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Rutgers University

Academic institutionnorthamerica · us
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Research library827linked papers
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Selected work

Representative Papers

Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis

Feb 18, 2026

Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising 1,500 queries across five experimental groups. Results demonstrate that reranking significantly improves answer quality, achieving 49.0 percent correctness for scores of 8 or above compared to 33.5 percent without reranking, representing a 15.5 percentage point improvement. Additionally, the error rate for completely incorrect answers decreases from 35.3 percent to 22.5 percent. Our findings emphasize the critical role of reranking in financial RAG systems and demonstrate performance improvements over baseline methods through modern language models and refined retrieval strategies.

7 citationsRead paper

Dynamic heterogeneous distribution regression panel models, with an application to labor income processes

Feb 08, 2022Social Science Research Network

This paper addresses the challenge of dynamic forecasting and steady-state distribution inference in panel data with cross-sectional heterogeneity in unit-specific coefficients. We propose a dynamic heterogeneous distribution regression framework that jointly estimates individual-level heterogeneous coefficients and their functional targets—including one-step-ahead forecasts, steady-state cross-sectional distributions, and quantile treatment effects. To enable uniform asymptotically valid inference on functional parameters under unknown heterogeneity, we develop a novel cross-sectional bootstrap procedure—the first of its kind for such settings. The method integrates fixed-effects estimation, distribution regression, and quantile treatment effect modeling. Empirical application to PSID data reveals that negative income shocks significantly increase right-skewness in labor income distributions and raise poverty persistence rates, while higher education mitigates these effects; moreover, income mobility exhibits systematic heterogeneity across individuals. Simulation studies confirm the method’s robustness and reliability.

4 citations1 influentialRead paper

Private Estimation and Inference in High-Dimensional Regression with FDR Control

Oct 25, 2023

This paper addresses the challenges of differentially private (DP) parameter estimation, statistical inference, and multiple testing control in high-dimensional linear regression. To this end, we propose a unified framework comprising three key components: (i) the first DP-protected Bayesian Information Criterion (BIC) for adaptive model sparsity selection; (ii) a privacy-preserving debiased LASSO estimator enabling unbiased parameter estimation and valid confidence interval construction under DP; and (iii) the first provably false discovery rate (FDR)-controlled DP multiple testing procedure, built upon a privacy-adapted Benjamini–Hochberg algorithm. Theoretical analysis establishes rigorous DP guarantees, statistical efficiency, and exact FDR control at the nominal level. Empirical evaluation demonstrates substantial improvements over state-of-the-art DP baselines: 42% higher sparsity identification accuracy, confidence interval coverage approaching the nominal level, and stable FDR containment strictly below the pre-specified threshold.

3 citations1 influentialRead paper

ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Sets

Feb 07, 2026

This work addresses the limited robustness of traditional counterfactual explanations, which are tailored to a single model and often fail to remain valid under model uncertainty or across near-optimal models. To overcome this, the authors propose a novel approach that constructs an ellipsoidal approximation of the Rashomon set and optimizes counterfactual generation over this set, marking the first integration of such an approximation into an algorithmic recourse framework. The method offers theoretical guarantees regarding uniqueness, stability, and alignment of feature directions, supports user-defined constraints, and enables efficient computation. Empirical results demonstrate that the generated counterfactuals remain effective across multiple near-optimal models, achieve significantly faster computation than existing baselines, and exhibit enhanced flexibility and robustness.

2 citationsRead paper

Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts

Jan 23, 2026

This work addresses the limitations of current large language model (LLM) agents in geospatial reasoning, which often rely on web search or pattern matching due to a lack of genuine computational capabilities, leading to spatial relational hallucinations. The authors frame geospatial question answering as a conceptual transformation problem and propose GeoFlow—a framework that constructs executable directed acyclic graphs through spatial concept extraction, functional role assignment, and ordered constraint template generation. By grounding reasoning in core theories from spatial information science, GeoFlow enables principled, interpretable, and reliable geospatial inference. This approach represents the first integration of foundational spatial information science principles into AI agents, significantly enhancing both explainability and correctness. Evaluated on the MapEval-API and MapQA benchmarks, GeoFlow outperforms established baselines such as ReAct and Reflexion, generating executable and semantically consistent geospatial workflows.

2 citationsRead paper
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