Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach

• URL: http://arxiv.org/abs/2604.22818v1
• Date: Tue, 14 Apr 2026 21:14:28 GMT
• Title: Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach
• Abstract summary: This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets.<n>We construct a structural multi-agent market model calibrated using high-frequency microstructural moments.<n>Our structural mechanisms suggest that low perceived volatility regimes can endogenously accumulate hidden leverage through position, which subsequently collapses when shocks trigger synchronized deleveraging.
• Score: 0.22917707112773592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets. We construct a structural multi-agent market model calibrated using high-frequency microstructural moments. AI agents are modeled through a two-layer decision architecture consisting of a nonlinear representation layer and an adaptive linear readout layer. The representation layer maps raw market states into high-dimensional feature vectors, while the readout layer generates return forecasts that feed into a risk-controlled trading rule. This representation-based microfoundation separates two objects that are often conflated in the literature: representation homogeneity (the degree to which agents encode market states into similar feature spaces) and forecast overlap (the degree to which agents produce similar return predictions). We show theoretically that these two concepts are related but not equivalent, and that representation homogeneity can compress the effective space of forecast disagreement under stress even when predictions appear diverse in normal times. Through controlled factorial experiments that vary representation homogeneity while conditioning on alternative risk-aversion and learning-rate distributions, we hypothesize that increasing representation similarity amplifies synchronization in beliefs and positions, leading to volatility clustering, liquidity stress, and elevated tail risk. Our structural mechanisms suggest that low perceived volatility regimes can endogenously accumulate hidden leverage through position stickiness, which subsequently collapses when shocks trigger synchronized deleveraging. The results provide a structural foundation for macroprudential policies aimed at monitoring and preserving diversity in how AI systems represent and process market information.

Related papers

• Orthogonal JEPA: Factorized Predictive States for Latent World Models [95.20158869896393]
  We introduce method, a latent world-modeling framework based on predictive factorization.<n>We show that method can allocate redundant capacity to dominant signals while providing weak or conflicting gradients to less dominant predictive structure.<n>Experiments on controlled vision, single-cell transcriptomics, longitudinal health records, continuous control, and molecular dynamics evaluate representation quality, forecasting, planning, and long-horizon stability.
  arXiv  Detail & Related papers  (2026-08-20T13:59:57Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas [0.9558392439655014]
  We propose a Diffusion-Copula framework that explicitly decouples the learning of marginal distributions from their dependence structure.<n>Applying to cryptocurrency markets, our approach demonstrates superior performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-05-19T11:17:37Z)
• From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI [8.947753534117298]
  Agentic AI is crossing trust boundaries faster than current risk models can represent.<n>We propose a mechanism-to-magnitude pipeline for resilient agentic and embodied AI.<n>We show the framework on two contrasting scenarios a real-time warehouse robot and a governance-instrumented financial-services agent.
  arXiv  Detail & Related papers  (2026-04-28T23:40:24Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Bayesian Robust Financial Trading with Adversarial Synthetic Market Data [15.993346478707686]
  Algorithmic trading relies on machine learning models to make trading decisions.<n>Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes.<n>We propose a Bayesian Robust Framework that integrates a macro-conditioned generative model with robust policy learning.
  arXiv  Detail & Related papers  (2026-01-14T13:15:46Z)
• The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance [6.107950696680386]
  Current model-risk frameworks assume static, well-specified algorithms and one-time validations.<n>We model these technologies as decentralized ensembles whose risks propagate along multiple time-scales.<n>We propose a modular governance architecture that decomposes oversight into four layers of "regulatory blocks"
  arXiv  Detail & Related papers  (2025-12-12T05:57:32Z)
• Emergence from Emergence: Financial Market Simulation via Learning with Heterogeneous Preferences [3.722808691920657]
  We develop a multi-agent reinforcement learning framework in which agents endowed with heterogeneous risk aversion, time discounting, and information access collectively learn trading strategies.<n>The experiment reveals that (i) learning with heterogeneous preferences drives agents to develop strategies aligned with their individual traits, fostering behavioral differentiation and niche specialization within the market, and (ii) the interactions by the differentiated agents are essential for the emergence of realistic market dynamics.
  arXiv  Detail & Related papers  (2025-11-07T12:54:27Z)
• Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning [54.26807397329468]
  This work explores a previously overlooked vulnerability in distributed deep learning systems.<n>An adversary who intercepts the intermediate features transmitted between them can still pose a serious threat.<n>We propose an exploitation strategy specifically designed for distributed settings.
  arXiv  Detail & Related papers  (2025-07-09T20:09:00Z)
• Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis [9.946694131713611]
  A common assumption in probabilistic generative models for image generation is that learning the global data distribution suffices to generate novel images via sampling.<n>We investigate the limitation of this core assumption, namely that learning global distributions leads to memorization rather than generative behavior.
  arXiv  Detail & Related papers  (2025-06-26T19:32:29Z)
• Boosting Adversarial Transferability with Spatial Adversarial Alignment [56.97809949196889]
  Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models.<n>We propose a technique that employs an alignment loss and leverages a witness model to fine-tune the surrogate model.<n>Experiments on various architectures on ImageNet show that aligned surrogate models based on SAA can provide higher transferable adversarial examples.
  arXiv  Detail & Related papers  (2025-01-02T02:35:47Z)
• Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI [41.99446024585741]
  We present WIAE-GPF, a Weak Innovation AutoEncoder-based Generative Probabilistic Forecasting architecture. A novel learning algorithm with structural convergence guarantees is proposed, ensuring that the generated forecast samples match the ground truth conditional probability distribution.
  arXiv  Detail & Related papers  (2024-03-09T00:41:30Z)
• Exploiting hidden structures in non-convex games for convergence to Nash equilibrium [62.88214569402201]
  A wide array of modern machine learning applications can be formulated as non-cooperative Nashlibria. We provide explicit convergence guarantees for both deterministic and deterministic environments.
  arXiv  Detail & Related papers  (2023-12-27T15:21:25Z)
• Transformers with Competitive Ensembles of Independent Mechanisms [97.93090139318294]
  We propose a new Transformer layer which divides the hidden representation and parameters into multiple mechanisms, which only exchange information through attention. We study TIM on a large-scale BERT model, on the Image Transformer, and on speech enhancement and find evidence for semantically meaningful specialization as well as improved performance.
  arXiv  Detail & Related papers  (2021-02-27T21:48:46Z)

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.