@inproceedings{kasprova-etal-2026-polite,
title = "Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems",
author = "Kasprova, Vira and
Parulekar, Amruta and
AlRabah, Abdulrahman and
Agaram, Krishna and
Garg, Ritwik and
Jha, Sagar and
Bozdag, Nimet Beyza and
Hakkani-Tur, Dilek",
editor = "Choi, Jinho D. and
Chen, Yun-Nung and
Funakoshi, Kotaro and
Emami, Ali",
booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2026",
address = "Atlanta, Georgia, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.sigdial-1.56/",
pages = "795--814",
abstract = "Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model{'}s opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents' sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer{'}s tendency toward sycophancy. These rankings are based on scores calculated using various static (pre-discussion) and dynamic (online) strategies. We find that providing sycophancy priors reduces the influence of sycophancy-prone peers, mitigates error-cascades, and improves final discussion accuracy by an absolute 10.5{\%}. Thus, this is a lightweight and efficient way to reduce model sycophancy during discussions and subsequently improve downstream accuracy."
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<abstract>Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model’s opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents’ sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer’s tendency toward sycophancy. These rankings are based on scores calculated using various static (pre-discussion) and dynamic (online) strategies. We find that providing sycophancy priors reduces the influence of sycophancy-prone peers, mitigates error-cascades, and improves final discussion accuracy by an absolute 10.5%. Thus, this is a lightweight and efficient way to reduce model sycophancy during discussions and subsequently improve downstream accuracy.</abstract>
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%0 Conference Proceedings
%T Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
%A Kasprova, Vira
%A Parulekar, Amruta
%A AlRabah, Abdulrahman
%A Agaram, Krishna
%A Garg, Ritwik
%A Jha, Sagar
%A Bozdag, Nimet Beyza
%A Hakkani-Tur, Dilek
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F kasprova-etal-2026-polite
%X Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model’s opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents’ sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer’s tendency toward sycophancy. These rankings are based on scores calculated using various static (pre-discussion) and dynamic (online) strategies. We find that providing sycophancy priors reduces the influence of sycophancy-prone peers, mitigates error-cascades, and improves final discussion accuracy by an absolute 10.5%. Thus, this is a lightweight and efficient way to reduce model sycophancy during discussions and subsequently improve downstream accuracy.
%U https://aclanthology.org/2026.sigdial-1.56/
%P 795-814
Markdown (Informal)
[Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems](https://aclanthology.org/2026.sigdial-1.56/) (Kasprova et al., SIGDIAL 2026)
ACL
- Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah, Krishna Agaram, Ritwik Garg, Sagar Jha, Nimet Beyza Bozdag, and Dilek Hakkani-Tur. 2026. Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 795–814, Atlanta, Georgia, USA. Association for Computational Linguistics.