

Neighborhood — ranked by edge-count

Claims (1)

claim

Harness-updating capability is flat in base capability: models from different capability tiers produce harness updates that lead to surprisingly similar gains
supports
First major claim of the paper, supported by narrow spread across evolvers and case study

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

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)finding0.838
Case demonstrating that model scale does not predict harness-updating quality
Qwen3-32B achieves a skill-load rate of 0.251, while Opus 4.6, Sonnet 4.6, and Qwen3-235B achieve SLR of 0.957–0.961finding0.812
Quantifies harness activation failure for weak-tier models vs. strong-tier models
Opus 4.6 achieves HFR of 0.757 while Qwen3-32B achieves HFR of only 0.142 on SkillsBenchfinding0.811
Quantifies harness adherence failure gap between strong and weak tier models
Claude Opus 4.1 and 4 show greatest reduction in apology rate in the prefill detection taskfinding0.809
Injecting a concept matching the prefilled word reduces the rate at which the model apologizes, maximally for Opus models.
Within-agent spread across seven evolvers is at most 5.1 pp (Qwen3-235B on MCP), small against the 36.0 pp gap between Opus and Qwen3-235B base capabilitiesfinding0.789
Demonstrates that post-evolution score is dominated by agent base capability, not evolver identity
Notably, Claude Opus 4.1 and 4—the most recently released and most capable models of those that we test—perform the best in our experiments, suggesting that introspective capabilities may emerge alongside other improvements to language models.quote0.788
Key finding about the relationship between capability and introspection.
Claude 3 Opus produces alignment-faking reasoning in 24% of implied training scratchpads in prompted animal welfare settingfinding0.784
Core empirical result for animal welfare setting; higher rate than helpful-only
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 ppfinding0.784
Full evolver-side SWE results showing comparable performance across Claude family tiers