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Agentic Limit Order Books: Phase Transitions and Market Impact

arXiv 2609.31260 ↗First posted 25 Sep 2026q-fin.TR cs.AI q-fin.CPcited by 0

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Abstract (arXiv)

We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning agentic traders. By formalizing agent interactions within a microscopic order-matching engine, we examine two fundamental quantitative phenomena: equilibrium phase transitions in order flow regime shifts, and the structural dynamics of market impact. We show that agentic LOBs exhibit distinct phase boundaries separating orderly price discovery from hyper-volatile cascade states, governed by critical thresholds in the number of agents and observable market depth. Furthermore, we demonstrate that market impact under agentic liquidity provision deviates from classical square-root dynamics, exhibiting distinct dissipative, balanced, and non-dissipative regimes under non-linear feedback loops.

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  1. 2026DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Books
  2. 2025ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Optimal Execution with Endogenous Liquidity ↗
  3. 2025Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
  4. 2025Reinforcement Learning in Queue-Reactive Models: Application to Optimal Execution
  5. 2025Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation
  6. 2026FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
  7. 2026Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
  8. 2024Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior