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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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6 results for “Path-Law”

Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functionscs.LG

ARL bridges non-Markovian decision processes with reinforcement learning, improving foresight and stability.

problem Inaccurate foresight in non-Markovian environments due to state-based methods' limitations.

method Lifted state space into a signature-augmented manifold, using a self-consistent field approach to anticipate future path-law.

result ARL achieves deterministic evaluation of expected returns with reduced computational complexity and variance.

Reinforcement Learning Non-Markovian Processes Path-Law Signature Manifold Self-Consistent Field

2026-04-06abs ↗pdf ↗

Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHSstat.ML

Novel framework synthesizes stochastic trajectories with anticipated structural breaks.

problem Synthesizing forward-looking, time-evolving stochastic trajectories with anticipated structural breaks.

method Anticipatory Neural Jump-Diffusion (ANJD) flow, AVNSG for dynamic spectral whitening.

result The framework effectively captures non-commutative moments and high-order stochastic texture.

stochastic trajectories anticipated structural breaks maximum mean discrepancy signature manifold dynamic spectral whitening

2026-04-06abs ↗pdf ↗

Model Risk via Signature-Induced Optimal Transportq-fin.RM

Framework uses optimal transport to quantify model risk in stochastic path laws.

problem Model risk in stochastic path laws.

method Signature-induced optimal transport framework.

result Explicit robust bounds and budget-aware sparse surrogate method.

Model Risk Optimal Transport Signature Stochastic Path Budget Awareness

2026-07-22abs ↗pdf ↗

Triangular-Reference Schrödinger Bridges for Time Series Generationstat.ML

New method generates synthetic time series paths with more flexibility.

problem Restrictions in generating synthetic paths using Brownian reference.

method Introduces Triangular-Reference Schrödinger Bridges (TR-SBTS) for time series generation.

result Generates synthetic paths with more flexibility in stochastic volatility and correlated noise.

Schrödinger bridges time series generation stochastic volatility correlated noise latent covariance

2026-05-26abs ↗pdf ↗

Closing the Approximation Gap in Simulation-free Latent SDEsstat.ML

Simulation-free VI closes the approximation gap in latent SDEs

problem Recovering dynamical systems from noisy observations

method Helmholtz-SDE

result Recovers dynamics more faithfully than prior methods

simulation-free latent SDEs Helmholtz-SDE posterior inference parameter learning

2026-06-15abs ↗pdf ↗

Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approachmath.OC

Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.

problem Learning stochastic dynamics from endpoint and intermediate distributional observations.

method Formulates generation as a McKean-Vlasov control problem with soft energy constraints, solving it through FBSDE.

result Model learns coherent stochastic trajectories matching prescribed marginal laws.

Stochastic Dynamics Generative Models Soft Constraints FBSDE Human Motion

2026-05-09abs ↗pdf ↗