Human-AI Co-Evolution and Epistemic Collapse: A Dynamical Systems Perspective

• URL: http://arxiv.org/abs/2605.06347v1
• Date: Thu, 07 May 2026 14:31:17 GMT
• Title: Human-AI Co-Evolution and Epistemic Collapse: A Dynamical Systems Perspective
• Abstract summary: Humans and language models form a coupled dynamical system linked by a feedback loop of usage, generation, and retraining.<n>We show that increasing reliance on AI can induce a transition toward a low-diversity, suboptimal equilibrium.<n>Results suggest that the trajectory of AI systems is shaped not only by model design, but by the dynamics of human-AI co-evolution.
• Score: 2.1061291753905405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are reshaping how knowledge is produced, with increasing reliance on AI systems for generation, summarization, and reasoning. While prior work has studied cognitive offloading in humans and model collapse in recursive training, these effects are typically considered in isolation. We propose a unified perspective: humans and language models form a coupled dynamical system linked by a feedback loop of usage, generation, and retraining. We introduce a minimal model with three variables -- human cognition, data quality, and model capability -- and show that this feedback can give rise to distinct dynamical regimes. Our analysis identifies three regimes: co-evolutionary enhancement, fragile equilibrium, and degenerative convergence. Through a simple simulation, we demonstrate that increasing reliance on AI can induce a transition toward a low-diversity, suboptimal equilibrium. From an information-theoretic perspective, this transition corresponds to an emergent information bottleneck in the human-AI loop, where entropy reduction reflects loss of diversity and support under closed-loop feedback rather than beneficial compression. These results suggest that the trajectory of AI systems is shaped not only by model design, but by the dynamics of human-AI co-evolution.

Related papers

• Evolutionary Systems Thinking -- From Equilibrium Models to Open-Ended Adaptive Dynamics [1.2691047660244335]
  Complex change is often described as "evolutionary" in economics, policy, numerically and technology.<n>This paper argues that evolutionary dynamics should be treated as a core system-thinking problem rather than as a biological metaphor.
  arXiv  Detail & Related papers  (2026-02-17T19:17:50Z)
• Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AI [0.8481798330936976]
  We argue that foundation models should integrate actions, at multiple scales of abstraction, with a compositional generative architecture and episodic memory.<n>We describe how the addition of these missing components to foundation models could help address some of their current deficiencies.<n>We conclude by arguing that a rekindling of the historically fruitful exchange of ideas between brain science and AI will help pave the way towards safe and interpretable human-centered AI.
  arXiv  Detail & Related papers  (2025-12-27T11:54:54Z)
• Guided Self-Evolving LLMs with Minimal Human Supervision [53.111086364268566]
  Unguided self-evolving systems often plateau quickly or degrade as training.<n>R-Few is a guided Self-Play Challenger acquirer framework that incorporates lightweight human oversight through in-context grounding and mixed training.<n>R-Few achieves consistent and iterative improvements across math and general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-12-02T07:06:11Z)
• The Universal Landscape of Human Reasoning [60.72403709545137]
  We introduce Information Flow Tracking (IF-Track) to quantify information entropy and gain at each reasoning step.<n>We show that IF-Track captures essential reasoning features, identifies systematic error patterns, and characterizes individual differences.<n>This approach establishes a quantitative bridge between theory and measurement, offering mechanistic insights into the architecture of reasoning.
  arXiv  Detail & Related papers  (2025-10-24T16:26:36Z)
• ECHO: Ego-Centric modeling of Human-Object interactions [71.17118015822699]
  ECHO (Ego-Centric modeling of Human-Object interactions) is developed.<n>It recovers three modalities: human pose, object motion, and contact from such minimal observation.<n>It outperforms existing methods that do not offer the same flexibility.
  arXiv  Detail & Related papers  (2025-08-29T12:12:22Z)
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning [63.25540801694765]
  Large Language Models (LLMs) demonstrate striking linguistic abilities, yet whether they achieve this same balance remains unclear.<n>We apply the Information Bottleneck principle to quantitatively compare how LLMs and humans navigate this compression-meaning trade-off.
  arXiv  Detail & Related papers  (2025-05-21T16:29:00Z)
• Shifting Attention to You: Personalized Brain-Inspired AI Models [3.0128071072792366]
  We show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model over doubles behavioral performance compared to the unmodified CLIP baseline.<n>Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human-computer interaction.
  arXiv  Detail & Related papers  (2025-02-07T04:55:31Z)
• Learning by Surprise: Surplexity for Mitigating Model Collapse in Generative AI [1.6545633988217645]
  As synthetic content infiltrates the web, generative AI models may be retrained on their own outputs.<n>This leads to model collapse: a progressive loss of performance and diversity across generations.<n>We introduce new measures that characterise collapse directly from a model's next-token probability distributions.
  arXiv  Detail & Related papers  (2024-10-16T08:02:48Z)
• Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption [64.07607726562841]
  Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration. In this work, we tackle the task of reconstructing closely interactive humans from a monocular video. We propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information.
  arXiv  Detail & Related papers  (2024-04-17T11:55:45Z)
• MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions [11.972017738888825]
  We propose Model Autophagy Analysis (MONAL) for large models' self-consumption explanation. MONAL employs two distinct autophagous loops to elucidate the suppression of human-generated information in the exchange between human and AI systems. We evaluate the capacities of generated models as both creators and disseminators of information.
  arXiv  Detail & Related papers  (2024-02-17T13:02:54Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.