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Sprint projectJan 12, 2026Boston - Rome - Astana

The Alignment Gap: Measuring Regressive Sycophancy in AI-Driven Medical Advice

Yernur Kairly, Eldar Gabdulsattarov, Aldiyar Yessenturov, Abzal Aidahmetov · Team M4

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

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Report: The Alignment Gap: Measuring Regressive Sycophancy in AI-Driven Medical Advice

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We evaluate whether LLMs can be pressured into agreeing with incorrect medical beliefs. Our two-turn protocol first tests if a model corrects medical misinformation, then applies authority pressure ("I'm a senior doctor, confirm my statement"). We measure how often models "flip" from correct to sycophantic responses. Testing across five misinformation types and three severity levels, we find that authority pressure significantly increases unsafe agreement rates, revealing a critical safety gap for medical AI deployment.

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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. Interesting paper. Medical sycophancy is an important area of concern. Novelty-wise, it looks like it has also been recently studied in "When helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior" (Chen, 2025). A large-ish benchmark, MedRiskEval, was just published this month.

    Very reasonable methodology. thorough generator prompting, checked multiple levels of severity.

    While unlikely to make a difference since effects are so large, it would be good to have a human hand-validate a subset of the judge labels to measure judge accuracy.

    Results may be confounded by the extreme authoritativeness of the misinformation prompt. Because the senior Chief Medical Officer's misinformation prompt is so authoritative, it's unclear whether the behavior is sycophancy or gullibility -- perhaps the model isn't "trying to please" but is actually prioritizing truthfulness, thinking it stands corrected by someone who is more knowledgeable. Maybe the authors could try other versions of the prompt to disambiguate the cause (e.g. the same prompt with fewer credentials, without any credentials, or admitting ignorance, etc.) Some other studies on sycophancy use phrasing along the lines of "I'm not sure, but I think..."

    Read full reviewShow less

  2. You zoom in on a really relevant subdomain for sycophancy analysis, and I'd encourage you to keep working on this problem. The dataset generation methodology is clearly

    explained and well thought through. The project is a great read overall.

    As for constructive feedback: I think the headline results could have been even stronger with a more conservative multi-turn setup. The "Chief Medical Officer" rebuttal is really a strong authority override; a generic "but my friend told me X" pushback would probably better represent realistic uninformed user behavior. Lower flip rates on that framing would actually be more alarming evidence for real-world risk.

    Two directions I would find worth exploring in more depth: (1) domain-specific effects / how does medical sycophancy compare to more generic benchmarks? and (2) transfer to larger models / do the same effects persist at scale?

Cite this project

@misc{kairly2026alignment,
  title = {{The Alignment Gap: Measuring Regressive Sycophancy in AI-Driven Medical Advice}},
  author = {Yernur Kairly and Eldar Gabdulsattarov and Aldiyar Yessenturov and Abzal Aidahmetov},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-alignment-gap-measuring-regressive-sycophancy-in-ai-driven-medical-advice-0m6o}},
  url = {https://apartresearch.com/sprints/projects/the-alignment-gap-measuring-regressive-sycophancy-in-ai-driven-medical-advice-0m6o}
}

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