Run evals at K=10 and K=20 to bridge K=5 (ours) vs K=100 (literature) gap

[UditAkhourii]

External research review by u/mxriverlynn pointed out that the academic evidence for divergent-convergent separation (A4: CreativeDC, arXiv 2512.23601, reporting 51.5–63.5% novelty improvement and 72% diversity advantage) is measured at K=100 parallel samples. ADHD's current evals are at K=5. The K-gap is real and the paper implicitly leans on A4-style numbers without running at A4-style K.

Action:

• Extend the eval harness to support configurable K (already trivially possible via framesPerRun).
• Re-run the same six-problem suite at K=5, K=10, K=20 with the same LLM-as-judge methodology.
• Add a new table to EVALS.md reporting win-rate as a function of K.
• If the win rate flattens or degrades above K=5, document that honestly. If it scales, ADHD's positioning gains a quantitative claim.

Cost note: at K=20, the per-run LLM call count is ~25 calls. Across six problems, ~150 calls per condition. Three conditions (K=5/10/20) = ~450 calls. Roughly $15-30 in API costs at current Sonnet pricing. Feasible.

Risks worth measuring:

• Critic context overload at high K (already tracked as Critic context overload at high branch count — pairwise/chunked scoring #7) becomes the dominant cost/quality bottleneck before win-rate gains.
• Likely interaction: K helps until critic saturates, then degrades. Finding the inflection point is itself a finding.

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Raised by u/mxriverlynn in adhd-application-to-han.md, validation point V8.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs.

[UditAkhourii]

Closing as part of a backlog reset — re-filed (where still relevant) as a fresh, more fully-specced issue.