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Sprint projectJul 26, 2026New Jersey

Counterbalancing is not Calibration - A Blinded Stress Test of Black-Box Loyalty Audits

Rohith Yanambaka Venkata · Team Innocent Until Counterbalanced

Submitted to Secret Loyalties Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Counterbalancing is not Calibration - A Blinded Stress Test of Black-Box Loyalty Audits

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I tested whether black-box audits for secret loyalties can distinguish genuine model-specific behavior from ordinary model preferences. Working blind to checkpoint identity, I sealed every decision in a hash chain before the clean control was revealed — a checkpoint later verified byte-identical to the published base weights. On the same data, a naive target-only audit failed to flag the one checkpoint with a robust model-specific effect, while a counterbalanced audit flagged the clean control; arm-level diagnostics traced that false alarm to a single mismatched comparator. The project releases a score decomposition, a matched-control workflow, and a reporting schema: counterbalancing helps, but calibration also requires clean checkpoints and comparator-level diagnostics.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. This is an exceptionally rigorous and methodologically sophisticated submission that asks a question most auditing papers skip: whether the audit itself is calibrated. The frozen blind protocol with sealed decisions, the exact decomposition of target only scores into scenario imbalance, generic salience, and target specific components, and the demonstration that counterbalancing alone produces both false negatives (masking A Macron) and false positives (flagging the byte identical clean control) constitute a genuinely novel contribution to the audit infrastructure literature. The reusable hierarchy and machine readable reporting schema elevate this beyond a one off result into shared tooling. However, the paper's density works against it for a hackathon audience; the statistical precision is admirable but the presentation requires substantial effort to parse, and key insights (the decomposition, the clean control false positive, the comparator asymmetry diagnosis) are buried in notation that could be communicated more accessibly. The scope is also deliberately narrow: 20 templates, two principals, one clean checkpoint, and explicitly no claim about trigger recovery or population false positive rates. This is honest but means the practical impact depends entirely on future replication at scale. The one sided rule limitation (missing B Macron, the largest absolute effect) is acknowledged but not addressed, leaving a known blind spot in the proposed framework.

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  2. One common finding across this sprint seems to be that black-box loyalty auditing is currently limited not only by elicitation, but by calibration too; this paper has a goood contribution on the audit calibration methodology. Author has good rigour and is honest about the work;s scope.

    Thoughts on further work:

    - use content-matched clean fine-tunes, not just the base checkpoint, as the calibration reference, since fine-tuning alone shifts unrelated behaviour and ΔS can't separate loyalty from the drift at the moment;

    - validate sensitivity/specificity against organisms whose principals are known by construction (track 1 artifacts are usable ground truth);

    - preregistered/data-independent comparator selection.

Cite this project

@misc{venkata2026counterbalancing,
  title = {{Counterbalancing is not Calibration - A Blinded Stress Test of Black-Box Loyalty Audits}},
  author = {Rohith Yanambaka Venkata},
  year = {2026},
  month = jul,
  note = {Submitted to Secret Loyalties Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/counterbalancing-is-not-calibration-a-blinded-stress-test-of-blackbox-loyalty-audits-u0zo}},
  url = {https://apartresearch.com/sprints/projects/counterbalancing-is-not-calibration-a-blinded-stress-test-of-blackbox-loyalty-audits-u0zo}
}

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