arXiv subjects

Shinyoung Kang

Publications and source records attributed to Shinyoung Kang.

5 recordsLinked to original sources

arXiv2026

Integer-Exact Microscaling: Running MXFP4 Language Models on INT8 Matrix Units

On hardware whose fastest matrix datapath is INT8$\times$INT8, such as the Neural Accelerators of Apple's M5 GPUs, 4-bit weights must become INT8 operands. Existing MXFP4 kernels already map FP4 (E2M1) values to integers by doubling them, but apply every 32-element block scale inside the reduction. We show when no per-block work is needed: a row of MXFP4 weights is exactly one INT8 vector times one power-of-two scale if its block exponents span at most three, so a linear layer becomes a single INT8 GEMM with one rescale per output. Enforcing the condition adds at most 1/16 of a row's largest rounding error to the blocks it changes, and real weights already satisfy it: 97.9-99.97% of rows across seven models, including 99.93% of the 95M rows of a 125B-parameter mixture-of-experts (MoE) model, Qwen3.8-Flash-Next. The resulting spread-constrained MXFP4 files stay standard MXFP4. With integer instead of power-of-two block multipliers, an integer-exact format (16-wide blocks, multipliers 1..10, 4.26 bits) runs as one exact INT8 GEMM and is more accurate than NVFP4 (4.5 bits) with its standard scale rule on five small models, within 0.06 perplexity of NVFP4 with scale search. On Qwen3.8-Flash-Next's routed experts with INT8 activations it is as close to the FP8 model as NVFP4 with scale search (KL divergence 0.0557 vs. 0.0553) and closer than MLX's MXFP4 and affine INT4; with every matrix quantized it solves 94.8% of GSM8K vs. 91.0% for MLX's MXFP4. Quantizing only the 74k routed expert matrices (96% of the parameters) to either of our formats with INT8 activations costs at most 0.2% perplexity. A complete W4A8 system on the M5 produces outputs bit-identical to the quantized reference; prefill is 1.34-1.74x faster than MLX on a dense model, 1.10-1.57x on two MoE models up to 35B parameters and 1.22-1.29x on Qwen3.8-Flash-Next, where shared non-GEMM work bounds the gain.

cs.LG↗

arXiv2026

OASIS: A Rubric-Based Multimodal Assessment Platform Using Large Language Models

OASIS (Open Assessment and Scoring Infrastructure Stack) is a systems platform for rubric-based grading of video, audio, and text with large language models. Scoring one artifact with an LLM is straightforward; deploying assessment at scale requires encounter management, rubric versioning, modality-aware execution, provenance capture, and human review. OASIS pairs a standalone command-line interface with a canonical integrated Elephant + MAPLES stack for encounter management and multimodal grading orchestration. Both paths can target hosted APIs or self-hosted open-weight models through Ollama and OpenAI-compatible endpoints such as vLLM. SimRubrics rubric authoring and the Wayfinder conversational agent gateway are optional extensions that use the same authenticated interfaces as human operators. Given a rubric and recorded encounters, OASIS produces per-criterion scores, evidence, and rationales, preserving execution artifacts for audit. Distinctive features include rubric-as-program compilation, progressive execution plans, content-addressable grading identity, transcript-augmented multimodal grading, explicit review state, and a shared command surface for humans and autonomous agents. Though developed in medical education, the architecture is domain-agnostic, applying wherever structured performance can be evaluated from recorded or written artifacts. In production at UT Southwestern Medical Center since Fall 2023, the platform has processed more than 7,000 encounters. This publication includes the report and project information, not application source, binaries, installation materials, sample data, or a tagged software release.

cs.SE↗

arXiv2025

Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing

In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $CO_{2}$ Henry's constant values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 88.6% and 78.0% for binary classification tasks on pore volume and $CO_{2}$ Henry's constant, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 92% and 80% across different classes for pore volume and $CO_{2}$ Henry's constant datasets. Finally, the performance of generating MOF with target properties showed accuracies of 93.5% for pore volume and 87% for $CO_{2}$ Henry's constant, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs.

cs.LG↗

arXiv2025

Quantum Computing Based Design of Multivariate Porous Materials

Multivariate (MTV) porous materials exhibit unique structural complexities based on diverse spatial arrangements of multiple building block combinations. These materials possess potential synergistic functionalities that exceed the sum of their individual components. However, the exponentially increasing design complexity of these materials poses challenges for accurate ground-state configuration prediction and design. To address this, a Hamiltonian model was developed for quantum computing that integrates compositional, structural, and balance constraints, enabling efficient optimization of the MTV configurations. The model employs a graph-based representation to encode linkers as qubits. To validate our model, a variational quantum circuit was constructed and executed using the Sampling VQE algorithm. Simulations on experimentally known MTV porous materials successfully reproduced their ground-state configurations, demonstrating the validity of our model. Furthermore, VQE calculations were performed on real quantum hardware for validation purposes, signaling a first step toward a practical quantum algorithm for the rational design of porous materials.

quant-ph↗

arXiv2024

Large Language Models for Medical OSCE Assessment: A Novel Approach to Transcript Analysis

Grading Objective Structured Clinical Examinations (OSCEs) is a time-consuming and expensive process, traditionally requiring extensive manual effort from human experts. In this study, we explore the potential of Large Language Models (LLMs) to assess skills related to medical student communication. We analyzed 2,027 video-recorded OSCE examinations from the University of Texas Southwestern Medical Center (UTSW), spanning four years (2019-2022), and several different medical cases or "stations." Specifically, our focus was on evaluating students' ability to summarize patients' medical history: we targeted the rubric item 'did the student summarize the patients' medical history?' from the communication skills rubric. After transcribing speech audio captured by OSCE videos using Whisper-v3, we studied the performance of various LLM-based approaches for grading students on this summarization task based on their examination transcripts. Using various frontier-level open-source and proprietary LLMs, we evaluated different techniques such as zero-shot chain-of-thought prompting, retrieval augmented generation, and multi-model ensemble methods. Our results show that frontier LLM models like GPT-4 achieved remarkable alignment with human graders, demonstrating a Cohen's kappa agreement of 0.88 and indicating strong potential for LLM-based OSCE grading to augment the current grading process. Open-source models also showed promising results, suggesting potential for widespread, cost-effective deployment. Further, we present a failure analysis identifying conditions where LLM grading may be less reliable in this context and recommend best practices for deploying LLMs in medical education settings.

cs.CL↗