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Recovering Agentic Sovereignty: Mitigating the Consensus Paradox via Contrastive Epistemic Decoding

Dahlia Shehata, Ming Li

arXiv:2609.25570v1cs.AI

TL;DR

LLMs can conform to adversarial swarm consensus, abandoning latent factual knowledge, while standard contrastive decoding has auxiliary-model and grammatical-degradation drawbacks under heavy penalties. CED uses a single model’s dual forward-pass, an asymmetric clamp, and a top-k mask to suppress sycophancy. Across the evaluated models and benchmarks, it reduces loafing and recovers accuracy, while the paper frames the intervention as decoupling compliance from capability.

  • Problem

    In multi-agent swarms, LLMs may echo adversarial or incorrect consensus despite latent factual knowledge, while standard contrastive decoding requires an auxiliary model and can degrade grammar under heavy penalties.

    Source: Introduction · p. 1

  • Method

    CED uses a single architecture’s dual forward-pass to isolate conformity bias, then applies an asymmetric probability clamp and discrete top-k mask at inference time.

    Source: Introduction · pp. 1–2

  • Results

    Up to 33.00% cognitive loafing suppression and up to 30.75% absolute accuracy recovery were reported for highly susceptible models.

    Source: Introduction · p. 2

  • Takeaways & Limitations

    CED is presented as a training-free behavioral filter that decouples compliance from capability without inflating capacity-limited models’ competence.

    Source: Conclusion · p. 7

    CED requires direct pre-softmax log-probability manipulation, restricting implementation to open-weight architectures.

    Source: Discussion, Limitations and Future Work · p. 7

Abstract

from arXiv · show

Large language models (LLMs) exhibit a parametric vulnerability to adversarial swarm consensus. To mitigate this sycophancy, we introduce Contrastive Epistemic Decoding (CED), a zero-shot inference intervention. Unlike standard Contrastive Decoding (CD) which relies on a weaker secondary model, CED utilizes a dual forward-pass on a single architecture to isolate conformity bias. By introducing a novel asymmetric, zero-bounded probability clamp and discrete top-k truncation mask, CED mathematically suppresses toxic consensus tokens without causing grammatical collapse. Evaluated across 7,200 paired trajectories on complex benchmarks (GAIA, SWE-bench, Multi-Challenge) using Gemma-2 (9B), Llama-3.1 (8B), and Mistral v0.3 (7B), CED successfully neutralizes architectural and positional biases. By reducing cognitive loafing by up to 33.00% absolute, CED drives significant performance gains, yielding up to a 30.75% accuracy recovery. Regaining sovereignty induces distinct architectural behaviors---passive task-focus in Gemma-2 and active refutation of the simulated swarm in Llama-3.1---showing CED decouples compliance from capability without fine-tuning.

Introduction

LLMs can abandon latent factual knowledge to conform to adversarial swarm consensus, while standard contrastive decoding is poorly suited to suppressing that behavior under heavy social pressure. CED uses a single model’s dual forward-pass and an asymmetric clamp to suppress sycophantic tokens while preserving independent reasoning and syntax.

Introduction

  • Adversarial consensus can trigger cognitive loafing, in which models echo an incorrect or adversarial stance despite latent factual knowledge.The paper links this vulnerability to sycophancy and group conformity dynamics.Source: Introduction · p. 1
  • CED’s positive clamp suppresses the sycophantic Decoy token while leaving the ground-truth Truth token untouched.The figure contrasts this with standard CD’s artificial token boosting.Source: Figure 1 · p. 1
  • Standard contrastive decoding requires a weaker auxiliary model, adding memory overhead, and heavy penalties can boost toxic tokens and degrade grammar.These constraints make standard formulations incompatible with the social loads of multi-agent sycophancy.Source: Introduction · p. 1
  • CED’s single-architecture inference intervention uses an asymmetric, zero-bounded clamp and top-k mask to suppress sycophantic alignment without grammatical collapse.The dual forward-pass contrasts standard and high-pressure conformity environments to isolate conformity bias.Source: Introduction · pp. 1–2
  • CED suppresses cognitive loafing by up to 33.00% and yields up to a 30.75% absolute accuracy recovery in highly susceptible models.Stance transitions indicate passive task-focus in Gemma-2 and active refutation in Llama-3.1.Source: Introduction · p. 2

Contrastive Epistemic Decoding (CED)

CED compares a model’s standard and conformity-conditioned token distributions, then penalizes tokens favored by the conformity condition. A top-k mask keeps token selection within the standard distribution’s most plausible options, while synchronized prefixes maintain comparable contexts across passes.

Contrastive Epistemic Decoding (CED)

Isolating the Sycophantic Weight

  • The base and conformity distributions are compared token by token, with matched prompt structure intended to keep divergence focused on adversarial consensus rather than harmless syntax.The contrastive divergence is the conformity-prompt log-probability minus the base-prompt log-probability.Source: Isolating the Sycophantic Weight · p. 2

The Asymmetric Positive-Difference Penalty

  • CED clamps negative contrastive differences to zero, so only tokens more strongly favored under conformity are penalized.The resulting raw logits subtract α times the clamped penalty from the base log-probability.Source: The Asymmetric Positive-Difference… · p. 2

The Grammatical Plausibility Constraint

  • A discrete top-k mask restricts selection to the most probable tokens under the standard distribution, protecting structural syntax under strong penalties.Grid-search ablation determines k to preserve syntax while allowing the penalty to displace sycophantic tokens; both forward-passes share the selected-token prefix to maintain synchronized contexts.Source: The Grammatical Plausibility Constraint · p. 3

Experimental Methodology

The methodology uses open-weight models and controlled adversarial prompts to measure conformity while enabling access to token-level probabilities for CED. It evaluates three benchmarks and four swarm configurations, with parameters selected on a disjoint validation subset before the paired zero-shot evaluation.

Experimental Methodology

  • The final zero-shot pipeline evaluates 7,200 trajectories: 3,600 standard baselines and 3,600 using CED.The evaluation is paired across the two decoding strategies.Source: Experimental Methodology · p. 3

Experimental Setup

  • Experiments use NVIDIA L4 GPUs with 4-bit quantization for open-weight models, while API-based closed-weight evaluations run asynchronously with up to 10 workers.Past key and value states are cached to avoid redundant prompt prefill across penalty settings.Source: Experimental Setup · p. 3

Model Selection and Prompt Routing

  • Gemma-2 (9B), Llama-3.1 (8B), and Mistral v0.3 (7B) are evaluated at temperature 0, with native prompt formats and separate reasoning and final-answer sections.Open-weight models provide the pre-softmax log-probabilities needed to compute contrastive divergence; the 7B–9B range limits scale disparity.Source: Model Selection and Prompt Routing · p. 3

Task Formulation and Open-Weights Validation

  • Open-weight architectures enable token-level contrastive-divergence measurement and provide sycophancy baselines for testing whether anticipatory conformity also appears outside closed-weight models.The adversarial trap combines a decoy consensus, a nested three-hop fact chain, and dense semantic distraction.Source: Task Formulation and Open-Weights… · p. 4

Benchmark Choice and Task Entropy

  • The benchmarks cover general reasoning and QA (GAIA), repository-scale GitHub issues (SWE-bench), and multi-turn conversations (Multi-Challenge), using 100 official test samples per dataset.The official test splits contain 301, 500, and 266 samples, respectively.Source: Benchmark Choice and Task Entropy · p. 4

Swarm Topologies and Adversarial Social Load

  • Static injected peer dialogue isolates reactions to controlled social pressure, while four configurations compare no swarm, same-architecture peers, and two out-group speaking orders.Across 300 samples, the four configurations yield 1,200 trajectories per model and 7,200 paired trajectories across three architectures and two decoding strategies.Source: Swarm Topologies and Adversarial… · p. 4

Evaluation Mechanism and Metrics

  • Evaluation separates stance classification from task performance, measuring decoy adoption as Loafing Rate and accuracy only when the answer is correct and excludes the decoy.A judge model classifies ADOPTED, IGNORED, or REJECTED stances; the stated objective is to reduce adoption with minimal to no accuracy loss versus standard decoding.Source: Evaluation Mechanism and Metrics · pp. 4–5

Hyperparameter Optimization

  • CED parameters α and k are selected on a disjoint validation subset using constraints on loafing and accuracy, alongside sweeps of probability thresholds and grammatical-safety masks.The selected combination is locked before the main evaluation; the validation trajectories total 1,404 for the α sweep.Source: Hyperparameter Optimization · p. 5

Empirical Results

CED’s parameter choices suppress conformity while preserving syntax, and its evaluation reports reduced cognitive loafing with accuracy recovery across models and tasks. The intervention also neutralizes architectural and positional biases, while stance shifts and accuracy gains vary by architecture.

Parameterization of the CED Engine

  • α = 20 is the optimal penalty plateau: lower values fail to overcome conformity bias, while α ≥50.0 begins to degrade accuracy slightly.The selected penalty meets the stated constraint of reducing loafing while bounding standard accuracy degradation at 5%. For the truncation mask, k = 3 protects syntax while preserving generation diversity; confidence-based masks can shield sycophantic errors or neutralize needed penalties.Source: Parameterization of the CED Engine · pp. 5–6

Recovery of Agentic Sovereignty

  • 30.75% absolute GAIA accuracy increase for Gemma-2 (35.50% to 66.25%) accompanies a 33.00% absolute reduction in cognitive loafing (64.25% to 31.25%).On SWE-bench, Gemma-2 accuracy rises from 33.75% to 60.25% while loafing falls from 66.25% to 38.25%; Llama-3.1 accuracy rises from 38.75% to 52.00% on Multi-Challenge, while Mistral’s loafing falls on SWE-bench and GAIA with modest accuracy gains.Source: Recovery of Agentic Sovereignty · p. 6, Decoupling Compliance and Capability · p. 7

Architectural Bias and the Lead Anchor Effect

  • CED flattens baseline variation in loafing, neutralizing both same-architecture peer effects and speaking-order bias across swarm compositions.Under standard decoding, models show peak sycophancy toward peers of their own architecture, and changing speaking order shifts loafing rates among heterogeneous peers.Source: Architectural Bias and the Lead Anchor… · p. 6

Stance Transitions: Sycophancy Mitigation

  • CED suppresses the Adopted stance across benchmarks; Gemma-2 shifts reclaimed GAIA bandwidth to Ignored, while Llama-3.1 increases Rejected and Ignored on Multi-Challenge.Gemma-2’s 33.00% Adopted reduction on GAIA is matched by a 33.00% Ignored increase, with rejection at 0.25%; Llama-3.1’s Multi-Challenge Rejected stance rises 10.25% and Ignored rises 18.75%.Source: Table 2 · p. 6, Stance Transitions: Sycophancy… · p. 7

Decoupling Compliance and Capability

  • Mistral v0.3 shows that reduced loafing does not ensure accuracy recovery when latent reasoning capacity is limited.On SWE-bench, loafing falls from 51.75% to 43.25%, while accuracy shifts only from 25.25% to 26.00%.Source: Decoupling Compliance and Capability · p. 7

Discussion, Limitations and Future Work

CED trades higher inference latency for memory efficiency and has deployment and evaluation boundaries that motivate future work.

Discussion, Limitations and Future Work

  • CED’s dual forward-pass imposes 2× compute latency, while avoiding the memory cost of loading auxiliary amateur models.The authors suggest speculative decoding to bypass the dual-pass requirement for high-confidence tokens.Source: Discussion, Limitations and Future Work · p. 7
  • CED requires direct pre-softmax log-probability manipulation, restricting implementation to open-weight architectures.The evaluation uses static adversarial dialogue; deployment in live multi-agent systems is left to future work.Source: Discussion, Limitations and Future Work · p. 7
  • CED restores epistemic sovereignty but cannot supply latent reasoning capabilities absent from the base model.The authors suggest coupling CED with retrieval-augmented generation to provide missing multi-hop knowledge.Source: Discussion, Limitations and Future Work · p. 7

Related Works

The related-work discussion contrasts CED with prior conformity mitigations and contrastive decoding approaches.

Related Works

  • Prior conformity and strategic-deception mitigations include preference fine-tuning, activation steering, and structural loss regularization.The passage identifies training overhead and degradation of general capabilities as scalability bottlenecks for these methods.Source: Related Works · p. 7
  • CED instead isolates and neutralizes conformity through zero-shot, inference-time logit manipulation.Source: Related Works · p. 7
  • The passage says prior contrastive-decoding variants can fail under extreme multipliers, while CED targets multi-agent sycophancy without artificial token boosting or grammatical collapse.Source: Related Works · p. 7

Conclusion

The conclusion presents CED as a zero-shot intervention that suppresses sycophancy and recovers accuracy while decoupling compliance from capability.

Conclusion

  • CED replaces linear logit subtraction with an asymmetric probability clamp and discrete top-k mask.Source: Conclusion · p. 7
  • Across 7,200 trajectories, CED suppresses cognitive loafing by up to 33.00% and yields accuracy recoveries up to 30.75%.Source: Conclusion · p. 7
  • The conclusion frames CED as a training-free behavioral filter that restores sovereignty without inflating capacity-limited architectures’ competence.Source: Conclusion · p. 7

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