The Blind Machine

Independently review The Blind Machine paper the way a careful, skeptical referee would — verifying every claim against evidence rather than accepting it.

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Install the whole repository into your agent's skills directory in one command — it unpacks as a ready-to-use review/ folder (change -C to your skills path):

curl -fsSL https://blindmachine.org/skills/review/archive | tar xz -C ~/.claude/skills

Or fetch just the recipe to read or execute inline:

curl https://blindmachine.org/skills/review.md
review / references / review-dimensions.md

65 lines · 2.94 KB · sha256:2e4602f57e30…4f312ea2

Review dimensions

Work through all six. For each finding, record severity, location, the problem, the
evidence, and a concrete fix.

1. Claim-evidence integrity

Trace every headline number to its source and confirm it matches (see
claim-evidence-map.md). Specifically:

  • The synthetic exactness taxonomy: six applications, max_error 0, 128-bit
    ciphertext sizes 262,282 / 1,310,882 / 2,621,791 bytes, 5× and 10× premiums.
  • The real-genome studies (Section 11): E5 mean absolute AF delta ≈ 0.0609; E6 max
    F_ST-like 0.1363 and 624 suppressed rows; E7 recovery rates (1.000 under
    min-N-20, 0.000 under freeze, 0.125 under query budget 5); E8 exact product
    moments and max r2 1.0000.

Flag any value that does not match, any transposed digit, and any table whose rows
disagree with its cited CSV/JSON.

2. Security analysis (Section 9)

  • Confirm the coverage matrix (Table 4) and the per-attack catalog agree: every
    threat in the matrix has a structured subsection, and every subsection maps to a
    matrix row.
  • Confirm every attack subsection uses the same seven-field template (Adversary,
    Attack, Assets at risk, Platform control, Verification hook, Status, Residual
    risk / evidence) and that each Status is drawn from the fixed vocabulary
    (Prevented / Detectable / Mitigated / Residual / Out of scope), consistently with
    the matrix.

3. Honesty and overclaiming

  • The real-genome studies must be framed as aggregate-only workflow demonstrations
    on public reference data — NOT biomedical validation or population-genetics
    estimates.
  • The F_ST-like and LD r2 values must be labeled simplified small-panel contrasts;
    the E8 r2 = 1.0000 pair must be flagged as a small-count artifact; the
    below-quorum E8 caveat (25 samples < floor of 30) must be present.
  • The certificate honesty box must state that the certificate binds artifacts and
    policy facts and does NOT by itself prove correct computation, correct decryption,
    contributor identity, or bounded output-privacy leakage.
  • Flag any sentence that claims more than the evidence supports.

4. Citation and cross-reference integrity

  • Every reference R1-R37 is both defined and cited, with no gaps or orphans.
  • Every “Section N”, “Table N”, and “Figure N” reference resolves to the right
    target; tables are numbered 1-16 and figures 1-8; the Appendix A index matches the
    body.

5. Reproducibility

  • The paper gives a concrete, runnable reproduction path (Appendix B); the commands
    exist; the open CLI, open applications, and public data are sufficient to
    reproduce every result without the hosted service. Optionally run the replicate
    skill and confirm the numbers.

6. Scope and framing

  • The contribution claims (Section 2) are supported and appropriately bounded.
  • The threat model’s assumptions are stated.
  • The limitations are acknowledged: single keyholder, metadata leakage,
    output-privacy as the largest residual risk, and synthetic-plus-small-real
    evidence.

Part of the Independently review The Blind Machine paper agent skill.

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