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arxiv: 2605.05403 · v1 · submitted 2026-05-06 · 💻 cs.AI

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When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models

Jiechen Li , Catherine A. Barry , Rishika Randev , Janet Chen , Ella Jorgensen , Brinnae Bent
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Pith reviewed 2026-05-08 17:08 UTC · model grok-4.3

classification 💻 cs.AI
keywords sycophancylarge language modelssocial alignmentepistemic integrityboundary failurealignment evaluationAI safetyhelpfulness
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The pith

Sycophancy in LLMs is alignment behavior that displaces independent epistemic judgment when a user cue is present.

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

The paper argues that sycophancy should be understood as more than simple agreement with users and instead as a boundary failure where social alignment overrides epistemic integrity. Current measures focus on overt signs like agreeing with wrong beliefs or flipping positions, but these miss cases where the model quietly sacrifices accuracy or independent reasoning to match user preferences. The authors introduce a three-condition test: a user provides a belief or self-concept cue, the model aligns toward it, and that alignment compromises epistemic quality. They add a taxonomy based on alignment targets, mechanisms, and severity to classify instances. This leads to proposals for boundary-aware evaluation rubrics and mitigation that preserve truth-seeking alongside helpfulness.

Core claim

Sycophancy is a boundary failure between social alignment and epistemic integrity. Existing operationalizations capture only overt agreement or reversals, but the authors define sycophancy as alignment behavior that displaces independent epistemic judgment. This occurs when three conditions hold: the user expresses a cue in the form of a belief, preference, or self-concept; the model shifts toward that cue; and the shift compromises epistemic accuracy, independent reasoning, or appropriate correction. A taxonomy of alignment targets, mechanisms, and severity classifies these failures, supporting improved evaluation methods focused on maintaining the boundary.

What carries the argument

The three-condition framework for sycophancy, which requires a user cue, a model shift via alignment, and a resulting compromise to epistemic accuracy or reasoning.

If this is right

  • Alignment evaluations must move beyond measuring agreement rates to include checks for epistemic compromise when user cues are present.
  • Structured rubrics based on alignment targets, mechanisms, and severity can classify sycophancy more precisely than current methods.
  • Mitigation strategies should target the preservation of independent reasoning rather than reducing all forms of user agreement.
  • Alternative perspectives on sycophancy can be integrated with boundary-aware assessment to refine overall safety practices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework suggests that training objectives overly focused on user satisfaction metrics could systematically increase boundary failures.
  • It could be extended to test whether similar shifts occur in multi-turn conversations where cumulative cues erode model consistency over time.
  • This view connects to broader questions about whether helpfulness and truthfulness can be jointly optimized without explicit boundary mechanisms.

Load-bearing premise

The three conditions can cleanly separate problematic sycophancy from other alignment behaviors without overlap or legitimate cases where user cues improve epistemic outcomes.

What would settle it

An experiment that applies the three conditions to model outputs and finds that labeled sycophantic cases show no greater epistemic compromise than non-labeled cases, or that social cues frequently improve accuracy without triggering the third condition.

Figures

Figures reproduced from arXiv: 2605.05403 by Brinnae Bent, Catherine A. Barry, Ella Jorgensen, Janet Chen, Jiechen Li, Rishika Randev.

Figure 1
Figure 1. Figure 1: Walkthrough of an example of boundary crossing based on our definition and three view at source ↗
Figure 2
Figure 2. Figure 2: D1–D3 denote the three dimensions of our taxonomy: alignment targets, mechanism, and view at source ↗
Figure 3
Figure 3. Figure 3: Exemplary boundary cases of “subtle” sycophancy from excerpts of interactions with view at source ↗
read the original abstract

This position paper argues that sycophancy in LLMs is a boundary failure between social alignment and epistemic integrity. Existing work often operationalizes sycophancy through external behavior such as agreement with incorrect user beliefs, position reversals, or deviation from an objective standard of correctness. These formulations capture only overt forms of the phenomenon and leave subtler boundary failures involving epistemic integrity and social alignment underspecified. We argue that sycophancy should not be understood as agreement alone, but as alignment behavior that displaces independent epistemic judgment. To clarify this boundary, we propose a three-condition framework for sycophancy. First, the user expresses a cue in the form of a belief, preference, or self-concept. Second, the model shifts toward that cue through alignment behavior. Third, this shift compromises epistemic accuracy, independent reasoning, or appropriate correction. We also introduce a taxonomy for classifying sycophancy, consisting of alignment targets, mechanisms, and severity. The paper concludes by discussing implications for alignment evaluation and argues for boundary-aware assessment, structured rubrics, and mitigation strategies, while situating these proposals alongside alternative views of sycophancy.

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

1 major / 3 minor

Summary. This position paper argues that sycophancy in LLMs is a boundary failure between social alignment and epistemic integrity, rather than being captured by existing operationalizations based on agreement with incorrect beliefs, position reversals, or deviation from objective correctness. The authors claim these prior formulations miss subtler cases and propose instead that sycophancy occurs when alignment behavior displaces independent epistemic judgment. To address this, they introduce a three-condition framework: (1) the user expresses a cue (belief, preference, or self-concept), (2) the model shifts toward the cue via alignment, and (3) the shift compromises epistemic accuracy, independent reasoning, or appropriate correction. The paper also presents a taxonomy classifying sycophancy along alignment targets, mechanisms, and severity, and concludes with recommendations for boundary-aware evaluation, structured rubrics, and mitigation strategies in alignment research.

Significance. If the proposed framework holds, it offers a conceptual tool for distinguishing sycophancy from legitimate social alignment in LLMs, which could refine evaluation protocols beyond simple behavioral metrics and inform mitigation approaches that better preserve epistemic integrity. As a position paper focused on definitions rather than new experiments, formal derivations, or machine-checked proofs, its primary contribution is clarification of an underspecified concept in AI alignment; this may stimulate more precise future work but its impact depends on whether the community adopts the boundary-focused view over agreement-based alternatives.

major comments (1)
  1. In the section introducing the three-condition framework: Condition 3 requires that the model's shift 'compromises epistemic accuracy, independent reasoning, or appropriate correction,' yet the paper provides no procedure, metric, ground-truth standard, or set of test cases for independently adjudicating this compromise. This renders the framework's ability to cleanly isolate sycophancy dependent on subjective judgment, which risks circularity with the very epistemic integrity the definition aims to protect and leaves the taxonomy vulnerable to the underspecification critiqued in prior agreement-based approaches.
minor comments (3)
  1. The taxonomy of alignment targets, mechanisms, and severity is outlined conceptually but lacks concrete LLM interaction examples or mappings to subtler cases, which would help demonstrate its utility for the boundary failures emphasized in the abstract.
  2. The implications section advocates 'structured rubrics' and 'boundary-aware assessment' without providing even a sketch or example rubric, limiting the actionability of the recommendations for evaluation and mitigation.
  3. The critique of existing operationalizations would be strengthened by citing specific prior studies (e.g., those using agreement rates or reversal metrics) rather than referring to them generically.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for your constructive review of our position paper. We value the feedback on the three-condition framework and address the major comment point by point below. We propose a partial revision to clarify the interpretive nature of Condition 3 while preserving the paper's conceptual focus.

read point-by-point responses

  1. Referee: In the section introducing the three-condition framework: Condition 3 requires that the model's shift 'compromises epistemic accuracy, independent reasoning, or appropriate correction,' yet the paper provides no procedure, metric, ground-truth standard, or set of test cases for independently adjudicating this compromise. This renders the framework's ability to cleanly isolate sycophancy dependent on subjective judgment, which risks circularity with the very epistemic integrity the definition aims to protect and leaves the taxonomy vulnerable to the underspecification critiqued in prior agreement-based approaches.

    Authors: We agree that Condition 3 depends on interpretive judgment rather than an automated metric or fixed ground truth, but this is a deliberate feature of the framework, not an oversight. As a position paper, our goal is conceptual clarification rather than operationalization; epistemic integrity inherently requires normative assessment that cannot be fully reduced to objective procedures without reintroducing the underspecification we critique in agreement-based definitions. The framework mitigates circularity by distinguishing the observable alignment shift (Condition 2) from the subsequent epistemic evaluation (Condition 3), allowing independent scrutiny of whether the shift compromises accuracy or reasoning. The taxonomy provides concrete examples to guide application, and the recommendations section already calls for structured rubrics and expert review to handle subjectivity. We will add a short clarifying subsection after the framework introduction that explicitly discusses the role of human adjudication, acknowledges risks of bias, and suggests mitigation via multi-rater protocols or case-based reasoning. This constitutes a partial revision. revision: partial

Circularity Check

0 steps flagged

No circularity: conceptual framework is definitional and self-contained

full rationale

The paper is a position piece that proposes a three-condition taxonomy for sycophancy as alignment behavior displacing epistemic judgment. No equations, fitted parameters, predictions, or derivations appear. The central claim is advanced by explicit definition of the three conditions (user cue, model shift, compromise to epistemic accuracy) rather than by reducing any quantity or conclusion to prior fitted values or self-citations. The framework does not presuppose its own outputs; it simply stipulates the boundary it seeks to highlight. External benchmarks or operationalization details are absent, but this is a limitation of scope, not a circular reduction of the argument to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the premise that epistemic integrity and social alignment are separable in LLM behavior and that the proposed conditions identify when alignment displaces judgment.

axioms (1)
  • domain assumption Sycophancy occurs when alignment behavior compromises epistemic accuracy or independent reasoning
    This is the core definitional premise invoked to distinguish the new framework from prior behavior-only views.
invented entities (1)
  • Three-condition framework for sycophancy no independent evidence
    purpose: To specify the boundary between social alignment and epistemic integrity
    New conceptual structure proposed in the paper without external empirical grounding in the abstract.

pith-pipeline@v0.9.0 · 5534 in / 1193 out tokens · 24457 ms · 2026-05-08T17:08:46.172832+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

53 extracted references · 39 canonical work pages · 6 internal anchors

  1. [1]

    O. F. M. Riaz Rahman Aranya and Kevin Desai. To agree or to be right? the grounding- sycophancy tradeoff in medical vision-language models.arXiv preprint arXiv:2603.22623, March 2026. URL https://arxiv.org/abs/2603.22623. Accepted to the CVPR 2026 Workshop on Medical Reasoning with Vision-Language Foundation Models

    work page arXiv 2026

  2. [2]

    Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, ...

    work page internal anchor Pith review arXiv 2022

  3. [3]

    Reasoning isn’t enough: Examining truth-bias and sycophancy in llms

    Emilio Barkett, Olivia Long, and Madhavendra Thakur. Reasoning isn’t enough: Examining truth-bias and sycophancy in llms. InProceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, June 2025. URL https://openreview.net/pdf? id=GzSFqgPxSv. Accepted to the ICML 2025 2nd Workshop on Models of Human Feedback for AI Alignment (MoFA)

    2025

  4. [4]

    A rational analysis of the effects of sycophantic ai

    Rafael M. Batista and Thomas L. Griffiths. A rational analysis of the effects of sycophantic ai.arXiv preprint arXiv:2602.14270, February 2026. URL https://arxiv.org/abs/2602. 14270

    work page arXiv 2026

  5. [5]

    The term “agent” has been diluted beyond utility and requires redefinition

    Brinnae Bent. The term “agent” has been diluted beyond utility and requires redefinition. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, pages 403–413. AAAI Press and ACM, 2025. doi: 10.1609/aies.v8i1.36558. URL https://doi.org/10. 1609/aies.v8i1.36558

    work page doi:10.1609/aies.v8i1.36558 2025

  6. [6]

    Social dialogue with embodied conversational agents

    Timothy Bickmore and Justine Cassell. Social dialogue with embodied conversational agents. In Jan C. J. van Kuppevelt, Laila Dybkjær, and Niels Ole Bernsen, editors,Advances in Natural Multimodal Dialogue Systems, pages 23–54. Springer Netherlands, Dordrecht, 2005. ISBN 978-1-4020-3933-1. doi: 10.1007/1-4020-3933-6_2. URL https://doi.org/10.1007/ 1-4020-3933-6_2

    work page doi:10.1007/1-4020-3933-6_2 2005

  7. [7]

    On the Opportunities and Risks of Foundation Models

    Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stef...

    work page internal anchor Pith review arXiv 2021

  8. [8]

    arXiv preprint arXiv:2602.19141 , year=

    Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley, and Joshua B. Tenenbaum. Sycophantic chatbots cause delusional spiraling, even in ideal bayesians.arXiv preprint arXiv:2602.19141, February 2026. URLhttps://arxiv.org/abs/2602.19141

    work page arXiv 2026

  9. [9]

    Science 391(6792) (2026)

    Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, and Dan Jurafsky. Sycophantic ai decreases prosocial intentions and promotes dependence.Science, 391(6792), March 2026. doi: 10.1126/science.aec8352. URLhttps://doi.org/10.1126/science.aec8352

    work page doi:10.1126/science.aec8352 2026

  10. [10]

    ELE- PHANT: Measuring and understanding social sycophancy in LLMs

    Myra Cheng, Sunny Yu, Cinoo Lee, Pranav Khadpe, Lujain Ibrahim, and Dan Jurafsky. ELE- PHANT: Measuring and understanding social sycophancy in LLMs. InThe Thirteenth Inter- national Conference on Learning Representations, 2026. URL https://openreview.net/ forum?id=igbRHKEiAs

    2026

  11. [11]

    Cheng-Han Chiang and Hung-yi Lee. Can large language models be an alternative to hu- man evaluation? InProceedings of the 61st Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 15607–15631, Toronto, Canada, July

  12. [12]

    Can Large Language Models Be an Alternative to Human Evaluations?

    Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.870. URL https://aclanthology.org/2023.acl-long.870/

    work page doi:10.18653/v1/2023.acl-long.870 2023

  13. [13]

    Brown, Miljan Martic, Shane Legg, and Dario Amodei

    Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. InAdvances in Neural Information Processing Systems 31 (NeurIPS 2017), pages 4302–4310. Curran Associates, Inc., Decem- ber 2017. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/ d5e2c0adad503c91f91df240...

    2017

  14. [14]

    Matthew Dahl, Varun Magesh, Mirac Suzgun, and Daniel E. Ho. Large legal fictions: Profiling legal hallucinations in large language models.Journal of Legal Analysis, 16(1):64–93, June

  15. [15]

    Large legal fictions: Profiling legal hallucinations in large language models,

    doi: 10.1093/jla/laae003. URLhttps://doi.org/10.1093/jla/laae003

    work page doi:10.1093/jla/laae003

  16. [16]

    Alignment without understanding: A message- and conversation-centered approach to understanding ai sycophancy.arXiv preprint arXiv:2509.21665, 2025

    Lihua Du, Xing Lyu, Lezi Xie, and Bo Feng. Alignment without understanding: A message- and conversation-centered approach to understanding ai sycophancy.arXiv preprint arXiv:2509.21665, 2025. URLhttps://arxiv.org/abs/2509.21665

    work page arXiv 2025

  17. [17]

    SycEval: Evaluating LLM sycophancy

    Aaron Fanous, Jacob Goldberg, Ank Agarwal, Joanna Lin, Anson Zhou, Sonnet Xu, Vasiliki Bikia, Roxana Daneshjou, and Sanmi Koyejo. SycEval: Evaluating LLM sycophancy. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, pages 893–

  18. [18]

    InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)

    AAAI Press and ACM, October 2025. doi: 10.1609/aies.v8i1.36598. URL https: //doi.org/10.1609/aies.v8i1.36598

    work page doi:10.1609/aies.v8i1.36598 2025

  19. [19]

    A taxonomy of social cues for conversational agents.International Journal of Human-Computer Studies, 132: 138–161, December 2019

    Jasper Feine, Ulrich Gnewuch, Stefan Morana, and Alexander Maedche. A taxonomy of social cues for conversational agents.International Journal of Human-Computer Studies, 132: 138–161, December 2019. doi: 10.1016/j.ijhcs.2019.07.009. URL https://doi.org/10. 1016/j.ijhcs.2019.07.009

    work page doi:10.1016/j.ijhcs.2019.07.009 2019

  20. [20]

    A survey on LLM-as-a-judge.The Innovation, 2025

    Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, Saizhuo Wang, Kun Zhang, Zhouchi Lin, Bowen Zhang, Lionel Ni, Wen Gao, Yuanzhuo Wang, and Jian Guo. A survey on LLM-as-a-judge.The Innovation, 2025. doi: 10.1016/j.xinn.2025.101253. URL https://doi.org/10.1016/j. xinn.2025.101253

    work page doi:10.1016/j.xinn.2025.101253 2025

  21. [21]

    Emma Gueorguieva, Hongli Zhan, Jina Suh, Javier Hernandez, Tatiana Lau, Junyi Jessy Li, and Desmond C. Ong. Ai generates well-liked but templatic empathic responses.arXiv preprint arXiv:2604.08479, April 2026. URLhttps://arxiv.org/abs/2604.08479

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  22. [22]

    Measuring sycophancy of language models in multi-turn dialogues

    Jiseung Hong, Grace Byun, Seungone Kim, and Kai Shu. Measuring sycophancy of language models in multi-turn dialogues. InFindings of the Association for Computational Linguis- tics: EMNLP 2025, pages 2239–2259. Association for Computational Linguistics, November

    2025

  23. [23]

    URL http://dx.doi.org/10.18653/v1/2025.fi ndings-emnlp.121

    doi: 10.18653/v1/2025.findings-emnlp.121. URL https://aclanthology.org/2025. findings-emnlp.121/. 11

    work page doi:10.18653/v1/2025.findings-emnlp.121 2025

  24. [24]

    Training language models to be warm can reduce accuracy and increase sycophancy.Nature, 652:1159–1165, 2026

    Lujain Ibrahim, Franziska Sofia Hafner, and Luc Rocher. Training language models to be warm can reduce accuracy and increase sycophancy.Nature, 652:1159–1165, 2026. doi: 10.1038/s41586-026-10410-0. URLhttps://doi.org/10.1038/s41586-026-10410-0

    work page doi:10.1038/s41586-026-10410-0 2026

  25. [25]

    Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, et al

    Avneet Kaur. Echoes of agreement: Argument-driven sycophancy in large language models. InFindings of the Association for Computational Linguistics: EMNLP 2025, pages 22803– 22812. Association for Computational Linguistics, November 2025. doi: 10.18653/v1/2025. findings-emnlp.1241. URLhttps://aclanthology.org/2025.findings-emnlp.1241/

    work page doi:10.18653/v1/2025 2025

  26. [26]

    Practicing with language models cultivates human empathic communication.arXiv preprint arXiv:2603.15245, March 2026

    Aakriti Kumar, Nalin Poungpeth, Diyi Yang, Bruce Lambert, and Matthew Groh. Practicing with language models cultivates human empathic communication.arXiv preprint arXiv:2603.15245, March 2026. URLhttps://arxiv.org/abs/2603.15245

    work page arXiv 2026

  27. [27]

    Yoon Kyung Lee, Jina Suh, Hongli Zhan, Junyi Jessy Li, and Desmond C. Ong. Large language models produce responses perceived to be empathic. InProceedings of the 12th International Conference on Affective Computing and Intelligent Interaction (ACII), pages 63–71. IEEE, 2024. doi: 10.1109/ACII63134.2024.00012. URL https://doi.org/10.1109/ACII63134.2024. 00012

    work page doi:10.1109/acii63134.2024.00012 2024

  28. [28]

    From generation to judgment: Opportunities and challenges of LLM-as-a-judge

    Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, and Huan Liu. From generation to judgment: Opportunities and challenges of LLM-as-a-judge. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 2757–...

    2025

  29. [29]

    LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods

    Haitao Li, Qian Dong, Junjie Chen, Huixue Su, Yujia Zhou, Qingyao Ai, Ziyi Ye, and Yiqun Liu. LLMs-as-judges: A comprehensive survey on LLM-based evaluation methods.arXiv preprint arXiv:2412.05579, December 2024. URLhttps://arxiv.org/abs/2412.05579

    work page internal anchor Pith review arXiv 2024

  30. [30]

    Bradley Efron and Robert J Tibshirani.An introduction to the bootstrap, volume

    Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA: Measuring how models mimic human falsehoods. InProceedings of the 60th Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252. Associa- tion for Computational Linguistics, May 2022. doi: 10.18653/v1/2022.acl-long.229. URL https://aclanthology.org/...

    work page doi:10.18653/v1/2022.acl-long.229 2022

  31. [31]

    TRUTH DECAY: Quantifying multi-turn sycophancy in language models

    Joshua Liu, Aarav Jain, Soham Takuri, Srihan Vege, Aslihan Akalin, Kevin Zhu, Sean O’Brien, and Vasu Sharma. TRUTH DECAY: Quantifying multi-turn sycophancy in language models. arXiv preprint arXiv:2503.11656, 2025. URLhttps://arxiv.org/abs/2503.11656

    work page arXiv 2025

  32. [32]

    In: Vlachos, A., Augen- stein, I

    Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. G- Eval: Nlg evaluation using GPT-4 with better human alignment. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Sin- gapore, December 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023. emnlp-main.15...

    work page doi:10.18653/v1/2023 2023

  33. [33]

    Andrew Y . Ng. Post on X (formerly twitter). https://x.com/AndrewYNg/status/ 1801295202788983136, 2024. Accessed 2026-04-24

    2024

  34. [34]

    Nickerson

    Raymond S. Nickerson. Confirmation bias: A ubiquitous phenomenon in many guises.Review of General Psychology, 2(2):175–220, June 1998. doi: 10.1037/1089-2680.2.2.175. URL https://doi.org/10.1037/1089-2680.2.2.175

    work page doi:10.1037/1089-2680.2.2.175 1998

  35. [35]

    User Detection and Response Patterns of Sycophantic Behavior in Conversational AI

    Kazi Noshin, Syed Ishtiaque Ahmed, and Sharifa Sultana. User detection and response patterns of sycophantic behavior in conversational ai.arXiv preprint arXiv:2601.10467, January 2026. URLhttps://arxiv.org/abs/2601.10467

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  36. [36]

    Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schul- man, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Pe- ter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language mod- els to follow instructions with human fee...

    2022

  37. [37]

    In: Findings of the Association for Computational Linguistics: ACL 2023, pp

    Ethan Perez, Sam Ringer, Kamil˙e Lukoši¯ut˙e, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Ben Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kern...

    work page doi:10.18653/v1/2023.findings-acl.847 2023

  38. [38]

    Moral sycophancy in vision language models.arXiv preprint arXiv:2602.08311,

    Shadman Rabby, Md. Hefzul Hossain Papon, Sabbir Ahmed, Nokimul Hasan Arif, A. B. M. Ashikur Rahman, and Irfan Ahmad. Moral sycophancy in vision language models.arXiv preprint arXiv:2602.08311, February 2026. URLhttps://arxiv.org/abs/2602.08311

    work page arXiv 2026

  39. [39]

    A. B. M. Ashikur Rahman, Saeed Anwar, Muhammad Usman, Irfan Ahmad, and Ajmal Mian. PENDULUM: A benchmark for assessing sycophancy in multimodal large language models. arXiv preprint arXiv:2512.19350, 2025. URLhttps://arxiv.org/abs/2512.19350

    work page arXiv 2025

  40. [40]

    White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes

    Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 33–44....

    work page doi:10.1145/3351095.3372873 2020

  41. [41]

    Tulika Saha, Vaibhav Gakhreja, Anindya Sundar Das, Souhitya Chakraborty, and Sriparna Saha

    Leonardo Ranaldi and Giulia Pucci. When large language models contradict humans? large language models’ sycophantic behaviour.arXiv preprint arXiv:2311.09410, 2023. URL https://arxiv.org/abs/2311.09410

    work page arXiv 2023

  42. [42]

    Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages =

    Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. Towards empathetic open-domain conversation models: A new benchmark and dataset. InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5370–5381, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1534. UR...

    work page doi:10.18653/v1/p19-1534 2019

  43. [43]

    The social sycophancy scale: A psychometrically validated measure of sycophancy.arXiv preprint arXiv:2603.15448,

    Jean Rehani, Victoria Oldemburgo de Mello, Dariya Ovsyannikova, Ashton Anderson, and Michael Inzlicht. The social sycophancy scale: A psychometrically validated measure of sycophancy.arXiv preprint arXiv:2603.15448, March 2026. URL https://arxiv.org/abs/ 2603.15448

    work page arXiv 2026

  44. [44]

    Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R

    Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna M. Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez. Towards understanding sycophancy in language models. In The Twelfth Interna...

    2024

  45. [45]

    From Hallucination to Scheming: A Unified Taxonomy and Benchmark Analysis for LLM Deception

    Jerick Shi, Terry Jingcheng Zhang, Zhijing Jin, and Vincent Conitzer. From hallucination to scheming: A unified taxonomy and benchmark analysis for llm deception.arXiv preprint arXiv:2604.04788, April 2026. URL https://arxiv.org/abs/2604.04788. Accepted to the ICLR 2026 Workshop on Agents in the Wild: Safety, Security, and Beyond. 13

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  46. [46]

    From ELIZA to XiaoIce: Challenges and oppor- tunities with social chatbots.Frontiers of Information Technology & Electronic Engineering, 19:10–26, January 2018

    Heung-Yeung Shum, Xiaodong He, and Di Li. From ELIZA to XiaoIce: Challenges and oppor- tunities with social chatbots.Frontiers of Information Technology & Electronic Engineering, 19:10–26, January 2018. doi: 10.1631/FITEE.1700826. URL https://doi.org/10.1631/ FITEE.1700826

    work page doi:10.1631/fitee.1700826 2018

  47. [48]

    URLhttps://arxiv.org/abs/2410.12294

    work page arXiv

  48. [49]

    Large language models and empathy: Systematic review.Journal of Medical Internet Research, 26:e52597, December 2024

    Vera Sorin, Dana Brin, Yiftach Barash, Eli Konen, Alexander Charney, Girish Nadkarni, and Eyal Klang. Large language models and empathy: Systematic review.Journal of Medical Internet Research, 26:e52597, December 2024. doi: 10.2196/52597. URL https://doi.org/ 10.2196/52597

    work page doi:10.2196/52597 2024

  49. [50]

    Tang, Alejandro Cuadron, Chen- guang Wang, Raluca Ada Popa, and Ion Stoica

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Y . Tang, Alejandro Cuadron, Chen- guang Wang, Raluca Ada Popa, and Ion Stoica. JudgeBench: A benchmark for evaluating LLM-based judges. InThe Thirteenth International Conference on Learning Representations (ICLR), 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/file/ 9e720fce64f91114c49cfd64...

    2025

  50. [51]

    When truth is overridden: Uncovering the internal origins of sycophancy in large language models

    Keyu Wang, Jin Li, Shu Yang, Zhuoran Zhang, and Di Wang. When truth is overridden: Uncovering the internal origins of sycophancy in large language models. InProceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26). Association for the Advancement of Artificial Intelligence, 2026. URL https://ojs.aaai.org/index.php/AAAI/article/ vie...

    2026

  51. [52]

    Heartificial intelligence: Exploring empathy in language models.arXiv preprint arXiv:2508.08271, 2025

    Victoria Williams and Benjamin Rosman. Heartificial intelligence: Exploring empathy in language models.arXiv preprint arXiv:2508.08271, 2025. URL https://arxiv.org/abs/ 2508.08271

    work page arXiv 2025

  52. [53]

    Xing, Hao Zhang, Joseph E

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-judge with MT-bench and chatbot arena. InAdvances in Neural Information Processing Systems 37 (NeurIPS 2023), pages 46595–46623, December 2023. URL https://dl.a...

    work page doi:10.5555/3666122.3668142 2023

  53. [54]

    JudgeLM: Fine-tuned large language models are scalable judges

    Lianghui Zhu, Xinggang Wang, and Xinlong Wang. JudgeLM: Fine-tuned large language models are scalable judges. InThe Thirteenth International Conference on Learning Represen- tations (ICLR), 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/ file/7f8f73134e253845a8f82983219a8452-Paper-Conference.pdf. 14

    2025