arXiv subjects

Dongchan Kim

Publications and source records attributed to Dongchan Kim.

16 recordsLinked to original sources

arXiv2026

Benchmarking Large Language Models on Repairing Qiskit Programs using Bugs4Q

In quantum programs, Bugs4Q is a widely used benchmark containing real quantum defects. However, its evaluation assumes that benchmark labels remain valid and that generated fixes execute in the target environment. We evaluate two Bugs4Q versions containing 67 unique real Qiskit defects, adding executable tests where missing, and re-run all entries across six pinned Qiskit releases (0.25.0, 0.45.0, 1.0.0, 1.1.1, 2.0.0, and 2.3.1). We find that quantum benchmarks can suffer from silent label inversion: entries become invalid without errors when reference fixes stop executing or buggy programs no longer reproduce failures. Thus, correctness depends on the (benchmark, version) pair rather than the benchmark alone. We evaluate four LLMs (GPT-4o-mini, GPT-5o-mini, GPT-5.4, and GPT-5.4-mini), generating up to 10 repair candidates per defect and testing them across all versions. GPT-5.4 achieves the highest pass@10 (48.8%), followed by GPT-5.4-mini (47.3%), GPT-5o-mini (30.3%), and GPT-4o-mini (22.6%). All models perform best on Qiskit 0.45.0 and decline after the Qiskit 1.0 transition. Many failures arise from deprecated or incompatible APIs rather than incorrect repairs, and 64\% of successful repairs occur on entries invalid under the target version. We release a re-validated, version-pinned Bugs4Q benchmark and show that benchmark validation must precede repair evaluation.

cs.SE↗

arXiv2026

Retrieval Collapses When AI Pollutes the Web

The rapid proliferation of AI-generated content on the Web presents a structural risk to information retrieval, as search engines and Retrieval-Augmented Generation (RAG) systems increasingly consume evidence produced by the Large Language Models (LLMs). We characterize this ecosystem-level failure mode as Retrieval Collapse, a two-stage process where (1) AI-generated content dominates search results, eroding source diversity, and (2) low-quality or adversarial content infiltrates the retrieval pipeline. We analyzed this dynamic through controlled experiments involving both high-quality SEO-style content and adversarially crafted content. In the SEO scenario, a 67\% pool contamination led to over 80\% exposure contamination, creating a homogenized yet deceptively healthy state where answer accuracy remains stable despite the reliance on synthetic sources. Conversely, under adversarial contamination, baselines like BM25 exposed $\sim$19\% of harmful content, whereas LLM-based rankers demonstrated stronger suppression capabilities. These findings highlight the risk of retrieval pipelines quietly shifting toward synthetic evidence and the need for retrieval-aware strategies to prevent a self-reinforcing cycle of quality decline in Web-grounded systems.

cs.IR↗

arXiv2025

A Large-Scale Dynamic Characterization of Flaky Tests in Quantum Software: The Qiskit Terra Case Study

Flaky tests, tests that pass or fail nondeterministically without changes to code or environment, pose a threat to software reliability. While classical software engineering has developed a body of techniques to study flakiness, evidence for quantum software remains limited. Prior work relies on static analysis or small sets of manually reported incidents, leaving open questions about their prevalence, characteristics, and detectability. This paper presents a large-scale dynamic characterization of flaky tests in quantum software through a longitudinal case study of the Qiskit Terra core library. We executed the Qiskit Terra test suite 10,000 times across 23 releases in controlled environments. For each release, we measured test-outcome variability, identified flaky tests, estimated empirical failure probabilities, analyzed recurrence across versions, used Wilson confidence intervals to quantify baseline rerun budgets, and mapped flaky tests to Terra subcomponents. Across 27,026 qualified test identifiers, we identified 62 unique flaky tests. Although flakiness rates were low (0-0.17%), recurrence was substantial: 52 of 62 flaky tests (83.87%) reappeared in multiple releases, while only 10 tests (16.13%) were confined to a single release. Failure probabilities spanned several orders of magnitude, with a median of $9 \times 10^{-4}$ and 34 tests (54.84%) at or below $10^{-3}$, implying that thousands to tens of thousands of executions may be required for confident detection. These results show that flakiness observed in the studied Terra releases is rare under our controlled protocol but difficult to detect under typical continuous integration budgets. To support future research and replication, we release a dataset of per-test execution outcomes.

cs.SE↗

arXiv2025

New physics in toponium's shadow?

ATLAS and CMS have recently reported enhancements in the top-antitop production rate near threshold, a region where non-perturbative QCD dynamics associated with toponium formation become relevant. We investigate how this behaviour is modified in the presence of a neutral pseudoscalar that couples to gluons and top quarks, using an effective description that consistently incorporates perturbative Standard Model and new physics contributions, their interference and non-perturbative threshold effects. We show that the combined effect of those ingredients markedly shapes the viable region of the pseudoscalar parameter space, particularly for narrow resonances with masses close to twice the top mass. While Standard Model threshold effects could explain a sizeable part of the measured enhancements, the current data remain compatible with additional contributions from pseudoscalar interactions.

hep-ph↗

arXiv2025

Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech

The rapid advancement of Zero-Shot Text-to-Speech (ZS-TTS) technology has enabled high-fidelity voice synthesis from minimal audio cues, raising significant privacy and ethical concerns. Despite the threats to voice privacy, research to selectively remove the knowledge to replicate unwanted individual voices from pre-trained model parameters has not been explored. In this paper, we address the new challenge of speaker identity unlearning for ZS-TTS systems. To meet this goal, we propose the first machine unlearning frameworks for ZS-TTS, especially Teacher-Guided Unlearning (TGU), designed to ensure the model forgets designated speaker identities while retaining its ability to generate accurate speech for other speakers. Our proposed methods incorporate randomness to prevent consistent replication of forget speakers' voices, assuring unlearned identities remain untraceable. Additionally, we propose a new evaluation metric, speaker-Zero Retrain Forgetting (spk-ZRF). This assesses the model's ability to disregard prompts associated with forgotten speakers, effectively neutralizing its knowledge of these voices. The experiments conducted on the state-of-the-art model demonstrate that TGU prevents the model from replicating forget speakers' voices while maintaining high quality for other speakers. The demo is available at https://speechunlearn.github.io/

cs.SD↗

arXiv2025

A Survey of Predictive Maintenance Methods: An Analysis of Prognostics via Classification and Regression

Predictive maintenance (PdM) has become a crucial element of modern industrial practice. PdM plays a significant role in operational dependability and cost management by decreasing unforeseen downtime and optimizing asset life cycle management. Machine learning and deep learning have enabled more precise forecasts of equipment failure and remaining useful life (RUL). Although many studies have been conducted on PdM, there has not yet been a standalone comparative study between regression- and classification-based approaches. In this review, we look across a range of PdM methodologies, while focusing more strongly on the comparative use of classification and regression methods in prognostics. While regression-based methods typically provide estimates of RUL, classification-based methods present a forecast of the probability of failure across defined time intervals. Through a comprehensive analysis of recent literature, we highlight key advancements, challenges-such as data imbalance and high-dimensional feature spaces-and emerging trends, including hybrid approaches and AI-enabled prognostic systems. This review aims to provide researchers and practitioners with an awareness of the strengths and compromises of various PdM methods and to help identify future research and build more robust, directed adaptive maintenance systems. Future work may include a systematic review of practical aspects such as public datasets, benchmarking platforms, and open-source tools to support the advancement of PdM research.

cs.LG↗

arXiv2025

Identifying Flaky Tests in Quantum Code: A Machine Learning Approach

Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.

cs.SE↗

arXiv2024

A Short-lived Rejuvenation during the Decades-long Changing-look Transition in the Nucleus of Mrk 1018

Changing-look active galactic nuclei (CL-AGNs), characterized by emerging or disappearing of broad lines accompanied with extreme continuum flux variability, have drawn much attention for their potential of revealing physical processes underlying AGN evolution. We perform seven-season spectroscopic monitoring on Mrk~1018, one of the earliest identified CL-AGN. Around 2020, we detect a full-cycle changing-look transition of Mrk~1018 within one year, associated with a nucleus outburst, which likely arise from the disk instability in the transition region between the outer standard rotation-dominated disk and inner advection-dominated accretion flow. Over the past forty-five years, the accretion rate of Mrk~1018 changed 1000 times and the maximum Eddington ratio reached 0.02. By investigating the relation between broad-line properties and Eddington ratio ($L_{\rm bol}/L_{\rm Edd}$), we find strong evidence that the full-cycle type transition is regulated by accretion. There exists a turnover point in the Balmer decrement, which is observed for the first time. The broad Balmer lines change from a single peak in Type 1.0-1.2 to double peaks in Type 1.5-1.8 and the double-peak separation decreases with increasing accretion rate. We also find that the full width at half maximum (FWHM) of the broad Balmer lines obeys FWHM$\propto (L_{\rm bol}/L_{\rm Edd})^{-0.27}$, as expected for a virialized BLR. The velocity dispersion $\sigma_{\rm line}$ follows a similar trend in Type 1.5-1.8, but displays a sharp increases in Type 1.0-1.2, resulting in a dramatic drop of FWHM/$\sigma_{\rm line}$. These findings suggest that a virialized BLR together with accretion-dependent turbulent motions might be responsible for the diversity of BLR phenomena across AGN population.

astro-ph.GA↗

arXiv2024

CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting user intent, employing diverse retrieval strategies, and effectively filtering unintended or inappropriate responses, limiting their effectiveness. This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search framework that addresses these challenges through a multi-stage pipeline comprising image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering. CUE-M incorporates a robust filtering pipeline combining image-based, text-based, and multimodal classifiers, dynamically adapting to instance- and category-specific concern defined by organizational policies. Extensive experiments on real-word datasets and public benchmarks on knowledge-based VQA and safety demonstrated that CUE-M outperforms baselines and establishes new state-of-the-art results, advancing the capabilities of multimodal retrieval systems.

cs.CL↗

arXiv2024

A Panoramic Study of $K$-Factors for 111 Processes at the 14 TeV LHC

In this comprehensive study, we investigate $K$-factors ($K=σ_{\text{NLO}}/σ_{\text{LO}}\equiv 1+δK$) for a broad array of Standard Model processes at the 14 TeV LHC, which are pivotal for background assessments in Beyond the Standard Model (BSM) searches. Using MadGraph5_aMC@NLO, we calculate the leading-order and next-to-leading order (NLO) cross-sections and compute the corresponding $K$-factors for 111 processes. Our analysis reveals $K$-factors ranging from 1.005 for $pp \to jjj$ to 4.221 for $pp\to W^\pm γγγ$. Key findings include: (i) processes involving photons display significantly high $K$-factors, attributed to gluon-initiated processes at NLO; (ii) processes with multiple particle productions, particularly those involving vector bosons, exhibit elevated $K$-factors due to multiple real emission processes; (iii) there exists an inverse correlation between the number of jets and $δK$, indicating that the addition of jets generally leads to a decrease in $δK$. Additionally, our investigation into differential $K$-factors relative to transverse momentum and invariant mass shows notable increases with higher $p_T$, but minimal changes with invariant mass. This study highlights the indispensable role of precise $K$-factor evaluations for accurate interpretations of BSM search outcomes.

hep-ph↗

arXiv2021

Neural Motion Planning for Autonomous Parking

This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. A non-uniform expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.

cs.RO↗

arXiv2020

Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.

cs.CV↗

arXiv2018

Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding

Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new model that learns coupled representations of domains, intents, and slots by taking advantage of their hierarchical dependency in a Spoken Language Understanding system. Our proposed model learns the vector representation of intents based on the slots tied to these intents by aggregating the representations of the slots. Similarly, the vector representation of a domain is learned by aggregating the representations of the intents tied to a specific domain. To the best of our knowledge, it is the first approach to jointly learning the representations of domains, intents, and slots using their hierarchical relationships. The experimental results demonstrate the effectiveness of the representations learned by our model, as evidenced by improved performance on the contextual cross-domain reranking task.

cs.CL↗

arXiv2018

A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding

Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable neural shortlisting-reranking models for large-scale domain classification in IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.

cs.CL↗

arXiv2018

Efficient Large-Scale Domain Classification with Personalized Attention

In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed for many mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in ones to rapidly increase domain coverage and overall IPDA capabilities. We propose a scalable neural model architecture with a shared encoder, a novel attention mechanism that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. Our architecture is designed to efficiently accommodate new domains that appear in-between full model retraining cycles with a rapid bootstrapping mechanism two orders of magnitude faster than retraining. We account for practical constraints in real-time production systems, and design to minimize memory footprint and runtime latency. We demonstrate that incorporating personalization results in significantly more accurate domain classification in the setting with thousands of overlapping domains.

cs.CL↗

arXiv2014

Mid-Infrared Properties of Luminous Infrared Galaxies II: Probing the Dust and Gas Physics of the GOALS Sample

The Great Observatories All-Sky LIRG Survey (GOALS) is a comprehensive, multiwavelength study of luminous infrared galaxies (LIRGs) in the local universe. Here we present the results of a multi-component, spectral decomposition analysis of the low resolution mid-IR Spitzer IRS spectra from 5-38um of 244 LIRG nuclei. The detailed fits and high quality spectra allow for characterization of the individual PAH features, warm molecular hydrogen emission, and optical depths for silicate dust grains and water ices. We find that starbursting LIRGs, which make up the majority of GOALS, are very consistent in their MIR properties (i.e. tau_9.7um, tau_ice, neon line and PAH feature ratios). However, as their PAH EQW decreases, usually an indicator of an increasingly dominant AGN, LIRGs cover a larger spread in these MIR parameters. The contribution from PAHs to the total L(IR) in LIRGs varies from 2-29% and LIRGs prior to their first encounter show higher L(PAH)/L(IR) ratios on average. We observe a correlation between the strength of the starburst (IR8) and the PAH fraction at 8um but not with the 7.7 to 11.3 PAH ratio, suggesting the fractional PDR emission, and not the overall grain properties, is associated with the rise in IR8 for galaxies off the starburst main sequence. We detect crystalline silicate features in 6% of the sample but only in the most obscured sources (s_9.7um < -1.24). Ice absorption features are observed in 11% (56%) of GOALS LIRGs (ULIRGs). Most GOALS LIRGs have L(H2)/L(PAH) ratios elevated above those observed for normal star-forming galaxies and exhibit a trend for increasing L(H2)/L(PAH) ratio with increasing L(H2). While star formation appears to be the dominant process responsible for exciting the H2 in most of the GOALS galaxies, a subset of LIRGs (10%) show excess H2 emission that is inconsistent with PDR models and may be excited by shocks or AGN-induced outflows.

astro-ph.GA↗