arXiv subjects

Dongmin Kim

Publications and source records attributed to Dongmin Kim.

At least 19 recordsLinked to original sources

arXiv2026

High-Throughput Normalized Min-Sum Belief Propagation Decoding for Quantum LDPC Codes with Near-Memory Processing

Real-time quantum error correction requires classical decoders to process growing syndrome workloads with low and predictable latency. For quantum low-density parity-check (qLDPC) codes, iterative belief propagation (BP) repeatedly updates messages over sparse Tanner graphs, creating substantial memory-access and data-movement demands. We map normalized Min-Sum BP decoding of the [[144,12,12]] Bivariate Bicycle qLDPC code onto a DPU-based Processing-in-Memory (PIM) architecture. Within each DPU, 11 tasklets cooperatively decode one syndrome, while multiple DPUs process independent syndrome instances in parallel. Using uPIMulator and a data-qubit Pauli error model with ideal syndrome measurements, we compare throughput, per-syndrome processing time, logical error rate (LER), and single-syndrome tail latency against a 16-logical-CPU baseline. At a component-wise physical error probability of p=0.001 and one BP iteration, the projected aggregate kernel throughput of 2,560 DPUs reaches 1.071 x 10^7 decodes/s, compared with 1.22 x 10^6 decodes/s for the CPU, an 8.8x improvement. From two iterations onward, the measured LER remains below the physical error probability for every evaluated value of p. For one to five iterations, the maximum sampled serialized X+Z DPU compute latency remains below the 1 ms decoder-side reference for trapped-ion QEC, reaching approximately 0.873 ms at five iterations. These results show that near-memory processing can provide high aggregate throughput and sub-millisecond compute latency for qLDPC BP decoding under the evaluated conditions.

quant-ph↗

arXiv2026

A Dataset and Benchmark for Optical Music Recognition of String Quartet Scores

Optical music recognition (OMR) transcribes music scores into digital formats. While the field has advanced significantly on monophonic and piano-form scores, multi-part score transcription remains underexplored, largely due to the absence of a suitable dataset. We introduce OpenScore String Quartet for Optical Music Recognition (OSSQ-OMR), the first dataset dedicated to multi-part OMR. Built on the OpenScore String Quartet corpus, OSSQ-OMR pairs digitally encoded scores with their original scanned editions from IMSLP, with all images visually aligned to their transcriptions. The dataset is released with score images at system and staff levels, and paired transcriptions in three encoding formats: Extended Linearized MusicXML (LMXE), **kern, and ABC. In total, OSSQ-OMR contains 24,544 system images and 98,172 staff images drawn from 116 string quartet scores. We accompany the dataset with a benchmark protocol and baseline results from two representative OMR models, evaluated across four random score-level splits with mutually exclusive test sets. Baselines reach OMR-NED as low as 3.6% on synthetic and 5.9% on scanned inputs; results reveal substantial effects of encoding and segmentation choices, with the LSTM-based baseline degrading on scanned inputs roughly 2.6 times less than the Transformer-based baseline.

cs.CV↗

arXiv2026

Adaptive Qubit Freezing Enables Robust Graph Partitioning for Divide-and-Conquer QAOA

Divide-and-conquer variants of the Quantum Approximate Optimization Algorithm (QAOA) provide a promising route for executing combinatorial optimization problems beyond the qubit capacity of near-term quantum devices. However, existing approaches rely on the existence of small vertex separators and fail entirely on dense or highly connected graphs where such decompositions do not exist. We introduce Frozen Large Graph Partitioning (FrozenLGP), an adaptive decomposition framework that transforms partitionability from an assumption into an enforceable property. When standard partitioning fails, FrozenLGP identifies the minimum set of obstructing vertices through a minimum-vertex-cut computation based on max-flow and classically freezes their spin assignments. The energetic contributions of the removed interactions are rigorously preserved by folding them into linear bias terms in the Ising Hamiltonian of neighboring active qubits. Across graph sizes up to 10,000 vertices and multiple topology families, FrozenLGP achieves 100\% decomposition coverage, compared with 4.6\% for the standard divide-and-conquer baseline on high-connectivity instances. End-to-end MaxCut experiments demonstrate that FrozenLGP preserves approximation quality on instances already solvable by conventional divide-and-conquer QAOA while extending applicability to previously unsupported graphs, and outperforming alternative full-coverage decomposition strategies. Noise simulations further show improved robustness arising from reduced entangling-gate requirements. These results establish FrozenLGP as a topology-robust front end for distributed QAOA on near-term quantum hardware.

quant-ph↗

arXiv2026

Reducing Postselection Overhead in Magic-State Cultivation by In-Patch Multiplexing

Fault-tolerant quantum computing requires high-fidelity logical magic states for implementing non-Clifford operations. Magic-state cultivation provides a lower-overhead route to logical magic-state preparation, but its efficiency is limited by postselection loss during the early injection-and-cultivation stages. In this work, we propose an in-patch multiplexing scheme that uses early-stage idle resources within a single logical patch to create multiple local cultivation opportunities. A candidate that passes the early stages is forwarded to the standard escape pathway, while the escape stage and the decoder-based acceptance procedure are kept identical to those of the single-site baseline. Under a uniform depolarizing noise model with idle noise, the proposed protocol substantially reduces the injection-and-cultivation discard rate and the expected number of attempts required to obtain an accepted early-stage candidate. At a physical error rate of \(p=2\times10^{-3}\), the injection-and-cultivation expected attempts are reduced by \(45.46\%\) for \(d_1=3\) and by \(72.91\%\) for \(d_1=5\), relative to the single-site MSC baseline. In the direct full-cycle evaluation including escape, the expected attempts per kept logical output are further reduced by \(49.04\%\) for \(d_1=3\) and by \(78.69\%\) for \(d_1=5\) at the same physical error rate. The full-cycle cost curves are shifted toward smaller expected attempts, while the final logical-error behavior remains governed by the escape-stage gap threshold. These results show that in-patch multiplexing can reduce postselection overhead while preserving the standard magic-state cultivation framework.

quant-ph↗

arXiv2026

Simulating Infant First-Person Sensorimotor Experience via Motion Retargeting from Babies to Humanoids

Motion retargeting from humans to human-like artificial agents is becoming increasingly important as humanoid robots grow more capable. However, most existing approaches focus only on reproducing kinematics and ignore the rich sensorimotor experience associated with human movement. In this work, we present a framework for simulating the multimodal sensorimotor experiences of infants using physical and virtual humanoids. From a single video, our method reconstructs the infant's body configuration by extracting its skeletal structure and estimating the full 3D pose from each frame. Then we map the reconstructed motion onto several developmental platforms: the physical iCub robot and the virtual simulators pyCub, EMFANT and MIMo. Replaying the retargeted motions on these embodiments produces simulated multisensory streams including proprioception (joints and muscles), touch, and vision. For the best-matching embodiment, the retargeting achieves sub-centimeter accuracy and enables a rich multimodal analysis of infant development as well as enhanced automated annotation of behaviors. This framework provides a unique window into the infant's sensorimotor experience, offering new tools for robotics, developmental science, and early detection of neurodevelopmental disorders. The code is available at https://github.com/ctu-vras/motion-retargeting/.

q-bio.NC↗

arXiv2026

Active Inference with a Self-Prior in the Mirror-Mark Task

The mirror self-recognition test evaluates whether a subject touches a mark on its own body that is visible only in a mirror, and is widely used as an indicator of self-awareness. In this study, we present a computational model in which this behavior emerges spontaneously through a single mechanism, the self-prior, without any external reward. The self-prior, implemented with a Transformer, learns the density of familiar multisensory experiences; when a novel mark appears, the discrepancy from this learned distribution drives mark-directed behavior through active inference. A simulated infant, relying solely on vision and proprioception without tactile input, discovered a sticker placed on its own face in the mirror and removed it in approximately 70% of cases without any explicit instruction. Expected free energy decreased significantly after sticker removal, confirming that the self-prior operates as an internal criterion for distinguishing self from non-self. Cross-modal sampling further demonstrated that the self-prior captures visual--proprioceptive associations, functioning as a probabilistic body schema. These results provide a concise computational account of the key behavior observed in the mirror test and suggest that the free energy principle can serve as a unifying hypothesis for investigating the developmental origins of self-awareness. Code is available at: https://github.com/kim135797531/self-prior-mirror

cs.LG↗

arXiv2026

From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning

The evaluation of large language models (LLMs) has predominantly relied on static datasets, which offer limited scalability and fail to capture the evolving reasoning capabilities of recent models. To overcome these limitations, we propose an agent-centric benchmarking paradigm that moves beyond static datasets by introducing a dynamic protocol in which autonomous agents iteratively generate, validate, and solve problems. Within this protocol, a teacher agent generates candidate problems, an orchestrator agent rigorously verifies their validity and guards against adversarial attacks, and a student agent attempts to solve the validated problems. An invalid problem is revised by the teacher agent until it passes validation. If the student correctly solves the problem, the orchestrator prompts the teacher to generate more challenging variants. Consequently, the benchmark scales in difficulty automatically as more capable agents are substituted into any role, enabling progressive evaluation of large language models without manually curated datasets. Adopting text anomaly detection as our primary evaluation format, which demands cross-sentence logical inference and resists pattern-matching shortcuts, we demonstrate that this protocol systematically exposes corner-case reasoning errors that conventional benchmarks fail to reveal. We further advocate evaluating systems along several complementary axes including cross-model pairwise performance and progress between the initial and orchestrator-finalized problems. By shifting the focus from fixed datasets to dynamic protocols, our approach offers a sustainable direction for evaluating ever-evolving language models and introduces a research agenda centered on the co-evolution of agent-centric benchmarks.

cs.CL↗

arXiv2025

Constraint-Optimal Driven Allocation for Scalable QEC Decoder Scheduling

Fault-tolerant quantum computing (FTQC) requires fast and accurate decoding of Quantum Error Correction (QEC) syndromes. However, in large-scale systems, the number of available decoders is much smaller than the number of logical qubits, leading to a fundamental resource shortage. To address this limitation, Virtualized Quantum Decoder (VQD) architectures have been proposed to share a limited pool of decoders across multiple qubits. While the Minimize Longest Undecoded Sequence (MLS) heuristic has been introduced as an effective scheduling policy within the VQD framework, its locally greedy decision-making structure limits its ability to consider global circuit structure, causing inefficiencies in resource balancing and limited scalability. In this work, we propose Constraint-Optimal Driven Allocation (CODA), an optimization-based scheduling algorithm that leverages global circuit structure to minimize the longest undecoded sequence length. Across 19 benchmark circuits, CODA achieves an average 74\% reduction in the longest undecoded sequence length. Crucially, while the theoretical search space scales exponentially with circuit size, CODA effectively bypasses this combinatorial explosion. Our evaluation confirms that the scheduling time scales linearly with the number of qubits, determined by physical resource constraints rather than the combinatorial search space, ensuring robust scalability for large-scale FTQC systems. These results demonstrate that CODA provides a global optimization-based, scalable scheduling solution that enables efficient decoder virtualization in large-scale FTQC systems.

quant-ph↗

arXiv2025

ReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection

In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points without semantic context, overlooking rich textual metadata such as feature descriptions and domain knowledge that experts rely on in practice. This limitation restricts research flexibility and prevents models from fully leveraging domain knowledge for detection. ReTabAD addresses this gap by restoring textual semantics to enable context-aware tabular AD research. We provide (1) 20 carefully curated tabular datasets enriched with structured textual metadata, together with implementations of state-of-the-art AD algorithms including classical, deep learning, and LLM-based approaches, and (2) a zero-shot LLM framework that leverages semantic context without task-specific training, establishing a strong baseline for future research. Furthermore, this work provides insights into the role and utility of textual metadata in AD through experiments and analysis. Results show that semantic context improves detection performance and enhances interpretability by supporting domain-aware reasoning. These findings establish ReTabAD as a benchmark for systematic exploration of context-aware AD.

cs.AI↗

arXiv2025

Detecting disease progression from animal movement using hidden Markov models

Understanding disease dynamics is crucial for managing wildlife populations and assessing spillover risk to domestic animals and humans, but infection data on free-ranging animals are difficult to obtain. Because pathogen and parasite infections can alter host movement, infection status may be inferred from animal trajectories. We present a hidden Markov model (HMM) framework that links observed movement behaviors to unobserved infection states, consistent with epidemiological compartmental models (e.g., susceptible, infected, recovered, dead). Using movement data from 84 reintroduced scimitar-horned oryx (Oryx dammah), 38 confirmed dead in the field and 6 sampled for disease testing, we demonstrate how HMMs can incorporate epidemiological structure through (1) constrained transition probabilities (e.g., to preclude or allow recovery), (2) covariate effects on transmission, and (3) hierarchically structured HMMs (HHMMs) for multi-scale transitions. Comparing veterinary diagnostic reports with model outputs, we found that HMMs with epidemiological constraints successfully identified infection-associated reductions in movement, whereas unconstrained models failed to capture disease progression. Simulations further showed that constrained HMMs accurately classified susceptible, infected, and recovered states. By illustrating flexible formulations and a workflow for model selection, we provide a transferable approach for detecting infection from movement data. This framework can enhance wildlife disease surveillance, guide population management, and improve understanding of disease dynamics.

q-bio.QM↗

arXiv2025

Safety-Aware Robust Model Predictive Control for Robotic Arms in Dynamic Environments

Robotic manipulators are essential for precise industrial pick-and-place operations, yet planning collision-free trajectories in dynamic environments remains challenging due to uncertainties such as sensor noise and time-varying delays. Conventional control methods often fail under these conditions, motivating the development of Robust MPC (RMPC) strategies with constraint tightening. In this paper, we propose a novel RMPC framework that integrates phase-based nominal control with a robust safety mode, allowing smooth transitions between safe and nominal operations. Our approach dynamically adjusts constraints based on real-time predictions of moving obstacles\textemdash whether human, robot, or other dynamic objects\textemdash thus ensuring continuous, collision-free operation. Simulation studies demonstrate that our controller improves both motion naturalness and safety, achieving faster task completion than conventional methods.

cs.RO↗

arXiv2025

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains

Despite the widespread use of tabular data in real-world applications, most benchmarks rely on average-case metrics, which fail to reveal how model behavior varies across diverse data regimes. To address this, we propose MultiTab, a benchmark suite and evaluation framework for multi-dimensional, data-aware analysis of tabular learning algorithms. Rather than comparing models only in aggregate, MultiTab categorizes 196 publicly available datasets along key data characteristics, including sample size, label imbalance, and feature interaction, and evaluates 13 representative models spanning a range of inductive biases. Our analysis shows that model performance is highly sensitive to such regimes: for example, models using sample-level similarity excel on datasets with large sample sizes or high inter-feature correlation, while models encoding inter-feature dependencies perform best with weakly correlated features. These findings reveal that inductive biases do not always behave as intended, and that regime-aware evaluation is essential for understanding and improving model behavior. MultiTab enables more principled model design and offers practical guidance for selecting models tailored to specific data characteristics. All datasets, code, and optimization logs are publicly available at https://huggingface.co/datasets/LGAI-DILab/Multitab.

cs.LG↗

arXiv2025

Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio

Music exists in various modalities, such as score images, symbolic scores, MIDI, and audio. Translations between each modality are established as core tasks of music information retrieval, such as automatic music transcription (audio-to-MIDI) and optical music recognition (score image to symbolic score). However, most past work on multimodal translation trains specialized models on individual translation tasks. In this paper, we propose a unified approach, where we train a general-purpose model on many translation tasks simultaneously. Two key factors make this unified approach viable: a new large-scale dataset and the tokenization of each modality. Firstly, we propose a new dataset that consists of more than 1,300 hours of paired audio-score image data collected from YouTube videos, which is an order of magnitude larger than any existing music modal translation datasets. Secondly, our unified tokenization framework discretizes score images, audio, MIDI, and MusicXML into a sequence of tokens, enabling a single encoder-decoder Transformer to tackle multiple cross-modal translation as one coherent sequence-to-sequence task. Experimental results confirm that our unified multitask model improves upon single-task baselines in several key areas, notably reducing the symbol error rate for optical music recognition from 24.58% to a state-of-the-art 13.67%, while similarly substantial improvements are observed across the other translation tasks. Notably, our approach achieves the first successful score-image-conditioned audio generation, marking a significant breakthrough in cross-modal music generation.

cs.SD↗

arXiv2025

Emergence of Goal-Directed Behaviors via Active Inference with Self-Prior

Infants often exhibit goal-directed behaviors, such as reaching for a sensory stimulus, even when no external reward criterion is provided. These intrinsically motivated behaviors facilitate spontaneous exploration and learning of the body and environment during early developmental stages. Although computational modeling can offer insight into the mechanisms underlying such behaviors, many existing studies on intrinsic motivation focus primarily on how exploration contributes to acquiring external rewards. In this paper, we propose a novel density model for an agent's own multimodal sensory experiences, called the "self-prior," and investigate whether it can autonomously induce goal-directed behavior. Integrated within an active inference framework based on the free energy principle, the self-prior generates behavioral references purely from an intrinsic process that minimizes mismatches between average past sensory experiences and current observations. This mechanism is also analogous to the acquisition and utilization of a body schema through continuous interaction with the environment. We examine this approach in a simulated environment and confirm that the agent spontaneously reaches toward a tactile stimulus. Our study implements intrinsically motivated behavior shaped by the agent's own sensory experiences, demonstrating the spontaneous emergence of intentional behavior during early development.

cs.AI↗

arXiv2024

Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel Encoding

We introduce a project that revives a piece of 15th-century Korean court music, Chihwapyeong and Chwipunghyeong, composed upon the poem Songs of the Dragon Flying to Heaven. One of the earliest examples of Jeongganbo, a Korean musical notation system, the remaining version only consists of a rudimentary melody. Our research team, commissioned by the National Gugak (Korean Traditional Music) Center, aimed to transform this old melody into a performable arrangement for a six-part ensemble. Using Jeongganbo data acquired through bespoke optical music recognition, we trained a BERT-like masked language model and an encoder-decoder transformer model. We also propose an encoding scheme that strictly follows the structure of Jeongganbo and denotes note durations as positions. The resulting machine-transformed version of Chihwapyeong and Chwipunghyeong were evaluated by experts and performed by the Court Music Orchestra of National Gugak Center. Our work demonstrates that generative models can successfully be applied to traditional music with limited training data if combined with careful design.

cs.SD↗

arXiv2023

Enhancing the Yield of Bucket Brigade Quantum Random Access Memory using Redundancy Repair

Quantum Random Access Memory (qRAM) is an essential computing element for running oracle-based quantum algorithms. qRAM exploits quantum superposition to access all data stored in the memory cells simultaneously and guarantees the superior performance of quantum algorithms. A qRAM memory cell comprises logical qubits encoded through quantum error correction technology for successful operation against various quantum noises. In addition to quantum noise, the low-technology nodes based on silicon technology can increase the qubit density and may introduce defective qubits. As qRAM comprises many qubits, its yield will be reduced by defective qubits; these qubits must be handled using QEC scheme. However, the QEC scheme requires numerous physical qubits, which burdens resource overhead. In this paper, to resolve this overhead problem, we propose a novel quantum memory architecture that compensates for defective qubits by introducing redundant qubits. We also analyze the yield improvement offered by our proposed quantum memory architecture by varying the ideal fabrication error rate from 0.5% to 1% for different numbers of logical qubits in the qRAM. We demonstrate that for the qRAM comprising 1,024 logical qubits, eight redundant logical qubits improved the yield by 95.92% from that of qRAM not employing the redundant repair scheme.

quant-ph↗

arXiv2023

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distribution shifts between training and test data. This paper highlights the prevalence of the new normal problem in unsupervised time-series anomaly detection studies. To tackle this issue, we propose a simple yet effective test-time adaptation strategy based on trend estimation and a self-supervised approach to learning new normalities during inference. Extensive experiments on real-world benchmarks demonstrate that incorporating the proposed strategy into the anomaly detector consistently improves the model's performance compared to the baselines, leading to robustness to the distribution shifts.

cs.LG↗

arXiv2023

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.

cs.LG↗