pyclustering-selective-joint

Task gallery / Data processing / data science / Version 1

Reference solution (oracle): Unverified
No-op resistance check: Unverified
QA verdict: Not recorded
Aggregate eligibility: Eligible version

The task

Add joint selective inference after k-means clustering to the pyclustering checkout at /repo. We need to test differences among several estimated clusters using the same data that produced the clusters, including when the tested cluster pairs were chosen after inspecting their separation. Provide both a known-variance test and an exact unknown-variance test, including data-dependent D-squared initialization. Computa…

Excerpt only; not a complete executable task specification. Task ID task_462d3c6816b7413b · version 1. Selection status: not fully recorded.

Results by model and effort

This exact task version only. Every rate is passes / valid runs with a Wilson 95% interval; n≤2 remains counts only. Macro aggregation does not apply within a single task. Baselines are summarized in the quality gates, not ranked. A passed no-op resistance check means that doing nothing did not solve the task.

Current evidence

Model / effort	Verifier result [95% Wilson]	Diagnostic labels / valid	HARNESS_ERROR · excluded	Outcome classes
GPT-6 Astra · default (unverified)	0/7 · 0.0% [0.0%, 35.4%]	0 / 7	0	GOOD_FAILURE: 0 BAD_FAILURE: 0 GOOD_SUCCESS: 0 BAD_SUCCESS: 0

Legacy evidence

Model / effort	Verifier result [95% Wilson]	Diagnostic labels / valid	HARNESS_ERROR · excluded	Outcome classes

Difficulty, with uncertainty

Selected-best difficulty: 100.0% [64.6%, 100.0%]. This is one minus the best measured current-row pass rate among rows with at least five valid runs.

The maximum is selected from the observed rows, so the estimate is optimistic. Its Wilson interval is not adjusted for model selection. “Hard” requires qualifying GOOD_FAILURE evidence; the threshold flag additionally requires the interval and recorded quality gates to meet the documented rule. This is not independent validation. Threshold flag: no.

Every recorded run

7 recorded trials, including controls. Diagnostic labels do not replace verifier reward. Labels and quality checks are source declarations, not independently attested here. Internal evidence remains private; a missing public URL is shown explicitly. Records with invalid or absent rewards are not scored.

Run / model	Date / source	Reward / eligibility	Diagnostic class / source	Resources	Evidence
`trial_ed9f4055910f45cd` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_ee939b2bb576447c` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_3b88e1eb625d44ea` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_0ba479e02a5b4c0f` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_523d9299873d4690` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_77c5565fd4e14893` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available
`trial_55f8907c78134787` GPT-6 Astra · default (unverified)	Oct 20, 2026 console	0 · valid	No diagnostic label derived	Tokens: — Turns: — Cost: —	Not publicly available