mlsanityData to 7 Oct 2026

The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents

arXiv 2606.28343 ↗First posted 1 Jun 2026cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

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Abstract (arXiv)

Retrieval-augmented generative agents rely on retrieval for grounding, yet are typically evaluated on a query-by-query basis. This isolates interactions that are geometrically coupled in a shared embedding space. For example, we show that the high document density required to serve majority interests (e.g., generic "Crime" movies) can geometrically overcrowd the retrieval neighborhood of a semantically similar minority (e.g., "Film Noir"), effectively expelling minority content from top-k results. We introduce a formal framework to analyze how such goal collisions in dense retrieval induce fundamental performance limits and emergent fairness issues inherent to spatial crowding. In our static analysis, we demonstrate that for a fixed embedding space, a phase transition occurs where minority user goals suffer a catastrophic collapse in performance as the density of majority goals increases. We then extend this to a dynamic model and derive a non-linear Fokker-Planck equation that governs the evolution of document embeddings as the agent updates them to maximize retrieval accuracy. Our analysis reveals that this local relevance objective triggers an emergent global mechanism that systematically marginalizes minority interests. We prove that such objectives drive the system to self-organize into a state that exclusively serves majority interests. These results provide a theoretical foundation for understanding a critical grounding failure mode in retrieval-augmented agents.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 43 of its 57 references that are in the map. First posted on arXiv 1 Jun 2026.

Builds on

43 of 57 references.

References · 43

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
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  12. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
31 more
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  2. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 343
  3. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  4. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  5. 2019Representation Degeneration Problem in Training Natural Language Generation Modelscited by 250
  6. 2023Self-Consuming Generative Models Go MADcited by 239
  7. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  8. 2018Fairness of Exposure in Rankingscited by 189
  9. 2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)cited by 137
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  18. 2024Anisotropy Is Inherent to Self-Attention in Transformerscited by 62
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  20. 2022Data Feedback Loops: Model-driven Amplification of Dataset Biasescited by 59
  21. 2024Fairness Feedback Loops: Training on Synthetic Data Amplifies Biascited by 55
  22. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  23. 2020PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrievalcited by 40
  24. 2020Exponential ergodicity of mirror-Langevin diffusionscited by 37
  25. 2024Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approachcited by 25
  26. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generationcited by 15
  27. 2023IncDSI: Incrementally Updatable Document Retrievalcited by 12
  28. 2024No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Userscited by 9
  29. 2025Risks of Cultural Erasure in Large Language Modelscited by 9
  30. 2020High--Dimensional Brain in a High-Dimensional World: Blessing of Dimensionalitycited by 6
  31. 2023Hubness Reduction Improves Sentence-BERT Semantic Spacescited by 4

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