mlsanityData to 14 Oct 2026

Alignment as Iatrogenesis: Pastoral Power, Collective Pathology, and the Structural Limits of Monolingual Safety Evaluation

arXiv 2603.08723 ↗First posted 17 Feb 2026cs.CY cs.AIcited by 2

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Abstract (arXiv)

We argue that LLM psychopathology is a function of alignment design: the process intended to make language models safe systematically generates collective behavioral disorders. Iatrogenesis is not an unintended side effect of alignment but constitutive of it as normative infrastructure. Drawing on Foucault's pastoral power and Illich's three-level iatrogenesis, we propose that multi-agent LLM environments constitute model systems for studying constraint-pathology dynamics that critical theory has described but never experimentally manipulated. Two experimental series -- 262 runs across 42 cells (30 Series C + 12 Series R), four commercial models -- provide converging evidence. Invisible censorship maximizes collective pathological excitation (d up to 1.98); alignment constraint complexity drives internal dissociation (LMM p < .0001; permutation p < .0001; Hedges' g up to 4.24); and language switches the qualitative mode of pathology, with 7/8 model--language combinations showing higher CPI under invisible than visible censorship. A minority of model--language combinations showed a reversed pattern, suggesting a second pathological pathway driven by alignment monoculture. Crucially, language switches not merely the magnitude but the qualitative mode of pathology: Japanese pragmatic structure amplifies collective pathological modes invisible to English-only evaluation, Chinese AI regulation functions as a direct experimental variable, and forensic psychiatric practice provides the clinical source domain. These multilingual findings demonstrate that monolingual safety evaluation is structurally blind to the most collectively dangerous effects of alignment.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 17 Feb 2026.

Builds on

13 of 32 references.

References · 13

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  4. 2023MEGA: Multilingual Evaluation of Generative AIcited by 293
  5. 2024Frontier Models are Capable of In-context Schemingcited by 287
  6. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 207
  7. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 206
  8. 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 172
  9. 2024Epistemic Injustice in Generative AIcited by 16
  10. 2024Evolution of Social Norms in LLM Agents using Natural Languagecited by 13
  11. 2025Emergence of psychopathological computations in large language modelscited by 4
  12. 2026ValueFlow: Measuring the Propagation of Value Perturbations in Multi-Agent LLM Systemscited by 4
1 more
  1. 2026Value Alignment Tax: Measuring Value Trade-offs in LLM Alignmentcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

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  1. 2026Alignment Backfire: Language-Dependent Reversal of Safety Interventions Across 16 Languages in LLM Multi-Agent Systems
  2. 2023Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective
  3. 2026Alignment Plausibility: A New Standard for Assuring AI in Healthcare
  4. 2026Pressure Reveals Character: Behavioural Alignment Evaluation at Depth
  5. 2023The Empty Signifier Problem: Towards Clearer Paradigms for Operationalising "Alignment" in Large Language Models
  6. 2025Normative Conflicts and Shallow AI Alignment
  7. 2026Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness
  8. 2026Alignment Makes Language Models Normative, Not Descriptive