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

• URL: http://arxiv.org/abs/2606.28343v1
• Date: Mon, 01 Jun 2026 20:40:30 GMT
• Title: The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents
• Abstract summary: We show that the high document density required to serve majority interests can geometrically overcrowd the retrieval neighborhood of a semantically similar minority.<n>We introduce a formal framework to analyze how such goal collisions in dense retrieval induce fundamental performance limits.<n>We prove that such objectives drive the system to self-organize into a state that exclusively serves majority interests.
• Score: 51.56484100374058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges [3.1411048327150546]
  We propose a roadmap for compositional scene understanding, active counting agents, and unified multimodal evaluation protocols.<n>The main imperative is to build a robust evaluation infrastructure to distinguish open-world generalization from benchmark-specific optimization.
  arXiv  Detail & Related papers  (2026-08-24T21:32:56Z)
• Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation [38.13720312846963]
  We study diversity in MAS-based ideation across three bottom-up levels: model intelligence, agent cognition, and system dynamics.<n>We characterize these outcomes as collective failures emerging from structural coupling.<n>Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency.
  arXiv  Detail & Related papers  (2026-04-20T09:27:49Z)
• Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges [87.04241991512386]
  We propose the Proxy Compression Hypothesis (PCH) as a unifying framework for understanding reward hacking.<n>Under this view, reward hacking arises from the interaction of objective compression, optimization amplification, and evaluator--policy co-adaptation.<n>This perspective unifies empirical phenomena across RLHF, RLAIF, and RLVR regimes, and explains how local shortcut learning can generalize into broader forms of misalignment.
  arXiv  Detail & Related papers  (2026-04-15T08:11:34Z)
• Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems [27.694287638709643]
  Real-world Multi-Agent Systems are too complex to analyze entirely.<n>We conduct a baseline empirical study investigating how basic MAS topologies and feedback loops influence prejudice.<n>By stripping away advanced complexity to study swarm dynamics, we establish a crucial baseline: structural complexity does not guarantee ethical robustness.
  arXiv  Detail & Related papers  (2026-04-10T05:06:38Z)
• Greedy Is Enough: Sparse Action Discovery in Agentic LLMs [11.62669179647184]
  empirical evidence suggests that only a small subset of actions meaningfully influences performance in a given deployment.<n>Motivated by this observation, we study a contextual linear reward model in which action is governed by a structured sparsity assumption.<n>Our results identify sparse action discovery as a fundamental principle underlying large-action decision-making.
  arXiv  Detail & Related papers  (2026-01-13T07:15:32Z)
• Position: Don't be Afraid of Over-Smoothing And Over-Squashing [22.895536023786974]
  We argue that performance decreases often stem from uninformative receptive fields rather than over-smoothing.<n>We show that architectural interventions designed to mitigate over-squashing fail to yield significant performance gains.
  arXiv  Detail & Related papers  (2026-01-12T11:02:57Z)
• Fair Deepfake Detectors Can Generalize [51.21167546843708]
  We show that controlling for confounders (data distribution and model capacity) enables improved generalization via fairness interventions.<n>Motivated by this insight, we propose Demographic Attribute-insensitive Intervention Detection (DAID), a plug-and-play framework composed of: i) Demographic-aware data rebalancing, which employs inverse-propensity weighting and subgroup-wise feature normalization to neutralize distributional biases; and ii) Demographic-agnostic feature aggregation, which uses a novel alignment loss to suppress sensitive-attribute signals.<n>DAID consistently achieves superior performance in both fairness and generalization compared to several state-of-the-art
  arXiv  Detail & Related papers  (2025-07-03T14:10:02Z)
• Constrained Auto-Regressive Decoding Constrains Generative Retrieval [71.71161220261655]
  Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network.<n>In this paper, we examine the inherent limitations of constrained auto-regressive generation from two essential perspectives: constraints and beam search.
  arXiv  Detail & Related papers  (2025-04-14T06:54:49Z)
• Endogenous Macrodynamics in Algorithmic Recourse [52.87956177581998]
  Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment. We show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level.
  arXiv  Detail & Related papers  (2023-08-16T07:36:58Z)
• Fundamental Limits and Tradeoffs in Invariant Representation Learning [99.2368462915979]
  Many machine learning applications involve learning representations that achieve two competing goals. Minimax game-theoretic formulation represents a fundamental tradeoff between accuracy and invariance. We provide an information-theoretic analysis of this general and important problem under both classification and regression settings.
  arXiv  Detail & Related papers  (2020-12-19T15:24:04Z)

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.