mlsanityData to 14 Oct 2026

Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"

arXiv 2506.18600 ↗First posted 23 Jun 2025cs.CL cs.GT cs.MAcited by 2

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Abstract (arXiv)

A potential concern when simulating populations of large language models (LLMs) is data contamination, i.e. the possibility that training data may shape outcomes in unintended ways. While this concern is important and may hinder certain experiments with multi-agent models, it does not preclude the study of genuinely emergent dynamics in LLM populations. The recent critique by Barrie and T"ornberg [1] of the results of Flint Ashery et al. [2] offers an opportunity to clarify that self-organisation and model-dependent emergent dynamics can be studied in LLM populations, highlighting how such dynamics have been empirically observed in the specific case of social conventions.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

7 of 29 references.

References · 7

  1. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  2. 2024Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?cited by 63
  3. 2025How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfarecited by 20
  4. 2015The Spontaneous Emergence of Conventions: An Experimental Study of Cultural Evolutioncited by 17
  5. 2017The Emergence of Consensus: A Primercited by 12
  6. 2025Emergent LLM behaviors are observationally equivalent to data leakagecited by 9
  7. 2018The Dynamics of Norm Change in the Cultural Evolution of Languagecited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.