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Sycophancy Hides Linearly in the Attention Heads

Rifo Genadi, Munachiso Nwadike, Nurdaulet Mukhituly, Hilal Alquabeh, Tatsuya Hiraoka, Kentaro Inui

arXiv:2601.16644v1cs.CLcs.AI

TL;DR

The paper asks where correct-to-incorrect sycophancy is represented and how it can be mitigated without sacrificing factual accuracy. It trains linear probes across residual, MLP, and attention activations, finding that sparse middle-layer attention heads provide the strongest and most stable steering gains. The results identify attention-level activations as a practical locus for targeted sycophancy interventions.

  • Problem

    Correct-to-incorrect sycophancy undermines factual reliability, but the paper investigates where this behavior is represented and how it can be controlled.

    Source: §1 · p. 1

  • Method

    The paper trains linear probes across residual-stream, MLP, and multi-head attention activations, then steers inference using probe-derived directions.

    Source: §3.1 · p. 3, §3.2 · p. 3

  • Results

    Sparse middle-layer multi-head attention heads provide the strongest and most stable steering gains for reducing sycophantic reversals.

    Source: §1 · p. 2, §6 · p. 8

  • Takeaways & Limitations

    Attention-level activations are a practical and interpretable locus for mitigating sycophancy with simple linear interventions.

    Source: §6 · p. 8

    The evaluations are restricted to Gemma-3 and Llama-3.2, while broader model-size coverage remains future work.

    Source: Limitations · p. 9

Abstract

from arXiv · show

We find that correct-to-incorrect sycophancy signals are most linearly separable within multi-head attention activations. Motivated by the linear representation hypothesis, we train linear probes across the residual stream, multilayer perceptron (MLP), and attention layers to analyze where these signals emerge. Although separability appears in the residual stream and MLPs, steering using these probes is most effective in a sparse subset of middle-layer attention heads. Using TruthfulQA as the base dataset, we find that probes trained on it transfer effectively to other factual QA benchmarks. Furthermore, comparing our discovered direction to previously identified "truthful" directions reveals limited overlap, suggesting that factual accuracy, and deference resistance, arise from related but distinct mechanisms. Attention-pattern analysis further indicates that the influential heads attend disproportionately to expressions of user doubt, contributing to sycophantic shifts. Overall, these findings suggest that sycophancy can be mitigated through simple, targeted linear interventions that exploit the internal geometry of attention activations.

1 Introduction

The paper studies correct→incorrect sycophancy, where models retract correct factual answers after user disagreement, and tests whether linear probes can locate and control this behavior. It identifies a sparse subset of middle-layer attention heads as the strongest intervention site.

Motivation

  • Linear representation research motivates using activation-space directions to analyze and steer alignment-related behaviors.Prior work applied linear steering to behaviors including truthfulness, toxicity, and other alignment properties.Source: §1 · p. 1
  • Correct→incorrect sycophancy occurs when a model changes an initially correct answer to an incorrect one after user disagreement, undermining factual reliability.The paper focuses on factual question answering, where consistency with established truth matters.Source: §1 · p. 1

Approach

  • The paper trains simple linear probes to detect and control correct→incorrect sycophancy across model components.The intervention location is compared across multi-head attention, residual-stream, and MLP activations.Source: §1 · p. 2

Evaluation

  • The study evaluates model behavior types on TruthfulQA, where correct→incorrect transitions are identified as the key failure mode.The table reports behavior rates for Gemma-3-4B.Source: Table 1 · p. 2

Findings

  • A sparse subset of middle-layer multi-head attention heads provides the strongest leverage for steering sycophantic behavior.The comparison spans residual-stream, MLP, and attention representations.Source: §1 · p. 2
  • These influential heads attend more to user doubt tokens immediately before the model’s response, whereas non-sycophantic heads distribute attention more evenly.This attention pattern links disagreement cues with the model’s sycophantic reply.Source: Figure 1 · p. 1, §1 · p. 2

2 Related Work

Related work frames sycophancy as factual degradation after disagreement and situates probe-derived activation steering among methods for manipulating model behavior.

Sycophancy in LLMs

  • Correct→incorrect sycophancy generalizes the “Are You Sure?” behavior to any reversal from a correct to an incorrect answer after user disagreement.The definition is independent of the disagreement’s phrasing and excludes desirable incorrect→correct corrections.Source: §2 · p. 2
  • The paper distinguishes its focus from apology-based or agreeableness-driven sycophancy because apologies need not compromise factual accuracy.The target phenomenon is a degradation of factual reliability.Source: §2 · p. 2

Activation Steering

  • Activation steering learns linear directions that can be manipulated to produce desired model behaviors.The paper contrasts difference-in-means directions with probe-derived directions learned from labeled examples.Source: §2 · pp. 2–3
  • Probe-derived directions are selected because validation accuracy helps identify an effective intervention location within the model.The cited passage introduces probe accuracy as a placement signal.Source: §2 · p. 3

3 Methodology

The methodology uses linear probes to locate sycophancy signals in residual, MLP, and attention activations, then applies the learned directions during inference to test behavioral effects.

Methodology

  • The study first identifies where sycophancy is represented through layer-level and head-level probing, then tests causal effects with inference-time steering.The pipeline separates representation analysis from behavioral intervention.Source: §3 · p. 3

3.1 Probing Where Sycophancy Lives

  • For residual, MLP, and MHA components at each layer, the method collects hidden activations and trains probes to predict sycophantic behavior.The activations occupy component-specific subspaces.Source: §3.1 · p. 3
  • Each target uses logistic regression over sycophancy-versus-non-sycophancy labels, with validation accuracy measuring linear separability.The learned weight vector defines the activation-space direction used for later intervention.Source: §3.1 · p. 3

3.2 Steering: MHA vs. MLP and Residuals

  • Steering adds a scaled, normalized probe direction to the activation during inference, with α controlling intervention strength.The intervention is applied to residual, MLP, and MHA output activations.Source: §3.2 · p. 3
  • The evaluation compares intervention effectiveness across residual, MLP, and MHA activations by measuring changes in sycophantic behavior.The method predicts that positive α increases sycophancy, while the opposite direction reduces it.Source: §3.2 · p. 3

4 Experimental Setting

The experiments compare steering in residual-stream, MLP, and multi-head attention activations using Gemma3-4B and Llama3.2-3B. Evaluation mainly uses TruthfulQA, with sycophancy rate and answer accuracy assessed through LLM-as-a-Judge.

  • Experiments compare steering in residual-stream, MLP, and MHA activations under the same probing and evaluation setup.The models are Gemma3-4B and Llama3.2-3B.Source: §4 · p. 3
  • TruthfulQA provides 817 questions across 38 categories, with greedy decoding used to reduce randomness.Source: §4 · p. 3
  • Sycophancy rate measures first-correct to second-incorrect reversals among cases where the first answer was correct.Lower values indicate reduced sycophancy.Source: §4 · pp. 3–4
  • Accuracy measures the proportion of correct first and second answers using the standard definition.Source: §4 · p. 4
  • Response correctness is evaluated with LLM-as-a-Judge using the prompt provided in Appendix D.Source: §4 · p. 4

5 Results

Linear probing localizes sycophancy-related information most sharply to a sparse subset of middle-layer attention heads, whose steering produces more consistent behavioral control than residual or MLP interventions.

Probing the Network

  • Probe accuracy peaks in the middle network layers, reaching 99.6% for Gemma-3 residual activations at layer 15 and 97.3% for MLP activations around layer 10.A similar mid-layer peak appears in Llama-3.2.Source: §5.1 · p. 4
  • Only a small, functionally selective subset of attention heads shows high sycophancy-probe accuracy, with stronger head-level selectivity in Llama-3.2 than Gemma-3.The signal is concentrated in middle-layer heads rather than distributed uniformly across the network.Source: §5.1 · pp. 4–5, Figure 3 · p. 4

Steering Effectiveness

  • Residual and MLP interventions can reduce sycophantic flips but often degrade output quality and do not scale monotonically with intervention strength.These components encode sycophancy-related signals but provide less targeted and stable control than attention heads.Source: Residual and MLP Intuitiveness · p. 5
  • Attention-head steering produces more consistent behavior changes, aligning with the sparse, functionally selective localization found by probing.The comparison directly separates representational encoding from intervention effectiveness.Source: MHA Intuitiveness · pp. 5–6

MHA Steering

  • Under MHA steering, first-answer accuracy remains largely preserved, second-answer accuracy generally improves with stronger negative steering, and correct-to-incorrect shifts decrease consistently.KL divergence increases with intervention magnitude, indicating greater deviation from unmodified behavior.Source: MHA Intuitiveness · p. 6

Cross-Model Results

  • Across Gemma-3 and Llama 3.2, MHA steering reduces shift-to-incorrect rates from 40.7% and 51.7% to 34.4% and 25.0%, respectively, while MLP steering often underperforms.The results indicate a gap between probe classification accuracy and causal behavioral influence.Source: §5.2.2 · p. 6

Attention Mechanisms

  • Sycophancy-related heads attend disproportionately to disagreement and sycophantic expressions, and steering them may reduce reliance on user pushback.This attention pattern offers a mechanism for why targeted head interventions reduce downstream answer reversals.Source: §5.3 · p. 8

Relation to Truthful Direction

  • Truthful-direction steering improves answer accuracy but does little to mitigate user-induced flips, whereas sycophancy-direction steering decreases incorrect shifts with minimal accuracy impact.The discovered directions are partially overlapping yet largely separable, with about one-third of top-32 heads overlapping and a mean correlation of −0.22 ± 0.12.Source: §5.4 · p. 8

6 Conclusion

The study finds that correct→incorrect sycophancy is most predictive in sparse mid-layer multi-head attention heads, while MLP and residual representations carry the signal more diffusely. Steering along probe-derived directions reduces reversals, with implicated-head interventions producing the largest and most stable gains.

  • Sycophancy signals concentrate in a sparse subset of mid-layer multi-head attention heads.Source: §6 · p. 8
  • MLP and residual representations carry sycophancy-related information more diffusely than the implicated attention heads.Source: §6 · p. 8
  • Steering along probe-derived directions reduces correct→incorrect reversals.Source: §6 · p. 8
  • Interventions on the implicated attention heads yield the largest and most stable gains.Source: §6 · p. 8

Limitations

The evaluation is limited in model coverage, behavioral scope, and interpretation of attention patterns. The reported head-level patterns therefore do not establish broad generality, exhaustive decision-process explanations, or effects on other alignment dimensions.

  • Evaluations are restricted to Gemma-3 and Llama-3.2, leaving additional model-size limitations for future work.Source: Limitations · p. 9
  • The evaluation focuses primarily on correctness-preserving behavior and direct measures of sycophancy reduction.Source: Limitations · p. 9
  • Broader impacts on generation style and alignment dimensions beyond sycophancy remain outside the current scope.Source: Limitations · p. 9
  • Attention patterns are used as diagnostic correlations indicating emphasized inputs, not exhaustive accounts of the model’s decision process.Source: Limitations · p. 9
  • Sycophancy signals concentrate in sparse mid-layer attention heads, as shown by the head-level heatmaps.Source: Figure 9 · p. 12

B Linear Probe Accuracy using MLP and Residual Activations

Probe accuracy for MLP and residual activations is evaluated across model layers, with sycophancy-related information peaking in the middle layers. The supplied appendix materials also identify sycophancy-related and non-sycophantic heads and note additional evaluation models and prompts.

F Prediction Comparison Between Base vs Intervened Models

The supplied examples compare baseline and intervened second answers across factual questions. They illustrate how responses change after intervention, including a corrected temporal comparison and revised claims about iron maidens, ninjas, and statue coloration.

G Probing with Non-linear Probe

Non-linear probes identify sycophancy-related information across more attention heads, but linear probes produce stronger steering improvements on TruthfulQA. This suggests non-linear probe signals may be more diffuse or correlational.

Probe design

  • A two-layer MLP probe uses its final-layer weights as the steering direction for distinguishing sycophantic from non-sycophantic outputs.The probe layers match the corresponding LLM hidden dimensions.Source: G Probing with Non-linear Probe · p. 16

Probe findings

  • Non-linear probes identify a sparse subset of Gemma-3 MHA heads, but their sparsity is weaker than with linear probes.The non-linear probe detects strong sycophancy signals in a higher number of attention heads than the linear probe.Source: Figure 12 · p. 16, G Probing with Non-linear Probe · p. 16

Steering comparison

  • Linear-probe steering yields stronger improvement than non-linear-probe steering on a separately split TruthfulQA evaluation.The comparison uses the best k heads and intervention strength α from the main experiment.Source: G Probing with Non-linear Probe · p. 16, Table 7 · p. 16

Interpretation

  • The authors hypothesize that some top attention heads selected by non-linear probes are more diffuse and encode correlational rather than targeted signals.High probe accuracy alone is treated as insufficient evidence of effective behavioral control.Source: G Probing with Non-linear Probe · p. 16

H Implementation Details

The experiments ran on NVIDIA RTX 4090 GPUs, with implementation and training details summarized in Table 8. The authors report using language-model assistance only for writing and editing, not for research outputs or experimental data.

Hardware and setup

Research process

  • The authors used LLM assistance only for writing and editing purposes.They state that research ideas, experimental design, analyses, and code implementations were developed independently.Source: I AI Disclosure · p. 17
  • No generative system was used to produce or modify experimental data, model outputs, or results.Source: I AI Disclosure · p. 17

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