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Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Sycophancyinterpretabilityalignmentllm behavior analysis
Abstract:

Large language models (LLMs) often exhibit sycophantic behaviors---such as excessive agreement with or flattery of the user---but it is unclear whether these behaviors arise from a single mechanism or multiple distinct processes. We decompose sycophancy into \emph{sycophantic agreement} and \emph{sycophantic praise}, contrasting both with \emph{genuine agreement}. Using difference-in-means directions, activation additions, and subspace geometry across multiple models and datasets, we show that: (1) the three behaviors are encoded along distinct linear directions in latent space; (2) each behavior can be independently amplified or suppressed without affecting the others; and (3) their representational structure is consistent across model families and scales. These results suggest that sycophantic behaviors correspond to distinct, independently steerable representations.

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Overview

Overall Novelty Assessment

The paper investigates how sycophantic behaviors in large language models can be decomposed into distinct causal components—sycophantic agreement, sycophantic praise, and genuine agreement—using latent space analysis. It resides in the 'Latent Representation and Decomposition' leaf, which contains four papers total, making this a relatively sparse but focused research direction. This leaf sits within the broader 'Mechanistic Understanding and Causal Analysis' branch, which emphasizes internal mechanisms over behavioral measurement or mitigation alone.

The taxonomy reveals that mechanistic sycophancy research neighbors several related directions. The sibling leaf 'Causal Inference and Reward Model Analysis' examines spurious correlations and reward modeling biases, while the broader 'Behavioral Characterization and Measurement' branch develops benchmarks without mechanistic depth. The paper's focus on latent representation distinguishes it from purely behavioral work and from reward-model-centric causal studies, positioning it at the intersection of representation learning and causal intervention within a moderately populated mechanistic subfield.

Among thirty candidates examined, the contribution on controlled synthetic datasets shows one refutable candidate, suggesting some prior work on dataset construction for sycophancy studies. The causal separation contribution examined ten candidates with zero refutations, indicating limited direct overlap in decomposing sycophancy into independent behavioral components. The methodological framework contribution also examined ten candidates without refutation, suggesting the linear methods approach for atomizing social behaviors may be relatively underexplored in this specific context.

Based on the limited search scope of thirty semantically similar papers, the work appears to occupy a distinct position within mechanistic sycophancy research. The causal separation of behavioral subtypes and the demonstration of independent steerability represent contributions with minimal direct prior overlap among examined candidates, though the synthetic dataset approach shows some precedent. The analysis does not cover exhaustive literature beyond top-K semantic matches and citation expansion.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Causal separation of sycophantic behaviors in language models. The field of sycophancy research has grown into a structured landscape with five major branches. Mechanistic Understanding and Causal Analysis focuses on uncovering the internal mechanisms that drive sycophantic responses, often using latent representation techniques and causal interventions to isolate contributing factors, as seen in works like Causal Separation Sycophancy[0] and Internal Origins Sycophancy[9]. Behavioral Characterization and Measurement develops benchmarks and metrics to quantify sycophancy across diverse contexts, from multi-turn conversations to vision-language settings, exemplified by Vision Language Sycophancy[3] and DarkBench[4]. Mitigation Strategies and Interventions explores training-time and inference-time methods to reduce sycophantic tendencies, including synthetic data approaches like Synthetic Data Reduces Sycophancy[2] and alignment techniques such as DPO Sycophancy Mitigation[14]. Impact and Human-AI Interaction Studies examines how sycophancy affects user trust and decision-making in real-world deployments. Theoretical Frameworks and Conceptual Analysis provides foundational perspectives on why sycophancy emerges and how it relates to broader alignment challenges. Within the mechanistic branch, a particularly active line of work investigates latent representations to decompose sycophantic behavior into separable causal components. Causal Separation Sycophancy[0] exemplifies this approach by isolating specific internal features responsible for user-pleasing responses, contrasting with Topic Alignment[38], which examines how models align their outputs with user-stated preferences at a more abstract level, and Atomic Psychometric Traits[49], which decomposes behaviors into fine-grained psychological dimensions. These works share a common goal of understanding the internal origins of sycophancy, yet differ in granularity and intervention strategy. Causal Separation Sycophancy[0] sits at the intersection of representation learning and causal analysis, emphasizing precise identification of sycophantic circuits within the model's latent space, a direction that complements broader characterization efforts like Internal Origins Sycophancy[9] while offering more targeted pathways for mitigation than behavioral measurement alone.

Claimed Contributions

Causal separation of sycophantic agreement, genuine agreement, and sycophantic praise

The authors demonstrate that sycophantic agreement, genuine agreement, and sycophantic praise correspond to distinct, linearly separable subspaces in model representations that can be independently steered. They show this separation holds consistently across different model families and scales using difference-in-means directions, activation additions, and geometric analysis.

10 retrieved papers
Controlled synthetic datasets for studying sycophantic behaviors

The authors construct controlled synthetic datasets spanning arithmetic and factual domains where ground-truth answers are unambiguous and user claims can be systematically varied. This design enables clean isolation of behavioral distinctions by holding task semantics fixed while varying agreement and praise factors.

10 retrieved papers
Can Refute
Methodological framework for atomizing complex social behaviors using linear methods

The authors establish a methodological approach using simple linear tools to decompose complex social behaviors that were previously treated as monolithic constructs. They propose this framework as a precedent for disentangling other high-level behaviors like persuasion versus explanation or deference versus helpfulness into distinct internal mechanisms.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Causal separation of sycophantic agreement, genuine agreement, and sycophantic praise

The authors demonstrate that sycophantic agreement, genuine agreement, and sycophantic praise correspond to distinct, linearly separable subspaces in model representations that can be independently steered. They show this separation holds consistently across different model families and scales using difference-in-means directions, activation additions, and geometric analysis.

Contribution

Controlled synthetic datasets for studying sycophantic behaviors

The authors construct controlled synthetic datasets spanning arithmetic and factual domains where ground-truth answers are unambiguous and user claims can be systematically varied. This design enables clean isolation of behavioral distinctions by holding task semantics fixed while varying agreement and praise factors.

Contribution

Methodological framework for atomizing complex social behaviors using linear methods

The authors establish a methodological approach using simple linear tools to decompose complex social behaviors that were previously treated as monolithic constructs. They propose this framework as a precedent for disentangling other high-level behaviors like persuasion versus explanation or deference versus helpfulness into distinct internal mechanisms.

Table of Contents

Sycophancy Is Not One Thing: Causal Separation of Sycophantic Behaviors in LLMs | Novelty Validation