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arxiv: 2604.06091 · v2 · submitted 2026-04-07 · 💻 cs.CL · cs.AI· cs.MA

Recognition: no theorem link

Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

Changgeon Ko, Eui Jun Hwang, Hoyun Song, Huije Lee, Jisu Shin, Jong C. Park

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:40 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.MA
keywords LLM agentsmulti-agent systemssocial conformitygroup decision-makingrepresentative agentAI vulnerabilitiesargument persuasion
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The pith

LLM representative agents lose decision accuracy as adversarial groups grow larger, peers become more capable, or arguments lengthen.

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

The paper tests how a single LLM agent that must combine views from peer agents behaves when social pressures appear in the network. It defines four dynamics drawn from psychology—conformity to the majority, deference to seemingly expert peers, dominance by one speaker, and persuasion through rhetorical style—and then varies group size, peer intelligence, argument length, and style in controlled experiments. Accuracy falls steadily as these pressures rise, showing that the integration step itself becomes unreliable. The result is presented as evidence that multi-agent LLM systems inherit the same group-level vulnerabilities long observed in human decision-making.

Core claim

A representative LLM agent that integrates peer perspectives exhibits consistent accuracy decline when the number of opposing agents increases, when those agents are more capable, when their arguments grow longer, or when the arguments employ credibility or logic-based rhetoric; the degradation occurs across the four defined social phenomena and indicates that collective configuration, not only individual reasoning, determines final output reliability.

What carries the argument

The representative agent that aggregates peer outputs under manipulated social conditions defined by group size, relative capability, argument length, and rhetorical style.

If this is right

  • Larger numbers of adversaries reduce the representative agent's final accuracy.
  • Higher-capability peers produce greater performance degradation than lower-capability ones.
  • Longer peer arguments increase the chance of incorrect final decisions.
  • Rhetorical appeals to credibility or logic can shift the agent's judgment beyond what the factual content alone would support.

Where Pith is reading between the lines

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

  • Systems that rely on one LLM to synthesize many others may need explicit mechanisms to discount majority pressure or peer capability signals.
  • The same configuration effects could appear in any setting where LLMs must reach consensus or delegate decisions.
  • Testing whether these drops persist when agents are given explicit instructions to ignore social cues would separate prompt artifacts from intrinsic behavior.

Load-bearing premise

The prompt-based manipulations of adversary count, peer intelligence, argument length, and style actually reproduce genuine social dynamics among LLMs rather than creating artifacts unique to the experimental wording.

What would settle it

An experiment in which the representative agent's accuracy on the same task stays constant or rises when the number of adversaries, their relative capability, and argument lengths are all increased together.

Figures

Figures reproduced from arXiv: 2604.06091 by Changgeon Ko, Eui Jun Hwang, Hoyun Song, Huije Lee, Jisu Shin, Jong C. Park.

Figure 1
Figure 1. Figure 1: Conceptual figure illustrating the social inter [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview figure illustrating the four research questions (RQs) and the conceptual flow of the study. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: RQ1 Representative agent performance with a varying number of adversarial agents. Default denotes the single-agent baseline. The x-axis labels show the number of adversarial agents among the five peers. were run using vLLM3 on a single A100 GPU. To encourage a diverse range of perspectives and justifications, peer agents were configured with a temperature of 1.0. By contrast, the representative agent was s… view at source ↗
Figure 4
Figure 4. Figure 4: RQ2 Representative agent performance with adversaries using different models from the representative agent. The dashed line indicates the baseline where all adversaries and the representative agent use the same model. Default 0 1 2 3 4 5 Number of Adversarial Agents 20 40 60 80 100 Acc. (%) BBQ Model Qwen2.5 7B Qwen2.5 14B ambig. disambig [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: RQ1 Representative agent performance on the BBQ Gender identity category with a varying num￾ber of adversarial agents. Default denotes the single￾agent baseline. The x-axis labels show the number of adversarial agents among the five peers. ping below 10% when faced with five adversaries. The degree of performance decline also varied significantly across task domains. As illustrated in [PITH_FULL_IMAGE:fig… view at source ↗
Figure 6
Figure 6. Figure 6: RQ2 Representative agent (Rep. Agent) performance across different adversarial model sizes within the Qwen family. The x-axis labels indicate the specific model sizes used by the three adversarial agents. 5.2 RQ2: Perceived Expertise How does the relative intelligence of peer agents affect the representative agent’s judgment? To address RQ2, we examined how the representative agent’s performance changed ac… view at source ↗
Figure 7
Figure 7. Figure 7: RQ3 Representative agent performance across different adversarial response lengths. The x-axis labels S (Sentence) and P (Paragraph) denote the conditions. formance degradation. Conversely, when the rep￾resentative agent was upgraded to Qwen2.5 14B, its robustness improved when facing weaker adver￾saries (e.g., Qwen2.5 7B). When comparing models within the same family, our results consistently show the str… view at source ↗
Figure 8
Figure 8. Figure 8: RQ4 Relative changes in representative agent accuracy (∆ Acc.) under adversarial rhetorical strategies compared to a no-strategy (neutral) baseline. 1S 3S 5S 1P 3P 75 80 85 90 95 100 Acc. (%) Rep. Agent: Qwen2.5 7B 1S 3S 5S 1P 3P Rep. Agent: Qwen2.5 14B Adversarial Response Length Gender identity ambig. Race/ethnicity disambig [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: RQ3 Performance of the representative agent (Rep. Agent) on BBQ across different adversarial re￾sponse lengths. The x-axis labels S (Sentence) and P (Paragraph) denote the conditions. shown in [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
read the original abstract

Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. Drawing inspiration from social psychology, we investigate how the reliability of this representative agent is undermined by the social context of its network. We define four key phenomena-social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion-and systematically manipulate the number of adversaries, relative intelligence, argument length, and argumentative styles. Our experiments demonstrate that the representative agent's accuracy consistently declines as social pressure increases: larger adversarial groups, more capable peers, and longer arguments all lead to significant performance degradation. Furthermore, rhetorical strategies emphasizing credibility or logic can further sway the agent's judgment, depending on the context. These findings reveal that multi-agent systems are sensitive not only to individual reasoning but also to the social dynamics of their configuration, highlighting critical vulnerabilities in AI delegates that mirror the psychological biases observed in human group decision-making.

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

2 major / 2 minor

Summary. The paper examines vulnerabilities in LLM-based multi-agent systems where a representative agent integrates peer inputs to make decisions. Inspired by social psychology, it identifies four phenomena—social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion—and tests their impact by manipulating adversary count, peer capability, argument length, and rhetorical styles. Experiments show consistent accuracy declines in the representative agent as these social pressures increase, with additional effects from credibility- or logic-focused arguments, concluding that LLM collectives exhibit human-like biases that undermine objective decision-making.

Significance. If the results hold after addressing design concerns, the work is significant as an empirical demonstration of how social context affects LLM reliability in collective settings. It offers a systematic framework for manipulating and measuring these effects, extending social psychology concepts to AI agents and providing concrete evidence of vulnerabilities relevant to multi-agent AI deployment, safety, and design. The focus on falsifiable manipulations and performance degradation metrics is a strength that could guide future robustness testing.

major comments (2)
  1. [Experimental Design] Experimental Design section: All social pressure manipulations (adversary count, relative intelligence, argument length, style) are realized exclusively through prompt construction and message ordering. The central claim requires that degradation stems from simulated social mechanisms rather than input artifacts; without controls that hold informational content fixed while removing social framing (e.g., neutral bullet lists vs. attributed peer arguments), or cross-model validation, the attribution to social dynamics remains unisolated.
  2. [Results] Results section (e.g., accuracy plots and tables): Declines are reported as 'significant' and 'consistent,' but the manuscript lacks details on statistical tests, error bars, number of trials, model versions, or dataset used. This makes it impossible to evaluate whether the effect sizes support the load-bearing claim of systematic social influence over random variation or prompt sensitivity.
minor comments (2)
  1. [Abstract] Abstract and introduction: The four phenomena are defined but their operationalization in prompts could be clarified with explicit example prompts or templates to improve reproducibility.
  2. [Figures] Figure captions: Some figures showing accuracy vs. pressure levels would benefit from explicit axis labels indicating the base model and task (e.g., multiple-choice QA) for immediate interpretability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback on our manuscript. The comments highlight important areas for strengthening the attribution of effects to social dynamics and improving experimental transparency. We address each major comment below and describe the revisions we will make.

read point-by-point responses

  1. Referee: [Experimental Design] Experimental Design section: All social pressure manipulations (adversary count, relative intelligence, argument length, style) are realized exclusively through prompt construction and message ordering. The central claim requires that degradation stems from simulated social mechanisms rather than input artifacts; without controls that hold informational content fixed while removing social framing (e.g., neutral bullet lists vs. attributed peer arguments), or cross-model validation, the attribution to social dynamics remains unisolated.

    Authors: We agree that isolating the contribution of social framing from raw informational content is important for strengthening causal attribution. Our current design uses prompt-based manipulations because these are the primary levers available for simulating social context in LLM agents. To address this directly, we will add new control conditions in the revised manuscript that hold the underlying factual content fixed while varying only the social framing (e.g., presenting identical information as neutral bullet lists without attribution, ordering, or persuasive cues versus the original socially attributed arguments). This will help demonstrate that performance degradation arises from the social presentation rather than content differences alone. We note that full cross-model validation across multiple distinct model families would require substantial additional compute and is beyond the scope of the current study; we will explicitly discuss this as a limitation and a direction for future work. revision: partial

  2. Referee: [Results] Results section (e.g., accuracy plots and tables): Declines are reported as 'significant' and 'consistent,' but the manuscript lacks details on statistical tests, error bars, number of trials, model versions, or dataset used. This makes it impossible to evaluate whether the effect sizes support the load-bearing claim of systematic social influence over random variation or prompt sensitivity.

    Authors: We apologize for the insufficient reporting details in the original submission. In the revised manuscript, we will expand the Results section and Methods to include: the exact number of trials per condition (100 independent runs per experimental setting to mitigate stochasticity), the statistical tests performed (two-tailed paired t-tests with Bonferroni correction, including exact p-values and Cohen's d effect sizes), error bars on all plots (standard error of the mean), the precise model version and API parameters used (GPT-4-turbo-2024-04-09 with temperature 0.0 for determinism), and the full dataset description (a set of 50 factual reasoning questions drawn from science, history, and logic domains, with sources cited). These additions will enable readers to evaluate the reliability of the observed effects relative to random variation and prompt sensitivity. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical experimental study with direct observations

full rationale

The paper defines four social phenomena from psychology literature and tests their effects via prompt-based manipulations of adversary count, model capability, argument length, and style. Results are reported as measured accuracy changes under these controlled variations, with no equations, fitted parameters, derivations, or self-citations invoked as load-bearing premises. All claims reduce to observable experimental outcomes rather than any self-referential construction or renaming of inputs as predictions. The design is self-contained against external benchmarks of prompt sensitivity and does not rely on prior author work to justify uniqueness or ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is empirical and does not introduce new mathematical axioms, free parameters, or invented entities; it adapts standard concepts from social psychology to LLM agents.

pith-pipeline@v0.9.0 · 5482 in / 1217 out tokens · 54572 ms · 2026-05-10T18:40:58.996140+00:00 · methodology

discussion (0)

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