Skip to content
Apart Research
Apart Research
Contact us
Sprint projectJan 11, 2026dubai

ConvoGraph: Uncovering Structural Sycophancy via Heterogeneous Graph Transformers

tanzeel shaikh, hitesh kaushik, jakob widerberg · Team gacgroup

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

Read the report

Report: ConvoGraph: Uncovering Structural Sycophancy via Heterogeneous Graph Transformers

Open in a new tab (opens in new tab)
Share

As AI systems become more sophisticated, their capacity for sycophancy—prioritizing user validation over factual truth—poses a critical threat to interaction integrity. Current detection methods rely on text semantics, often failing when the domain shifts (e.g., from medical facts to political opinions). We introduce ConvoGraph, a heterogeneous graph neural network that models conversations as relational structures rather than text sequences. By combining Heterogeneous Graph Transformers (HGT) for local interaction patterns with SGFormer for global context, ConvoGraph identifies the "structural signature" of sycophancy. Crucially, we demonstrate Zero-Shot Domain Transfer: trained only on medical misconceptions, ConvoGraph achieves 0.70 F1 on completely unseen domains,multi-turn datasets, including political surveys and philosophical debates. This confirms that sycophancy is a structural phenomenon—a specific pattern of node dominance and edge agreement—transcending topic vocabulary. We release our open-source pipeline to enable structural safety evaluations for future LLMs.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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 project introduces the novel approach of detecting sycophancy using heterogenous graph representations rather than text classification. The idea of detecting structures such as “pressure - agree” and examining cross-domain generalizability is a fresh contribution. If this scales, it could offer new insights into sycophancy. However, given the noisiness and variability of real world data, the patterns identified here may not be reliably reproduced outside of a clean experimental set-up (an issue which the authors acknowledge in their limitations section).

    The project could benefit from more detailed explanation of their methodology, particularly edge construction, and further development of their results and conclusions sections.

  2. I like the concept of finding common sycophantic structural traits, and at face value it "seems to work." Brings up an interesting line of reasoning. Would've liked more detail in parts, and cross-examination of the datasets:

    1. Architecture justification: Would've liked more detail on why this specific graph architecture works better, especially given the expectation that LLMs "just work" for natural language due to scale. Unclear if graphs actually help here.

    2. Agreement vs sycophancy: Not entirely convincing that "detecting sycophancy" here is not just "detecting affirmative statements/agreement." A lot of assumption rests on this distinction.

    Essentially, quite interesting approach - would've given a 5 if it had provided more convincing evidence against the risk that it's basically overfitting on consistent agreement (what if I simply grepped "you're absolutely right!"), or at least some specific causal explanation/ablation that tackles that assumption.

    Read full reviewShow less

Cite this project

@misc{shaikh2026convograph,
  title = {{ConvoGraph: Uncovering Structural Sycophancy via Heterogeneous Graph Transformers}},
  author = {tanzeel shaikh and hitesh kaushik and jakob widerberg},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/convograph-uncovering-structural-sycophancy-via-heterogeneous-graph-transformers-p94x}},
  url = {https://apartresearch.com/sprints/projects/convograph-uncovering-structural-sycophancy-via-heterogeneous-graph-transformers-p94x}
}

More from AI Manipulation Hackathon

All 70 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026