mlsanityData to 7 Oct 2026

Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy

arXiv 2603.16643 ↗First posted 17 Mar 2026cs.CLcited by 7

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Alignment techniques often inadvertently induce sycophancy in LLMs. While prior studies studied this behaviour in direct-answer settings, the role of Chain-of-Thought (CoT) reasoning remains under-explored: does it serve as a logical constraint that mitigates sycophancy, or a tool for post-hoc rationalization that masks it? We evaluate a range of models across objective and subjective tasks to investigate the issue. Results show that reasoning generally reduces sycophancy in final decisions but also masks sycophancy in some samples, where models construct deceptive justifications through logical inconsistencies, calculation errors, and one-sided arguments etc. Furthermore, LLMs are more prone to sycophancy in subjective tasks and under authority-bias. Our mechanistic analysis on three open-source models reveals that the tendency of sycophancy is dynamic during the reasoning process rather than being pre-determined at the input stage.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 29 of its 70 references that are in the map. First posted on arXiv 17 Mar 2026.

Builds on

29 of 70 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation28 Jul
  2. 2025How does Chain of Thought Think? Mechanistic Interpretability of Chain-of-Thought Reasoning with Sparse Autoencoding24 Jul
  3. 2025Reasoning Models Don't Always Say What They Think8 May
  4. 2024Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought8 Mar
  5. 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul

Other references · 24

  1. 2024Qwen2.5 Technical Reportcited by 4,713
  2. 2024GPT-4o System Cardcited by 4,467
  3. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  4. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  5. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  6. 2025Persona Vectors: Monitoring and Controlling Character Traits in Language Modelscited by 303
  7. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
  8. 2023Simple synthetic data reduces sycophancy in large language modelscited by 174
  9. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 115
  10. 2025Persona Features Control Emergent Misalignmentcited by 76
  11. 2023Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experimentcited by 71
  12. 2025When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Modelscited by 46
12 more
  1. 2025I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoderscited by 43
  2. 2024Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questionscited by 35
  3. 2025Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMscited by 32
  4. 2023Aligning Large Language Models with Human: A Surveycited by 31
  5. 2025TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Modelscited by 31
  6. 2024SWAN: SGD with Normalization and Whitening Enables Stateless LLM Trainingcited by 20
  7. 2024Sycophancy in Vision-Language Models: A Systematic Analysis and an Inference-Time Mitigation Frameworkcited by 17
  8. 2025Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QAcited by 16
  9. 2025How Do LLMs Use Their Depth?cited by 15
  10. 2025Do LLMs "Feel"? Emotion Circuits Discovery and Controlcited by 13
  11. 2025BASIL: Bayesian Assessment of Sycophancy in LLMscited by 11
  12. 2025But what is your honest answer? Aiding LLM-judges with honest alternatives using steering vectorscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026MemSyco-Bench: Benchmarking Sycophancy in Agent Memorycited by 9
  2. 2026LLM-as-an-Investigator: Evidence-First Reasoning for Robust Interactive Problem Diagnosiscited by 0
  3. 2026Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agentscited by 0
  4. 2026From Plausible to Actionable: A Position on LLM Self-Explanationscited by 0
  5. 2026Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientistscited by 0
  6. 2026AfriSyCo: Measuring Assertive Framing, Verification, and Wording Sensitivity Around African-Language Contentcited by 0
  7. 2026"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accusedcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Beyond the Sycophancy Score: How Task, Model, and Pressure Shape LLM Yielding
  2. 2026When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
  3. 2025Internal Reasoning vs. External Control: A Thermodynamic Analysis of Sycophancy in Large Language Models
  4. 2026Looking Again: Measuring Sycophancy in the Reasoning Chains of Multimodal Models Under Pressure
  5. 2026LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
  6. 2026Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
  7. 2025Causal Strengths and Leaky Beliefs: Interpreting LLM Reasoning via Noisy-OR Causal Bayes Nets
  8. 2025Reasoning Isn't Enough: Examining Truth-Bias and Sycophancy in LLMs