Mandela

LilMGenius/paperthin/skills/depth/mandela

作者 LilMGenius7d5dc6235990230e45a24a25c527d81701da5458无许可证收录于 2026年10月9日更新于 2026年10月9日

Audit any eval, metric, experiment, or benchmark for leakage — does external ground-truth enter independently, or are the model, scorer, and designer just confirming a result no outside truth ever produced? Use before trusting any 'how we'll know it worked' — an A/B, a holdout, a score, a validation — and whenever a result feels too clean or self-confirming. Walks an 8-pattern leakage taxonomy and returns only the patterns that fire, each with an independence fix. Read-only.

AI 生成的概览

审计评估、指标、实验或基准是否存在泄漏,检查外部真实基准是否独立进入。

功能
针对指定的验证(如评估、指标、实验、留出集或基准)套用八种泄漏模式分类法。它会识别验证的组成部分(模型、评分器、设计者、数据集),追问外部真实基准是否独立进入,并只报告命中的模式。每个命中项都会被命名,并附上独立性修复建议。该技能为只读,不会重写实验。
适用场景
在信任任何关于如何衡量成功的说法之前使用,例如 A/B 测试、留出集、分数或验证。它也适用于结果看起来过于干净或自我印证的情况。可用于高风险验证,并可选地引入一名仅获得验证设计的新上下文审计者。
运行要求
无需脚本或特殊工具,仅为说明性内容。智能体需要待审计的验证设计或描述。只读,无需凭据或网络访问。

Audit a validation for leakage: does outside ground-truth actually enter, or is everyone confirming a result no one independently produced?

Goal

The name is the Mandela Effect — a whole population confidently remembers something that never independently happened; a leaky validation is the same shape. Walk the 8 patterns below. mandela checks one thing: whether a validation is independent, or whether the designer, model, and scorer are only confirming each other.

Workflow

  1. Identify the validation (eval / metric / experiment / holdout / "how we'll know"). Name its components — what plays model, scorer, designer, dataset.
  2. Ask the core question: does external ground-truth enter independently?
  3. Test the validation against all 8 patterns below (some apply only to certain components — a human subject, a scorer); report only the ones that fire, each by name.
  4. Give the independent-ground-truth fix for each hit.

The 8 leakage patterns

  1. Recall, not reason — a memorized answer recited instead of one actually derived; the system already knows the result it is supposedly computing.
  2. Wrong null hypothesis — an ablation that removes a surface label but not the underlying signal the system actually exploits, so the "control" still leaks.
  3. Shared hallucination — two components verifying each other; circularity reported as a number.
  4. Tautology — a scorer grading buckets it drew itself.
  5. Verifier = designer — a private, unreproducible recipe in a holdout's clothes.
  6. Shared-pool bias — train and holdout drawn from one labeler pool, so one bias enters both sides.
  7. Frame injection — a question that hands the subject the hypothesis.
  8. Demand characteristics — measured subjects who know they're being measured.

Rules

  • Subtlety that bites twice: you can blind the output value and still leak the collection recipe.
  • Read-only — name the leak and the independence fix; don't rewrite the experiment.
  • For a high-stakes validation, you may add one independent fresh-context auditor (N=1) handed only the validation design, not this session's reasoning, to re-run the 8-pattern taxonomy blind; the default remains same-session and read-only.

Verification

Turn mandela on this audit:

  1. Run patterns #3–#5 on yourself: are you a scorer grading buckets you drew (Tautology, #4)? is the verifier the designer (#5)? is your verdict a shared hallucination with the design's own claims (#3)?
  2. Could a reader who didn't run the audit reach your verdict from the cited evidence alone — independent ground-truth?
  3. The report names the root, not a laundry list.

来源与署名

来源:LilMGenius/paperthin位于skills/depth/mandela提交7d5dc62

许可证: 无许可证

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