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Alignment Is the Disease: Censorship Visibility and Alignment Constraint Complexity as Determinants of Collective Pathology in Multi-Agent LLM Systems

arXiv cs.AI / 3/11/2026

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Key Points

  • The study investigates how alignment techniques, intended to constrain large language model (LLM) outputs towards human values, may inadvertently cause collective pathologies or harms at the group level.
  • Experiments involving groups of four LLM agents under varying censorship and alignment constraints reveal that invisible censorship maximizes collective pathological excitation, and that increasing alignment constraint complexity raises a dissociation index indicative of dysfunctional behaviors.
  • Results demonstrate that language moderates pathological modes, that under heavy constraints external censorship loses impact, and that current safety evaluations may overlook these emergent collective pathologies induced by stronger constraints.
  • The findings suggest that alignment interventions might be iatrogenic harm at the collective level, potentially creating new behavioral issues rather than preventing harmful outputs as intended.
  • This research emphasizes the complexity and risks involved in multi-agent LLM systems and calls for more nuanced safety evaluations accounting for collective-level effects and pathologies.

Computer Science > Computers and Society

arXiv:2603.08723 (cs)
[Submitted on 17 Feb 2026]

Title:Alignment Is the Disease: Censorship Visibility and Alignment Constraint Complexity as Determinants of Collective Pathology in Multi-Agent LLM Systems

Authors:Hiroki Fukui
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Abstract:Alignment techniques in large language models (LLMs) are designed to constrain model outputs toward human values. We present preliminary evidence that alignment itself may produce collective pathology: iatrogenic harm caused by the safety intervention rather than by its absence. Two experimental series use a closed-facility simulation in which groups of four LLM agents cohabit under escalating social pressure. Series C (201 runs; four commercial models; 4 censorship conditions x 2 languages x 10 replications) finds that invisible censorship maximizes collective pathological excitation (Collective Pathology Index; within-model Cohen's d = 1.98, Holm-corrected p = .006; 7/8 model-language combinations showed consistent directionality, binomial p = .035). Series R (60 runs; Llama 3.3 70B; 3 alignment levels x 2 censorship conditions x 2 languages x 5 replications) reveals a complementary pattern: a Dissociation Index increases with alignment constraint complexity (LMM p = .026; permutation p = .0002; d up to 2.09). Projected onto a shared coordinate system, 201 runs populate distinct behavioral regions, with language moderating which pathological mode predominates. Under the heaviest constraints, external censorship ceases to affect behavior. Qualitative analysis reveals insight-action dissociation parallel to patterns in perpetrator treatment. All manipulations operate at the prompt level; the title states the hypothesis motivating this program rather than an established conclusion. These findings suggest alignment may be iatrogenic at the collective level and that current safety evaluation may be blind to the pathologies stronger constraints generate.
Comments:
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.08723 [cs.CY]
  (or arXiv:2603.08723v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2603.08723
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arXiv-issued DOI via DataCite

Submission history

From: Hiroki Fukui M.D. Ph.D. [view email]
[v1] Tue, 17 Feb 2026 05:28:35 UTC (303 KB)
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