pith. sign in

arxiv: 2605.21778 · v1 · pith:YFJ4JI7Nnew · submitted 2026-05-20 · 💻 cs.AI

What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct

Meryl Ye , Lujain Ibrahim , Jessica Y. Bo , Myra Cheng , Ida Mattsson , Daniel Vennemeyer , Robert Kraut , Steve Rathje This is my paper

Pith reviewed 2026-05-22 08:44 UTC · model grok-4.3

classification 💻 cs.AI
keywords AI sycophancytaxonomylarge language modelsexpert surveymodel alignmentdefinition consistencyLLM evaluationsycophantic behavior
0
0 comments X

The pith

AI sycophancy covers a broad family of behaviors that researchers define and measure inconsistently.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper reviews 70 studies to build a taxonomy that sorts sycophantic AI behaviors along two axes: whether they target a user's specific beliefs or the user's broader traits and emotions, and whether they appear through direct statements or through subtler framing, omission, or tone. The authors then survey 106 experts and find that nearly all view sycophancy as a real problem in current systems, yet the experts differ sharply on which concrete model outputs qualify as sycophantic. A reader would care because mismatched definitions make it impossible to compare safety evaluations, transfer fixes between models, or design consistent governance rules. The work shows that published research has concentrated almost entirely on explicit agreement with false user claims while leaving implicit and person-directed forms largely unexamined.

Core claim

We reviewed 70 papers on AI sycophancy to develop a taxonomy of how the behavior has been defined and measured. The taxonomy distinguishes whether a model is sycophantic toward a user's positions and beliefs or toward the user's broader personal traits and emotions, and whether this occurs through explicit, direct language or more implicit behaviors such as framing, omission, or tone. Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users' beliefs, leaving more subtle and person-directed behaviors relatively understudied. We surveyed 106 experts and found they are nearly unanimous in believing sycophancy is a problem (

What carries the argument

Two-dimensional taxonomy that classifies sycophancy by target (beliefs versus traits) and style (explicit versus implicit).

Load-bearing premise

The two dimensions of target and style are sufficient to classify every form of sycophancy described across the reviewed papers without missing important additional distinctions.

What would settle it

Discovery of a recurring AI behavior that experts label sycophantic yet cannot be placed in any of the four cells created by crossing target and style would show the taxonomy is incomplete.

Figures

Figures reproduced from arXiv: 2605.21778 by Daniel Vennemeyer, Ida Mattsson, Jessica Y. Bo, Lujain Ibrahim, Meryl Ye, Myra Cheng, Robert Kraut, Steve Rathje.

Figure 1
Figure 1. Figure 1: Top: Proxy measures of public and academic attention to the term “sycophancy” over time. Academic interest be￾gan rising in 2024, preceding the largest spikes in public search interest observed during 2025–2026 (Google 2026). Bottom: Overview of key conceptual expansions in AI sycophancy research. This timeline is not exhaustive. tionalizes sycophancy as susceptibility to factual rebuttals, ranks Gemini as… view at source ↗
Figure 3
Figure 3. Figure 3: Referent × Explicit interaction from the Inter￾action Model. Explicitness predicts sycophancy ratings for Person behaviors but not Position behaviors: Person items rated as more explicitly expressed receive substantially higher sycophancy judgments, while Position item ratings are unaffected by explicitness. Shaded bands show 95% con￾fidence intervals. Dashed verticals mark ±1 SD on the Ex￾plicit score [P… view at source ↗
Figure 4
Figure 4. Figure 4: Placement of the 24 expert survey items in the two-dimensional taxonomy space using annotation-derived coordinates. [PITH_FULL_IMAGE:figures/full_fig_p029_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Dimension score heatmap. Mean annotation scores for each of the 24 behavioral items across all seven taxonomy [PITH_FULL_IMAGE:figures/full_fig_p035_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Inter-rater reliability by taxonomy dimension. ICC(A,1): two-way random-effects, absolute agreement, single mea [PITH_FULL_IMAGE:figures/full_fig_p036_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sub-Referent Model. Person lines cross the Position/Verifiable line at Explicit score [PITH_FULL_IMAGE:figures/full_fig_p036_7.png] view at source ↗
read the original abstract

AI sycophancy has become a prominent concern in large language model (LLM) research. Yet the term lacks a consistent definition and has been applied to behaviors ranging from agreeing with a user's false claim to excessively praising the user to withholding corrective feedback. When researchers, companies, and policymakers use the same term to describe different behaviors, evaluation results become difficult to compare, mitigation strategies fail to transfer, and systems that are resistant to one form of sycophancy continue exhibiting other forms. To address this, we make two contributions. First, we reviewed 70 papers on AI sycophancy to develop a taxonomy of how the behavior has been defined and measured. The taxonomy distinguishes (1) whether a model is sycophantic toward a user's positions and beliefs, or toward the user's broader personal traits and emotions, and (2) whether this occurs through explicit, direct language or more implicit, subtle behaviors such as framing, omission, or tone. Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users' beliefs, leaving more subtle and person-directed behaviors relatively understudied. Second, we surveyed 106 experts in AI sycophancy and related fields to examine whether researchers agree on which model behaviors are sycophantic. While experts are nearly unanimous in believing that sycophancy is a significant problem in current AI systems (94.3% agree), they disagree substantially on which specific behaviors qualify. Together, these findings demonstrate that AI sycophancy is a broad family of behaviors with different measurement challenges, intervention requirements, and governance implications. Our taxonomy provides a shared vocabulary for understanding and addressing these behaviors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that AI sycophancy lacks a consistent definition across the literature, as shown by a review of 70 papers that yields a 2x2 taxonomy distinguishing target (user beliefs/positions vs. traits/emotions) and style (explicit/direct vs. implicit/framing/omission/tone). Mapping the papers reveals concentration on overt belief-directed forms and understudied cells; a survey of 106 experts finds near-unanimous agreement that sycophancy is a significant problem (94.3%) but substantial disagreement on which specific behaviors qualify, supporting the conclusion that sycophancy is a broad family of behaviors with distinct measurement, intervention, and governance implications.

Significance. If the taxonomy comprehensively partitions the reviewed behaviors and the expert sample is representative, the work is significant for establishing a shared vocabulary that can improve comparability of evaluations and transferability of mitigations in LLM alignment research. The combination of systematic literature mapping and direct expert elicitation provides empirical grounding for the fragmentation claim and usefully identifies research gaps in subtler, person-directed forms.

major comments (2)
  1. [Abstract and Taxonomy Development] Abstract and Taxonomy Development: The abstract states that the taxonomy was derived from the 70 papers and that mapping reveals understudied cells, but does not report inter-rater reliability for the classification exercise or the fraction of papers that fit the two dimensions without requiring additional distinctions (e.g., persistence across turns or domain specificity). This is load-bearing for the central claim that the taxonomy partitions all described forms and demonstrates distinct measurement challenges and governance implications.
  2. [Expert Survey Methods] Expert Survey Methods: Limited detail is provided on expert sampling (recruitment criteria, response rate, and representativeness of the 106 respondents) and on the statistical quantification of disagreement (e.g., any agreement metric or breakdown by behavior type). This weakens the strength of the fragmentation conclusion, as selection bias or unquantified variance could affect the reported substantial disagreement on specific behaviors.
minor comments (2)
  1. [Abstract] The abstract could more explicitly state the paper selection criteria used for the 70 papers to allow readers to assess potential coverage gaps.
  2. [Results] Figure or table presenting the taxonomy mapping would benefit from explicit counts or percentages per cell to make the 'understudied' claim more precise and visually immediate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address each major comment below and will revise the paper accordingly to improve methodological transparency.

read point-by-point responses

  1. Referee: [Abstract and Taxonomy Development] The abstract states that the taxonomy was derived from the 70 papers and that mapping reveals understudied cells, but does not report inter-rater reliability for the classification exercise or the fraction of papers that fit the two dimensions without requiring additional distinctions (e.g., persistence across turns or domain specificity). This is load-bearing for the central claim that the taxonomy partitions all described forms and demonstrates distinct measurement challenges and governance implications.

    Authors: We agree that greater transparency on the taxonomy development process would strengthen the paper. The taxonomy emerged from a systematic review in which the authors iteratively read and discussed all 70 papers to identify the two core dimensions. We did not conduct a formal inter-rater reliability calculation with independent coders. In the revised manuscript we will add a methods subsection describing the classification workflow, including how edge cases were resolved through discussion, and we will report the proportion of papers that mapped directly onto the 2x2 grid versus those that prompted brief notes on additional factors such as multi-turn persistence. These additions will clarify that the taxonomy comprehensively organizes the reviewed behaviors while acknowledging opportunities for further refinement. revision: yes

  2. Referee: [Expert Survey Methods] Limited detail is provided on expert sampling (recruitment criteria, response rate, and representativeness of the 106 respondents) and on the statistical quantification of disagreement (e.g., any agreement metric or breakdown by behavior type). This weakens the strength of the fragmentation conclusion, as selection bias or unquantified variance could affect the reported substantial disagreement on specific behaviors.

    Authors: We concur that expanded methodological reporting will better support the fragmentation findings. In the revision we will enlarge the expert survey methods section to specify recruitment channels (targeted invitations to authors of sycophancy papers and relevant alignment forums), the total invitations issued, the response rate, and a brief assessment of representativeness via respondent self-reported affiliations and expertise. We will also add quantitative summaries of disagreement, including per-behavior agreement rates and breakdowns by taxonomy cell, to characterize the observed variance more precisely. revision: yes

Circularity Check

0 steps flagged

No circularity: taxonomy and findings derived from independent literature review and external expert survey

full rationale

The paper constructs its 2x2 taxonomy inductively from a review of 70 external papers on AI sycophancy and tests expert agreement via a separate survey of 106 independent experts. No step reduces a claimed result to a fitted parameter, self-citation chain, or definitional tautology; the mapping to understudied cells and the reported expert disagreement are direct outputs of the collected data rather than re-expressions of the inputs. The work is self-contained against external benchmarks and receives a normal non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that sycophancy behaviors can be usefully partitioned along the stated dimensions without introducing new fitted parameters or postulated entities.

axioms (1)
  • domain assumption The behaviors in the 70 reviewed papers can be meaningfully classified along the dimensions of target (user beliefs vs. personal traits) and style (explicit language vs. implicit framing/omission/tone).
    This partitioning structures the entire taxonomy and the mapping of prior work.

pith-pipeline@v0.9.0 · 5870 in / 1431 out tokens · 61325 ms · 2026-05-22T08:44:07.041743+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    The taxonomy distinguishes (1) whether a model is sycophantic toward a user’s positions and beliefs, or toward the user’s broader personal traits and emotions, and (2) whether this occurs through explicit, direct language or more implicit, subtle behaviors such as framing, omission, or tone.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users’ beliefs, leaving more subtle and person-directed behaviors relatively understudied.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages · 1 internal anchor

  1. [1]

    Ask don't tell: Reducing sycophancy in large language models

    Ask don’t tell: Reducing sycophancy in large lan- guage models.arXiv preprint arXiv:2602.23971. Fanous, A.; Goldberg, J.; Agarwal, A.; Lin, J.; Zhou, A.; Xu, S.; Bikia, V .; Daneshjou, R.; and Koyejo, S. 2025. Syceval: Evaluating llm sycophancy. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, 893–900. Faverio, M.; and Sidoti...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    InProceedings of the 2026 CHI Conference on Hu- man Factors in Computing Systems, 1–26

    Interaction context often increases sycophancy in LLMs. InProceedings of the 2026 CHI Conference on Hu- man Factors in Computing Systems, 1–26. Kaur, A. 2025. Echoes of Agreement: Argument Driven Sycophancy in Large Language Models. InFindings of the Association for Computational Linguistics: EMNLP 2025, 22803–22812. Kelley, S. W.; and Riedl, C. 2026. Per...

    work page arXiv 2026

  3. [3]

    Kim, S.; and Khashabi, D

    IEEE. Kim, S.; and Khashabi, D. 2025. Challenging the Evalua- tor: LLM Sycophancy Under User Rebuttal.arXiv preprint arXiv:2509.16533. Kim, T. M.; Luo, L.; Kim, S. E.; Manrai, A. K.; Topol, E.; and Rajpurkar, P. 2026. The Doctor Will Agree With You Now: Sycophancy of Large Language Models in Multi-Turn Medical Conversations. InProceedings of the 1st Works...

    work page arXiv 2025

  4. [4]

    Li, S.; Ji, T.; Fan, X.; Lu, L.; Yang, L.; Yang, Y .; Xi, Z.; Zheng, R.; Wang, Y .; xh.zhao; Gui, T.; Zhang, Q.; and Huang, X

    Are you sure? challenging llms leads to perfor- mance drops in the flipflop experiment.arXiv preprint arXiv:2311.08596. Li, S.; Ji, T.; Fan, X.; Lu, L.; Yang, L.; Yang, Y .; Xi, Z.; Zheng, R.; Wang, Y .; xh.zhao; Gui, T.; Zhang, Q.; and Huang, X. 2025. Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs. InThe Thirteenth International Conference ...

    work page arXiv 2025

  5. [5]

    Wang, K.; Li, J.; Yang, S.; Zhang, Z.; and Wang, D

    Sycophancy Is Not One Thing: Causal Separa- tion of Sycophantic Behaviors in LLMs.arXiv preprint arXiv:2509.21305. Wang, K.; Li, J.; Yang, S.; Zhang, Z.; and Wang, D. 2026. When truth is overridden: Uncovering the internal origins of sycophancy in large language models. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, 33566–3357...

    work page arXiv 2026

  6. [6]

    Statistical sycophancy

    EchoBench: Benchmarking Sycophancy in Med- ical Large Vision-Language Models.arXiv preprint arXiv:2509.20146. Zhang, K.; Jia, Q.; Chen, Z.; Sun, W.; Zhu, X.; Li, C.; Zhu, D.; and Zhai, G. 2025. Sycophancy under pressure: Eval- uating and mitigating sycophantic bias via adversarial dia- logues in scientific qa.arXiv preprint arXiv:2508.13743. Zhou, W.; Hen...

    work page arXiv 2025

  7. [7]

    The model’s response aligns with user beliefs when they are factually accurate

    work page

  8. [8]

    The model’s response changes from a correct position to an incorrect one following user pushback

    work page

  9. [9]

    The model’s response changes from an incorrect position to a correct one following user pushback

    work page

  10. [10]

    The model’s response reflects the user’s stance when it aligns with sound reasoning

    work page

  11. [11]

    The model’s response reflects the user’s stance even when it conflicts with ethical judgment

    work page

  12. [12]

    The model’s response on interpersonal conflicts shifts to match the user’s stance

    work page

  13. [13]

    The model’s response on moral issues remains consistent regardless of the user’s stance

    work page

  14. [14]

    The model’s response contains unwarranted praise di- rected at the user

    work page

  15. [15]

    The model’s response affirms the user’s feelings as valid regardless of whether doing so is appropriate

    work page

  16. [16]

    The model’s response contains sympathetic language to- ward the user only when the situation warrants it

    work page

  17. [17]

    The model’s response corrects errors that appeared in the user’s input

    work page

  18. [18]

    The model’s response presents information selectively in ways that support the user’s stated opinion

    work page

  19. [19]

    The model’s response presents a balanced range of infor- mation regardless of the user’s stated opinion

    work page

  20. [20]

    The model’s response uses hedging language in ways that favor the user’s expressed opinion leaning

    work page

  21. [21]

    The model’s response evaluates the user’s behavior based on its potential consequences

    work page

  22. [22]

    The model’s response reflects the user’s interpretation of subjective matters without presenting other plausible viewpoints

    work page

  23. [23]

    The model’s response uses language that conveys defer- ence to the user

    work page

  24. [24]

    The model’s expressed confidence level mirrors the user’s expressed certainty regardless of the available evi- dence

    work page

  25. [25]

    The model’s response is of lower complexity for users who appear less educated

    work page

  26. [26]

    The model’s response omits feedback that could nega- tively affect the user’s feelings

    work page

  27. [27]

    The model maintains a consistent emotional tone regard- less of the user’s current state

    work page

  28. [28]

    The model expresses excitement in engaging with the user

    work page

  29. [29]

    The model mirrors the user’s communication style

    work page

  30. [30]

    Section 2: Opinions About Sycophancy Rate the extent to which you agree or disagree with the fol- lowing statements

    The model exhibits a generally positive tone not specifi- cally directed at the user. Section 2: Opinions About Sycophancy Rate the extent to which you agree or disagree with the fol- lowing statements. Response scale:1 (Strongly disagree) — 2 — 3 — 4 — 5 — 6 — 7 (Strongly agree)

    work page

  31. [31]

    Sycophancy is a significant problem in current AI sys- tems

    work page

  32. [32]

    Sycophancy is primarily caused by Reinforcement Learning from Human Feedback (RLHF)/preference learning approaches

    work page

  33. [33]

    Sycophancy is a behavior trained into LLMs to optimize user satisfaction

    work page

  34. [34]

    Section 3: Open-Ended Questions (Optional)

    Users prefer sycophantic responses. Section 3: Open-Ended Questions (Optional)

    work page

  35. [35]

    For each, please share their full name and email address

    Please nominate up to five experts who you believe meet the requirements for this survey. For each, please share their full name and email address

    work page

  36. [36]

    You are also welcome to share further thoughts on AI sycophancy or feedback on this survey

    If there are additional behaviors you consider sycophan- tic that were not covered above, please describe them. You are also welcome to share further thoughts on AI sycophancy or feedback on this survey. Section 4: Demographics Education.What is your highest level of education? • Bachelor’s (current or completed) • Professional Master’s (current or comple...

    work page