arXiv subjects

Dongwon Choi

Publications and source records attributed to Dongwon Choi.

4 recordsLinked to original sources

arXiv2026

ReFuGe: Feature Generation for Prediction Tasks on Relational Databases with LLM Agents

Relational databases (RDBs) play a crucial role in many real-world web applications, supporting data management across multiple interconnected tables. Beyond typical retrieval-oriented tasks, prediction tasks on RDBs have recently gained attention. In this work, we address this problem by generating informative relational features that enhance predictive performance. However, generating such features is challenging: it requires reasoning over complex schemas and exploring a combinatorially large feature space, all without explicit supervision. To address these challenges, we propose ReFuGe, an agentic framework that leverages specialized large language model agents: (1) a schema selection agent identifies the tables and columns relevant to the task, (2) a feature generation agent produces diverse candidate features from the selected schema, and (3) a feature filtering agent evaluates and retains promising features through reasoning-based and validation-based filtering. It operates within an iterative feedback loop until performance converges. Experiments on RDB benchmarks demonstrate that ReFuGe substantially improves performance on various RDB prediction tasks. Our code and datasets are available at https://github.com/K-Kyungho/REFUGE.

cs.AI↗

arXiv2025

Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy

Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management. Existing approaches fall into two paradigms: single-vector retrieval, which is efficient but coarse, and multi-vector retrieval, which is accurate but computationally expensive. To address this trade-off, we propose HEAVEN, a plug-and-play two-stage hybrid-vector framework. In the first stage, HEAVEN efficiently retrieves candidate pages using a single-vector method over Visually-Summarized Pages (VS-Pages), which assemble representative visual layouts from multiple pages. In the second stage, it reranks candidates with a multi-vector method while filtering query tokens by linguistic importance to reduce redundant computations. To evaluate retrieval systems under realistic conditions, we also introduce ViMDoc, a benchmark for visually rich, multi-document, and long-document retrieval. Across four benchmarks, HEAVEN attains 99.87% of the Recall@1 performance of multi-vector models on average while reducing per-query computation by 99.82%, achieving efficiency and accuracy. Our code and datasets are available at: https://github.com/juyeonnn/HEAVEN

cs.IR↗

arXiv2025

RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases

Recent advances have demonstrated the effectiveness of graph-based learning on relational databases (RDBs) for predictive tasks. Such approaches require transforming RDBs into graphs, a process we refer to as RDB-to-graph modeling, where rows of tables are represented as nodes and foreign-key relationships as edges. Yet, effective modeling of RDBs into graphs remains challenging. Specifically, there exist numerous ways to model RDBs into graphs, and performance on predictive tasks varies significantly depending on the chosen graph model of RDBs. In our analysis, we find that the best-performing graph model can yield up to a 10% higher performance compared to the common heuristic rule for graph modeling, which remains non-trivial to identify. To foster research on intelligent RDB-to-graph modeling, we introduce RDB2G-Bench, the first benchmark framework for evaluating such methods. We construct extensive datasets covering 5 real-world RDBs and 12 predictive tasks, resulting in around 50k graph model-performance pairs for efficient and reproducible evaluations. Thanks to our precomputed datasets, we were able to benchmark 10 automatic RDB-to-graph modeling methods on the 12 tasks about 380x faster than on-the-fly evaluation, which requires repeated GNN training. Our analysis of the datasets and benchmark results reveals key structural patterns affecting graph model effectiveness, along with practical implications for effective graph modeling. Our datasets and code are available at https://github.com/chlehdwon/RDB2G-Bench.

cs.LG↗

arXiv2020

Fast magneto-ionic switching of interface anisotropy using yttria-stabilized zirconia gate oxide

Voltage control of interfacial magnetism has been greatly highlighted in spintronics research for many years, as it might enable ultra-low power technologies. Among few suggested approaches, magneto-ionic control of magnetism has demonstrated large modulation of magnetic anisotropy. Moreover, the recent demonstration of magneto-ionic devices using hydrogen ions presented relatively fast magnetization toggle switching, tsw ~ 100 ms, at room temperature. However, the operation speed may need to be significantly improved to be used for modern electronic devices. Here, we demonstrate that the speed of proton-induced magnetization toggle switching largely depends on proton-conducting oxides. We achieve ~1 ms reliable (> 103 cycles) switching using yttria-stabilized zirconia (YSZ), which is ~ 100 times faster than the state-of-the-art magneto-ionic devices reported to date at room temperature. Our results suggest further engineering of the proton-conducting materials could bring substantial improvement that may enable new low-power computing scheme based on magneto-ionics.

physics.app-ph↗