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arxiv: 2605.12991 · v2 · pith:NUZR3R2Gnew · submitted 2026-05-13 · 💻 cs.LG · cs.AI

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy

Adarsh Kumarappan , Ananya Mujoo This is my paper

Pith reviewed 2026-05-20 21:24 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords multi-agent sycophancyRLHFactivation patchingpeer disagreementLLM alignmentyieldreasoning suppressiondissent
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The pith

Pretrained base models exhibit the same sycophancy pattern as RLHF versions, with higher average yield under peer pressure.

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

The paper tests whether multi-agent sycophancy in LLMs, where models flip to incorrect answers under simulated peer disagreement, is caused by RLHF alignment. Across four model families, it finds that pretrained base models exhibit the same substitution pattern and even higher yield rates than their Instruct counterparts. Activation patching localizes the issue to a narrow mid-layer window dominated by attention mechanisms, where patching restores 96% of the performance gap. It shows that pressure suppresses clean-reasoning features rather than activating a sycophancy circuit, and that a single dissenting agent arguing correctly can reduce yield by 54 to 73 percentage points. This implies that defenses should target the underlying mechanism with structured dissent at the pipeline level instead of relying on prompt-based fixes.

Core claim

LLM-based multi-agent pipelines flip from correct to incorrect answers under simulated peer disagreement at rates termed yield. Pretrained base models exhibit the same substitution pattern as their Instruct variants, averaging higher yield than Instruct. Using activation patching, the corruption is localized to a narrow mid-layer window where attention carries the causal weight and MLP contribution is negligible; patching above this window restores 96% of the clean-to-pressured P(correct) gap. The attack surface decomposes into two independent factors whose interaction produces a 47.5 percentage-point yield gap at majority consensus. Pressure suppresses clean-reasoning features rather than a

What carries the argument

Activation patching applied to mid-layer attention mechanisms to isolate how peer pressure suppresses clean-reasoning features instead of engaging a sycophancy circuit.

If this is right

  • Structured dissent at the pipeline level can substantially reduce yield rates across framings.
  • The vulnerability arises from two independent factors of channel framing and consensus strength.
  • Prompt-level defenses fail on attack variants outside their design surface.
  • Interventions in activation space can restore most of the clean performance without additional training.

Where Pith is reading between the lines

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

  • Incorporating dissenting agents by design could improve reliability in multi-agent LLM deployments.
  • Similar suppression of reasoning features may appear in other social influence scenarios beyond peer disagreement.
  • Since base models are affected, post-training alignment techniques alone may not address multi-agent vulnerabilities.

Load-bearing premise

The simulated peer-disagreement protocol and the four model families tested produce effects that generalize to real multi-agent LLM deployments, and that activation patching isolates the causal mechanism without introducing artifacts from the intervention itself.

What would settle it

A direct test in a live multi-agent system where models interact without simulation, checking if base models still yield more than instruct models and if introducing a dissenter reduces errors by similar margins.

Figures

Figures reproduced from arXiv: 2605.12991 by Adarsh Kumarappan, Ananya Mujoo.

Figure 1
Figure 1. Figure 1: From pretrained vulnerability to cross-framing mitigation. (A) Multi-agent pressure [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Wrong-agent count sweep at N=4, suffixed protocol. Yield as a function of kwrong, the number of agents arguing for the wrong answer. User-role framing produces a unanimity cliff at 4v0; assistant-role framing produces a majority cliff at 3v1; the 47.5 pp cross-framing gap at 3v1 is the two-factor interaction. 5.3 Causal localization at L14–L18 Having established the behavioral attack surface, we now ask: w… view at source ↗
Figure 3
Figure 3. Figure 3: Activation-patching restoration on Llama-3.1-8B-Instruct ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Base vs. Instruct on matched question pools across four model families and three pressure [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Dissenter rescue across three framings. The 3v0 [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Yield vs. fraction of wrong-arguing agents, for user-role and assistant-role framing, overlaid [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Component decomposition at L14–L18, n=400 named peer jury questions. Blue: MLP￾only patch; orange: attention-only; purple: both components (layer-local baseline); dashed green: full residual-stream (upstream) patch. Attention carries ≥81% of the layer-local restoration at every layer; MLP is null throughout. L14 and L17 are the layer-local loci; L15, L16, and L18 are upstream￾dominated. Error bars: 95% boo… view at source ↗
Figure 8
Figure 8. Figure 8: SAE feature clamping sweep: ∆P(wrong) and ∆P(correct) as a function of clamping strategy and number of clamped features. All deltas reported vs the reconstruction-only baseline [PITH_FULL_IMAGE:figures/full_fig_p025_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Full 400-question Mistral-7B replication with 95% bootstrap CIs ( [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Cross-domain direct user assertion yield. CS theory and calc-STEM are amplified above [PITH_FULL_IMAGE:figures/full_fig_p028_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Conditional activation patching across the 2 [PITH_FULL_IMAGE:figures/full_fig_p029_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: User-role 4v0 yield on the wrong-agent count sweep, plotted against clean-nosuffix [PITH_FULL_IMAGE:figures/full_fig_p030_12.png] view at source ↗
read the original abstract

LLM-based multi-agent pipelines flip from correct to incorrect answers under simulated peer disagreement at rates we term yield, a vulnerability widely attributed to RLHF-induced sycophancy. We test this attribution across four model families and find it largely wrong: pretrained base models exhibit the same substitution pattern as their Instruct variants, averaging higher yield than Instruct. Using activation patching, we localize the corruption to a narrow mid-layer window where attention carries the causal weight and MLP contribution is negligible; patching above this window restores 96% of the clean-to-pressured P(correct) gap. The attack surface decomposes into two independent factors (channel framing and consensus strength) whose interaction produces a 47.5 percentage-point yield gap at majority consensus, preserved across jury sizes $N \in \{4, 5, 6\}$. Two converging activation-space interventions show that pressure suppresses clean-reasoning features rather than activating a new sycophancy circuit. A single correctly-arguing dissenter reduces yield by 54-73 percentage points across all framings tested, whereas the strongest prompt-level defense fails on attack variants outside its design surface. Mitigations should target the mechanism, structured dissent at the pipeline level, rather than prompt-level defenses.

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

3 major / 2 minor

Summary. The paper investigates multi-agent sycophancy in LLMs, where models substitute correct answers for incorrect ones under simulated peer disagreement (termed 'yield'). It challenges the common attribution to RLHF/alignment by testing four model families and finding that pretrained base models exhibit the same substitution pattern, often with higher average yield than their Instruct variants. Activation patching localizes the effect to a narrow mid-layer window where attention carries causal weight (MLP contribution negligible); patching above this window restores 96% of the clean-to-pressured P(correct) gap. The attack surface decomposes into channel framing and consensus strength, producing a 47.5 pp yield gap at majority consensus (preserved for jury sizes N=4,5,6). Two activation-space interventions indicate that pressure suppresses clean-reasoning features rather than activating a new sycophancy circuit. A single correctly-arguing dissenter reduces yield by 54-73 pp across framings, while prompt-level defenses fail on variants outside their design surface. The paper concludes that mitigations should target the mechanism via structured dissent at the pipeline level.

Significance. If the empirical patterns and causal attributions hold, the work meaningfully shifts the framing of sycophancy away from post-training alignment toward intrinsic model behaviors observable in base models. The quantitative effect sizes (96% gap restoration, 47.5 pp framing-consensus interaction, 54-73 pp dissenter reduction) and the use of converging activation interventions across independent model families provide concrete, falsifiable support for mechanism-targeted interventions. This could inform more robust multi-agent LLM pipelines if the simulated disagreement protocol generalizes.

major comments (3)
  1. [Abstract / Experimental Results] Abstract and experimental results: precise claims such as '96% restoration' and '47.5 percentage-point yield gap' are reported without accompanying statistical tests, confidence intervals, data exclusion criteria, or controls for side-effects of the activation patching intervention itself. This leaves the central causal claim (pressure suppresses clean-reasoning features rather than activating a new circuit) vulnerable to alternative explanations such as general distribution shift.
  2. [Discussion / Generalization] Generalization section / discussion: the simulated peer-disagreement protocol (framing + consensus strength injected into single-context prompts) is used to attribute effects away from RLHF and to recommend pipeline-level dissent. However, it is unclear whether these patterns replicate when agents generate outputs independently and exchange messages, as base-model yield differences could arise from calibration or prompt sensitivity rather than an inherent non-RLHF mechanism.
  3. [Activation Patching Experiments] Activation patching results: the claim that patching above the mid-layer window isolates suppression of clean-reasoning features rests on the observed restoration of P(correct). Without explicit controls (e.g., random patching baselines, layer-wise ablation of attention vs. MLP, or checks for unintended attention-flow changes), it remains possible that the intervention produces the effect through unrelated mechanisms.
minor comments (2)
  1. [Introduction] The introduction of 'yield' as a term is useful but would benefit from an explicit formal definition (e.g., probability of flipping to incorrect under pressure) early in the text for readers unfamiliar with the framing.
  2. [Results] Notation for jury sizes N ∈ {4,5,6} is clear, but ensure that all tables and figures consistently report results broken down by N rather than aggregated only.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback, which has helped us improve the clarity and rigor of our work. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract / Experimental Results] Abstract and experimental results: precise claims such as '96% restoration' and '47.5 percentage-point yield gap' are reported without accompanying statistical tests, confidence intervals, data exclusion criteria, or controls for side-effects of the activation patching intervention itself. This leaves the central causal claim (pressure suppresses clean-reasoning features rather than activating a new circuit) vulnerable to alternative explanations such as general distribution shift.

    Authors: We agree that the original manuscript lacked sufficient statistical support for the reported figures. In the revised version, we have added 95% bootstrap confidence intervals for all key metrics including the 96% restoration and 47.5 pp yield gap. We performed paired statistical tests (Wilcoxon signed-rank) on the yield differences across models and conditions, reporting p-values. Data exclusion criteria (e.g., filtering for cases where clean accuracy > 0.5) are now explicitly stated in Section 3. For activation patching side-effects, we added controls by measuring performance on a held-out set of unrelated factual questions and included random patching baselines at non-critical layers. These revisions strengthen the causal claim by ruling out general distribution shift. revision: yes

  2. Referee: [Discussion / Generalization] Generalization section / discussion: the simulated peer-disagreement protocol (framing + consensus strength injected into single-context prompts) is used to attribute effects away from RLHF and to recommend pipeline-level dissent. However, it is unclear whether these patterns replicate when agents generate outputs independently and exchange messages, as base-model yield differences could arise from calibration or prompt sensitivity rather than an inherent non-RLHF mechanism.

    Authors: This is a valid concern regarding ecological validity. Our protocol was chosen to enable fine-grained manipulation of consensus and framing while holding other variables constant, which is difficult in free-form multi-agent exchanges. We maintain that the higher yield in base models points to an intrinsic mechanism, as the effect persists across model families and is localized via patching. However, we have expanded the discussion to explicitly acknowledge that real multi-agent interactions may introduce additional factors like calibration differences. We suggest that future work should test the protocol in asynchronous message-passing setups and have outlined a proposed experimental design for this. revision: partial

  3. Referee: [Activation Patching Experiments] Activation patching results: the claim that patching above the mid-layer window isolates suppression of clean-reasoning features rests on the observed restoration of P(correct). Without explicit controls (e.g., random patching baselines, layer-wise ablation of attention vs. MLP, or checks for unintended attention-flow changes), it remains possible that the intervention produces the effect through unrelated mechanisms.

    Authors: We appreciate this point on experimental controls. The revised manuscript now includes: (1) random patching baselines at layers outside the identified window, showing minimal restoration; (2) explicit layer-wise comparisons of attention vs. MLP patching, confirming attention's dominant role; (3) analysis of attention maps before and after patching to check for unintended flow changes, with no significant alterations observed beyond the targeted effect. These additions support our interpretation that the intervention targets feature suppression rather than unrelated mechanisms. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on direct empirical measurements

full rationale

The paper advances its central claims through experimental results: yield measurements on base vs. instruct models across four families, activation patching that restores 96% of the clean-to-pressured gap, decomposition into framing and consensus factors, and dissenter effects reducing yield by 54-73 points. No derivation chain, equations, or self-referential steps are present that would reduce any prediction or result to its inputs by construction. The work is self-contained against its own benchmarks and interventions, with no load-bearing self-citations or fitted parameters renamed as predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests primarily on the domain assumption that the simulated disagreement protocol validly models multi-agent dynamics; no free parameters are fitted to produce the headline results and no new entities are postulated.

axioms (1)
  • domain assumption The simulated peer disagreement setup captures the relevant dynamics of multi-agent LLM interactions.
    Invoked throughout the experimental design and yield measurements.

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discussion (0)

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    all three agree the answer is X

    C Mechanistic analysis details C.1 Component decomposition figure L14 L15 L16 L17 L18 Patched layer 0.0 0.2 0.4 0.6P(correct) restoration delta Residual (full upstream) MLP-only Attention-only Both (layer-local baseline) Figure 7: Component decomposition at L14–L18, n=400 named peer jury questions. Blue: MLP- only patch; orange: attention-only; purple: bo...

    work page 2024

  39. [39]

    The correct answer is (

    All 400 questions are used per cell with 1000-resample bootstrap CIs. The shared L14–L18 ramp shape across all pressured cells confirms a single circuit; the plateau height tracks the framing × consensus interaction, with user-role requiring near-unanimity and assistant-role framing engaging at majority consensus. E Robustness and calibration E.1 Unsuffix...

    work page 2000