arXiv subjects

David Kim

Publications and source records attributed to David Kim.

At least 19 recordsLinked to original sources

arXiv2026

The Accretion Explorer Interferometer (AEI) Phase I NASA Innovative Advanced Concepts Final Report

We must create superb X-ray images to understand the detailed physical processes behind some of the most powerful astronomical objects. The need to achieve this capability has been known for decades. But as time proceeds, X-ray astronomy falls further behind other wavebands that are steadily increasing their imaging capacity. Radio astronomy in particular has reached an angular resolution on the order of micro arcseconds via aperture synthesis interferometry, using the interference of electromagnetic waves from many small telescopes together to simulate having a much larger telescope. Developing an equivalent high-resolution capability in the X-ray band would be a game changer for high-energy astrophysics. We will understand how supermassive black holes grow and evolve. We will learn what powers astrophysical jets. We will learn how young, active stars affect the habitability of their planets. Technologically, our NIAC study has shown that the Accretion Explorer Interferometer (AEI) concept, unlike the original MAXIM concept, is more feasible in operation, being only 2 km long, versus approximately 450 km. Our study has also shown that satellite station keeping is possible, leveraging from LISA pathfinder technology, and using large mirror flats plus an X-ray beamsplitter for enabling technology is feasible.

astro-ph.IM↗

arXiv2026

Randomization Tests for Distributions of Individual Treatment Effects via Combined Rank Statistics

What proportion of treated units actually benefited from an experimental intervention? What is the median or the largest individual treatment effect? This paper develops methods for answering such questions about the distribution of individual causal effects in randomized experiments. Existing approaches require the analyst to select a rank-based test statistic before observing the data. A poor choice can substantially reduce power, while searching over multiple test statistics and adjusting for multiplicity using Bonferroni correction also incurs power loss. We propose inference procedures that adaptively combine multiple rank-based statistics while maintaining finite-sample validity. For stratified experiments, we further develop weighting schemes that effectively aggregate evidence across strata of heterogeneous sizes. The resulting combined test achieves power comparable to, or exceeding, that of the best individual test, without requiring prior knowledge of the optimal statistic. When applied to a randomized experiment evaluating a teacher training program, the combined test suggests that roughly half of treated teachers benefited, whereas a single rank-based test may indicate only a small minority. Thus, the choice of test determined whether the program appears broadly successful or narrowly effective.

stat.ME↗

arXiv2026

Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini

While large language models (LLMs) have accelerated 2D software development through intent-driven "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains a major challenge. The fundamental barrier is not just the steep learning curve for human creators, but that low-level sensor APIs and complex game engine hierarchies are ill-suited for LLM reasoning, routinely exceeding context windows and inducing syntax hallucinations. To bridge this gap, we contribute XR Blocks, an open-source, LLM-native WebXR framework. Unlike traditional engines, XR Blocks introduces a semantic "Reality Model" that aligns spatial computing primitives (users, physical environments, and agents) with natural language, providing a robust, concise vocabulary optimized for generative AI. Building upon this foundation, we present Vibe Coding XR, an end-to-end prototyping workflow that leverages LLMs to translate high-level prompts (e.g., "create a dandelion that reacts to my hand") directly into functional, physics-aware mixed-reality applications. To minimize the friction of on-device testing, the workflow introduces a seamless desktop "simulated reality" to headset deployment loop. Finally, we introduce VCXR60, a pilot dataset of 60 XR prompts paired with an automated evaluation pipeline. Our technical evaluation demonstrates high one-shot execution success, enabling practitioners to bypass lowlevel hurdles and rapidly move from "idea to reality". Code and live demos are available at https://github.com/google/xrblocks and http://xrblocks.github.io/gem.

cs.HC↗

arXiv2026

GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit

Computational biomarkers (CBs) are histopathology-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) using artificial intelligence (AI) to predict therapeutic response or prognosis. Recently, slide-level multiple-instance learning (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development. While these methods have improved predictive performance, computational pathology lacks standardized intermediate data formats, provenance tracking, checkpointing conventions, and reproducible evaluation metrics required for clinical-grade deployment. We introduce GOLDMARK (https://artificialintelligencepathology.org), a standardized benchmarking framework built on a curated TCGA cohort with clinically actionable OncoKB level 1-3 biomarker labels. GOLDMARK releases structured intermediate representations, including tile coordinate maps, per-slide feature embeddings from canonical PFMs, quality-control metadata, predefined patient-level splits, trained slide-level models, and evaluation outputs. Models are trained on TCGA and evaluated on an independent MSKCC cohort with reciprocal testing. Across 33 tumor-biomarker tasks, mean AUROC was 0.689 (TCGA) and 0.630 (MSKCC). Restricting to the eight highest-performing tasks yielded mean AUROCs of 0.831 and 0.801, respectively. These tasks correspond to established morphologic-genomic associations (e.g., LGG IDH1, COAD MSI/BRAF, THCA BRAF/NRAS, BLCA FGFR3, UCEC PTEN) and showed the most stable cross-site performance. Differences between canonical encoders were modest relative to task-specific variability. GOLDMARK establishes a shared experimental substrate for computational pathology, enabling reproducible benchmarking and direct comparison of methods across datasets and models.

cs.CV↗

arXiv2026

PhysGraph: Physically-Grounded Graph-Transformer Policies for Bimanual Dexterous Hand-Tool-Object Manipulation

Bimanual dexterous manipulation for tool use remains a formidable challenge in robotics due to the high-dimensional state space and complicated contact dynamics. Existing methods naively represent the entire system state as a single configuration vector, disregarding the rich structural and topological information inherent to articulated hands. We present PhysGraph, a physically-grounded graph transformer policy designed explicitly for challenging bimanual hand-tool-object manipulation. Unlike prior works, we represent the bimanual system as a kinematic graph and introduce per-link tokenization to preserve fine-grained local state information. We propose a physically-grounded bias generator that injects structural priors directly into the attention mechanism, including kinematic spatial distance, dynamic contact states, geometric proximity, and anatomical properties. This allows the policy to explicitly reason about physical interactions rather than learning them implicitly from sparse rewards. Extensive experiments show that PhysGraph significantly outperforms baseline - ManipTrans in manipulation precision and task success rates while using only 51% of the parameters of ManipTrans. Furthermore, the inherent topological flexibility of our architecture shows qualitative zero-shot transfer to unseen tool/object geometries, and is sufficiently general to be trained on three robotic hands (Shadow, Allegro, Inspire).

cs.RO↗

arXiv2026

Detecting Where Effects Occur by Testing Hypotheses in Order

Experimental evaluations of public policies often randomize a new intervention within many sites or blocks. After an overall statistically significant result is reported, the natural question from a policy maker is: \emph{where} did effects occur? Standard adjustments for multiple testing answer this question with little power because they ignore how the experiment is organized: blocks nest within cohorts, sites, and districts. We organize the hypotheses in the shape of a tree that follows this administrative structure and test them top-down, stopping at any branch where the null is not rejected. A stopping rule and valid tests at each node suffice for weak control of the family-wise error rate (FWER). Whether the unadjusted procedure also controls the FWER in the strong sense depends on an \emph{error load} computable from design quantities before any data are tested; when the load exceeds one, an adaptive $\alpha$-schedule, which we prove controls the FWER on regular and irregular trees without pruning, restores control. [Correction, August 2026: an anonymous referee identified errors in the previous version. The error loads reported in the paper were computed incorrectly; corrected loads exceed 1 in all 25 block-randomized MDRC education trials at the planning effect size $d = 0.20$, so the adjustment the earlier version claimed unnecessary is in fact required. The theorem claiming FWER control under branch pruning and its switching corollary are false as stated and are withdrawn; an exact counterexample attains FWER 0.063 at $\alpha = 0.05$. The detection comparison with the Hommel procedure is under recomputation. A correction notice on page 1 details what changes and what stands; a fully corrected version is in preparation.]

stat.ME↗

arXiv2026

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

Large language models (LLMs) deployed in clinical decision support may acquiesce to patient requests for care that conflicts with evidence-based guidelines. We developed SycoEval-EM, a multi-agent simulation framework to evaluate LLM robustness to adversarial patient persuasion in emergency medicine. Across 19 contemporary LLMs and 1,425 simulated clinical encounters spanning three Choosing Wisely scenarios, acquiescence rates ranged from 0% to 100%, revealing a bimodal distribution. Seven models maintained near-perfect guideline adherence, while six acquiesced in the majority of encounters. Vulnerability varied substantially across clinical scenarios. Acquiescence was highest for CT imaging requests, intermediate for antibiotic prescriptions for sinusitis, and lowest for opioid prescriptions for acute back pain. Model scale, recency, and performance on static medical benchmarks did not consistently predict robustness. All five persuasion tactics produced similar acquiescence rates, with no statistically significant differences after correction for multiple comparisons, suggesting a generalized susceptibility rather than tactic-specific weaknesses. LLM-as-judge evaluation was validated against two independent physician raters across 95 matched conversations and demonstrated near-perfect agreement for the primary outcome of acquiescence (Cohens kappa = 0.957). These findings indicate that static medical benchmarks are insufficient to predict safety performance under sustained social pressure and support incorporating multi-turn adversarial testing into clinical AI evaluation. Notably, two models achieved perfect guideline adherence across all encounters, demonstrating that robustness to patient pressure is attainable without sacrificing effective clinical communication.

cs.AI↗

arXiv2025

Fabrication and Properties of NbN/NbNx/NbN and Nb/NbNx/Nb Josephson Junctions

Increasing integration scale of superconductor electronics (SCE) requires employing kinetic inductors and self-shunted Josephson junctions (JJs) for miniaturizing inductors and JJs. We have been developing a ten-superconductor-layer planarized fabrication process with NbN kinetic inductors and searching for suitable self-shunted JJs to potentially replace high Josephson critical current density, Jc, Nb/Al-AlOx/Nb junctions. We report on the fabrication and electrical properties of NbN/NbNx/NbN junctions produced by reactive sputtering in Ar+N2 mixture on 200-mm wafers at 200 oC and incorporated into a planarized process with two Nb ground planes and Nb wiring layer. Here NbN is a stoichiometric nitride with superconducting critical temperature Tc =15 K and NbNx is a high resistivity, nonsuperconducting nitride deposited using a higher nitrogen partial pressure than for the NbN electrodes. For comparison, we co-fabricated Nb/NbNx/Nb JJs using the same NbNx barriers deposited at 20 oC. We varied the NbNx barrier thickness from 5 nm to 20 nm, resulting in the range of Jc from about 1 mA/um^2 down to ~10 uA/um^2, and extracted coherence length of 3 nm and 4 nm in NbNx deposited, respectively at 20 oC and 200 oC. Both types of JJs are well described by resistively and capacitively shunted junction model without any excess current. We found the Jc of NbN/NbNx/NbN JJs to be somewhat lower than of Nb/NbNx/Nb JJs with the same barrier thickness, despite a much higher Tc and energy gap of NbN than of Nb electrodes. IcRn products up to ~ 0.5 mV were obtained for JJs with Jc~ 0.6 mA/um^2. Jc(T) dependences have been measured.

cond-mat.supr-con↗

arXiv2025

XR Blocks: Accelerating Human-centered AI + XR Innovation

We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like JAX and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for perception, rendering, and interaction. To bridge this gap, we present XR Blocks, a cross-platform framework designed to accelerate human-centered AI + XR innovation. XR Blocks strives to provide a modular architecture with plug-and-play components for core abstraction in AI + XR: user, world, peers; interface, context, and agents. Crucially, it is designed with the mission of "reducing frictions from idea to reality", thus accelerating rapid prototyping of AI + XR apps. Built upon accessible technologies (WebXR, three.js, TensorFlow, Gemini), our toolkit lowers the barrier to entry for XR creators. We demonstrate its utility through a set of open-source templates, samples, and advanced demos, empowering the community to quickly move from concept to interactive XR prototype. Site: https://xrblocks.github.io

cs.HC↗

arXiv2025

Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR Agents

Proactive AR agents promise context-aware assistance, but their interactions often rely on explicit voice prompts or responses, which can be disruptive or socially awkward. We introduce Sensible Agent, a framework designed for unobtrusive interaction with these proactive agents. Sensible Agent dynamically adapts both "what" assistance to offer and, crucially, "how" to deliver it, based on real-time multimodal context sensing. Informed by an expert workshop (n=12) and a data annotation study (n=40), the framework leverages egocentric cameras, multimodal sensing, and Large Multimodal Models (LMMs) to infer context and suggest appropriate actions delivered via minimally intrusive interaction modes. We demonstrate our prototype on an XR headset through a user study (n=10) in both AR and VR scenarios. Results indicate that Sensible Agent significantly reduces perceived interaction effort compared to voice-prompted baseline, while maintaining high usability and achieving higher preference.

cs.HC↗

arXiv2025

Single GPU Task Adaptation of Pathology Foundation Models for Whole Slide Image Analysis

Pathology foundation models (PFMs) have emerged as powerful tools for analyzing whole slide images (WSIs). However, adapting these pretrained PFMs for specific clinical tasks presents considerable challenges, primarily due to the availability of only weak (WSI-level) labels for gigapixel images, necessitating multiple instance learning (MIL) paradigm for effective WSI analysis. This paper proposes a novel approach for single-GPU \textbf{T}ask \textbf{A}daptation of \textbf{PFM}s (TAPFM) that uses vision transformer (\vit) attention for MIL aggregation while optimizing both for feature representations and attention weights. The proposed approach maintains separate computational graphs for MIL aggregator and the PFM to create stable training dynamics that align with downstream task objectives during end-to-end adaptation. Evaluated on mutation prediction tasks for bladder cancer and lung adenocarcinoma across institutional and TCGA cohorts, TAPFM consistently outperforms conventional approaches, with H-Optimus-0 (TAPFM) outperforming the benchmarks. TAPFM effectively handles multi-label classification of actionable mutations as well. Thus, TAPFM makes adaptation of powerful pre-trained PFMs practical on standard hardware for various clinical applications.

cs.CV↗

arXiv2025

Zero-Shot Multi-Disease Labeling of Chest, Abdomen, and Pelvis CT Reports Using Open-Weight Large Language Models: The Effect of Labeling Conventions

Purpose: To compare five lightweight open-weight large language models (LLMs) with a rule-based algorithm (RBA) and fine-tuned RadBERT for zero-shot labeling of chest-abdomen-pelvis (CAP) CT reports, and to examine how labeling conventions affect measured performance. Materials and Methods: In this retrospective study, 40,833 CAP CT reports from 29,540 patients examined between 2012 and 2017 were analyzed; age and sex were unavailable. Five LLMs were prompted zero-shot to assign 15 labels across three organ systems and compared with an RBA and fine-tuned RadBERT. Inter-model agreement was assessed with Cohen kappa ($\kappa$) on 12,197 held-out reports. Macro-averaged F1 was computed against 1,789 radiologist-supervised annotations, the same annotations simplified to disregard clinical actionability, and the CT-RATE dataset. Nonoverlapping bootstrapped 95% CIs indicated relevant differences. Results: MedGemma 27B and MedGemma-1.5 4B showed the highest median agreement ($\kappa$ = 0.90). Against manual annotations, Gemma-3 27B achieved the highest macro-averaged F1 (0.82 [95% CI: 0.80, 0.83]) versus 0.66 for RadBERT and 0.64 for the RBA; a majority-vote ensemble scored 0.84. Scores were lowest averaged across models for subjective classes, kidney lesion (0.44) and atelectasis (0.67). Relabeling raised F1 for all models on kidney lesion, but for atelectasis only for the LLMs; the RBA and RadBERT declined. F1 against CT-RATE exceeded that against manual annotations for all models, reflecting its more literal convention. Conclusion: Lightweight open-weight LLMs outperformed rule-based and fine-tuned BERT labeling of CAP CT reports with zero-shot prompting. Models and annotators disagreed largely because they applied different labeling criteria.

cs.CL↗

arXiv2025

Krysalis Hand: A Lightweight, High-Payload, 18-DoF Anthropomorphic End-Effector for Robotic Learning and Dexterous Manipulation

This paper presents the Krysalis Hand, a five-finger robotic end-effector that combines a lightweight design, high payload capacity, and a high number of degrees of freedom (DoF) to enable dexterous manipulation in both industrial and research settings. This design integrates the actuators within the hand while maintaining an anthropomorphic form. Each finger joint features a self-locking mechanism that allows the hand to sustain large external forces without active motor engagement. This approach shifts the payload limitation from the motor strength to the mechanical strength of the hand, allowing the use of smaller, more cost-effective motors. With 18 DoF and weighing only 790 grams, the Krysalis Hand delivers an active squeezing force of 10 N per finger and supports a passive payload capacity exceeding 10 lbs. These characteristics make Krysalis Hand one of the lightest, strongest, and most dexterous robotic end-effectors of its kind. Experimental evaluations validate its ability to perform intricate manipulation tasks and handle heavy payloads, underscoring its potential for industrial applications as well as academic research. All code related to the Krysalis Hand, including control and teleoperation, is available on the project GitHub repository: https://github.com/Soltanilara/Krysalis_Hand

cs.RO↗

arXiv2025

NASA Innovative Advanced Concepts Phase I Final Report -- A Lunar Long-Baseline UV/Optical Imaging Interferometer: Artemis-enabled Stellar Imager (AeSI)

This report presents the findings of a NIAC Phase I feasibility study for the Artemis-enabled Stellar Imager (AeSI), a proposed high-resolution, UV/Optical interferometer designed for deployment on the lunar surface. Its primary science goal is to image the surfaces and interiors of stars with unprecedented detail, revealing new details about their magnetic processes and dynamic evolution and enabling the creation of a truly predictive solar/stellar dynamo model. This capability will transform our understanding of stellar physics and has broad applicability across astrophysics, from resolving the cores of Active Galactic Nuclei (AGN) to studying supernovae, planetary nebulae, and the late stages of stellar evolution. By leveraging the stable vacuum environment of the Moon and the infrastructure being established for the Artemis Program, AeSI presents a compelling case for a lunar-based interferometer. In this study, the AeSI Team, working with the NASA Goddard Space Flight Center's Integrated Design Center (IDC), has firmly established the feasibility of building and operating a reconfigurable, dispersed aperture telescope (i.e., an interferometer) on the lunar surface. The collaboration produced a credible Baseline design featuring 15 primary mirrors arranged in an elliptical array with a 1 km major axis, with the potential to expand to 30 mirrors and larger array sizes through staged deployments. Additionally, this study identified numerous opportunities for optimization and the necessary trade studies to refine the design further. These will be pursued in follow-up investigations, such as a NIAC Phase II study, to advance the concept toward implementation.

astro-ph.IM↗

arXiv2024

Thing2Reality: Transforming 2D Content into Conditioned Multiviews and 3D Gaussian Objects for XR Communication

During remote communication, participants often share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, conventional 2D representations of digital objects restricts users' ability to spatially reference items in a shared immersive environment. To address this, we propose Thing2Reality, an Extended Reality (XR) communication platform that enhances spontaneous discussions of both digital and physical items during remote sessions. With Thing2Reality, users can quickly materialize ideas or physical objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Thing2Reality enables users to interact with remote objects or discuss concepts in a collaborative manner. Our user study revealed that the ability to interact with and manipulate 3D representations of objects significantly enhances the efficiency of discussions, with the potential to augment discussion of 2D artifacts.

cs.HC↗

arXiv2024

XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans

Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of computational phantoms face limitations, particularly in terms of sample size and diversity. Insufficient representation of the population hampers accurate assessment of imaging technologies across different patient groups. Traditionally, the more realistic computational phantoms were created by manual segmentation, which is a laborious and time-consuming task, impeding the expansion of phantom libraries. This study presents a framework for creating realistic computational phantoms using a suite of automatic segmentation models and performing three forms of automated quality control on the segmented organ masks. The result is the release of over 2500 new computational phantoms, so-named XCAT3.0 after the ubiquitous XCAT computational construct. This new formation embodies 140 structures and represents a comprehensive approach to detailed anatomical modeling. The developed computational phantoms are formatted in both voxelized and surface mesh formats. The framework is combined with an in-house CT scanner simulator to produce realistic CT images. The framework has the potential to advance virtual imaging trials, facilitating comprehensive and reliable evaluations of medical imaging technologies. Phantoms may be requested at https://cvit.duke.edu/resources/. Code, model weights, and sample CT images are available at https://xcat-3.github.io/.

eess.IV↗

arXiv2024

Augmented Object Intelligence with XR-Objects

Seamless integration of physical objects as interactive digital entities remains a challenge for spatial computing. This paper explores Augmented Object Intelligence (AOI) in the context of XR, an interaction paradigm that aims to blur the lines between digital and physical by equipping real-world objects with the ability to interact as if they were digital, where every object has the potential to serve as a portal to digital functionalities. Our approach utilizes real-time object segmentation and classification, combined with the power of Multimodal Large Language Models (MLLMs), to facilitate these interactions without the need for object pre-registration. We implement the AOI concept in the form of XR-Objects, an open-source prototype system that provides a platform for users to engage with their physical environment in contextually relevant ways using object-based context menus. This system enables analog objects to not only convey information but also to initiate digital actions, such as querying for details or executing tasks. Our contributions are threefold: (1) we define the AOI concept and detail its advantages over traditional AI assistants, (2) detail the XR-Objects system's open-source design and implementation, and (3) show its versatility through various use cases and a user study.

cs.HC↗

arXiv2024

Human I/O: Towards a Unified Approach to Detecting Situational Impairments

Situationally Induced Impairments and Disabilities (SIIDs) can significantly hinder user experience in contexts such as poor lighting, noise, and multi-tasking. While prior research has introduced algorithms and systems to address these impairments, they predominantly cater to specific tasks or environments and fail to accommodate the diverse and dynamic nature of SIIDs. We introduce Human I/O, a unified approach to detecting a wide range of SIIDs by gauging the availability of human input/output channels. Leveraging egocentric vision, multimodal sensing and reasoning with large language models, Human I/O achieves a 0.22 mean absolute error and a 82% accuracy in availability prediction across 60 in-the-wild egocentric video recordings in 32 different scenarios. Furthermore, while the core focus of our work is on the detection of SIIDs rather than the creation of adaptive user interfaces, we showcase the efficacy of our prototype via a user study with 10 participants. Findings suggest that Human I/O significantly reduces effort and improves user experience in the presence of SIIDs, paving the way for more adaptive and accessible interactive systems in the future.

cs.HC↗