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Representative Papers

Occupied Processes: Going with the Flow

Nov 14, 2023

Modeling strongly path-dependent financial derivatives—such as exotic options and variance instruments—remains challenging due to the non-Markovian nature of path-dependent functionals. Method: This paper introduces the “occupied process” framework, augmenting the original process $X$ with its occupation measure flow $O$ to form a Markovian lifted system $(O,X)$. It defines the novel “occupation derivative”, unifying functional Itô calculus and mean-field derivatives, and recasts a broad class of path-dependent PDEs as parabolic equations in the occupation measure time variable. Contribution/Results: The framework enables an Itô calculus tailored to path occupation-time functionals and extends the Feynman–Kac formula accordingly. It yields closed-form solutions to local-time-driven optimal stopping problems, with direct applications to corridor variance swap pricing and path-dependent volatility modeling. By bridging stochastic analysis, mean-field theory, and financial mathematics, this work substantially expands both the theoretical foundations and practical applicability of path-dependent stochastic modeling.

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Is this Citation on Point?

Aug 12, 2026

This study addresses the challenge of determining whether legal citations genuinely support the claims they are intended to substantiate. The authors propose an evaluation framework based on controlled perturbations—specifically, substituting cited cases or altering page numbers—to systematically assess models’ ability to distinguish between topical relevance and proposition-level evidentiary support. Experiments were conducted across two legal corpora (court opinions and legal briefs) using fourteen model configurations enhanced with high-reasoning prompting. Results reveal that while models excel at identifying irrelevant cases (93–100% accuracy), they exhibit marked deficiencies in detecting page-number mismatches (37–83% accuracy). Notably, even GPT-5.4 in high-reasoning mode fails to flag 40% of such page-level errors, underscoring a critical limitation: current models conflate thematic relatedness with precise, citation-specific support.

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Is this Citation on Point?

Aug 12, 2026

This study addresses the challenge of determining whether legal citations genuinely support the claims they are intended to substantiate. The authors propose an evaluation framework based on controlled perturbations—specifically, substituting cited cases or altering page numbers—to systematically assess models’ ability to distinguish between topical relevance and proposition-level evidentiary support. Experiments were conducted across two legal corpora (court opinions and legal briefs) using fourteen model configurations enhanced with high-reasoning prompting. Results reveal that while models excel at identifying irrelevant cases (93–100% accuracy), they exhibit marked deficiencies in detecting page-number mismatches (37–83% accuracy). Notably, even GPT-5.4 in high-reasoning mode fails to flag 40% of such page-level errors, underscoring a critical limitation: current models conflate thematic relatedness with precise, citation-specific support.

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Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

Jul 27, 2026

This work addresses the challenge of causal discovery in high-dimensional, nonstationary multivariate time series by introducing an open-source Python library that integrates, for the first time, a unified GPU-accelerated conditional independence testing layer, plug-in structural change-point detection, and multiple causal discovery algorithms—including CDNOTS, GES, Granger causality, and LASSO-VAR—to enable piecewise causal modeling and end-to-end causal effect estimation. Implemented in PyTorch for computational efficiency, the library supports Python 3.10–3.12, offers a command-line interface, and seamlessly integrates with DoWhy. Released publicly on GitHub, this tool significantly enhances the scalability and usability of causal analysis for nonstationary time series data.

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