News & Research

arXiv:2605.19170v2 Announce Type: replace-cross Abstract: Diffusion/score-based models have emerged as powerful generative models, capable of generating high-quality samples that mimic the training data distribution. However, it has been observed that they are prone to reproducing training samples-known as "memorization"-potentially violating copyright and privacy. In this paper, we study the effect of Higher-Order Langevin Dynamics (HOLD) on this phenomenon. HOLD diffusion processes introduce auxiliary variables; if the data variable is interpreted as "position," then the auxiliary variables can be interpreted as "velocity" and "acceleration," depending on the chosen order of the model. They were originally proposed based on the intuition that they regularize the trajectories of the data variable by implicitly imposing additional dynamical constraints. Our work provides, to our knowledge, the first theoretical characterization of the regularization effect of HOLD. Specifically, we sho

arXiv:2605.19938v2 Announce Type: replace-cross Abstract: Uniform sampling on implicitly defined manifolds is a core primitive in motion planning, constrained simulation, and probabilistic machine learning. MASEM addresses this problem by entropy-maximizing resampling, but its resampling weights depend on a local k-nearest-neighbour density estimate whose errors can be amplified by aggressive resampling temperatures. We ask whether a polynomial-maximization moment estimator can replace the plug-in density rule without changing the surrounding MASEM architecture. The proposed PMM-MASEM module computes shell spacings from nested k-nearest-neighbour radii, estimates their standardized cumulants, and uses a gated PMM2/PMM3 estimator only when the spacing distribution departs from the flat Exp(1) regime; otherwise it falls back to the plug-in/MLE rule. This fallback is essential: on a flat homogeneous manifold the plug-in estimator is already the MLE, so PMM should not outperform it. A loca

arXiv:2605.20747v2 Announce Type: replace-cross Abstract: Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D). We investigated ten literature reported lncRNAs associated with T2D: MALAT1, MEG3, MIAT, ANRIL, GAS5, KCNQ1OT1, H19, BCYRN1, XIST, and HOTAIR across two independent population-based RNA-seq cohorts. Single-omics approaches provide an incomplete view of disease biology, therefore, an integrative multi-feature framework was developed, extracting expression, secondary-structure, and sequence features for each lncRNA. Eight machine learning (ML) classifiers were evaluated under stratified k-fold, leave-one-out cross-validation (LOOCV), and repeated hold-out schemes to ensure robust performance estimation. SHAP analysis was applied for subject-level association interpretation. In one cohort, GAS5 and XIST expression features, along with GAS5, MEG3, and ANRIL sequence features, were fou

arXiv:2605.24142v1 Announce Type: new Abstract: Metacognitive theories provide foundational frameworks for understanding self-regulated learning, yet they lack systematic integration into comprehensive scenario taxonomies capable of guiding AI-enhanced professional development interventions. Existing models inadequately specify how metacognitive components combine into distinct learning scenarios or how professionals progress from novice to expert functioning. A six-node open systems model, consisting of Environment, Input, Processes, Structures, Output, and Feedback, was developed by synthesizing four major theoretical frameworks. Combinatorial enumeration generated 216 mathematically possible learning scenarios. Four sequential constraint-based filters, including psychological plausibility, educational relevance, measurement feasibility, and intervention potential, informed by empirical workplace learning research, reduced this space to 24 priority scenarios. Five focal scenarios wer

arXiv:2605.24228v1 Announce Type: new Abstract: We investigate sketch-like pen input as an alternative way to support execution control in interactive debugging. In our interface, programmers draw lightweight marks to set breakpoints, use symbolic strokes to control execution, and extend strokes into spirals to repeat traversal actions. The prototype combines gesture recognition with Python execution tracing in a conventional editor interface. In a controlled study with 24 programmers, we compared the sketch interface with conventional mouse-and-keyboard input on debugging tasks that required breakpoint placement, step-wise execution, and runtime state inspection. The results show that sketch-like input can support these execution-control tasks, while also introducing challenges in precision, recognition, and gesture recall. Our findings suggest that pen input is most promising where debugger interactions benefit from spatial grounding or continuous movement, rather than as a wholesale

arXiv:2605.24307v1 Announce Type: new Abstract: Privacy measurement instruments (e.g., CFIP, IUIPC, PAQ) predate GDPR by over a decade and measure privacy concerns, distinct from preferences for regulatory protections (e.g., data portability, erasure, automated decision-making rights). This leaves practitioners without tools to assess whether users value the GDPR mechanisms implemented in compliant policies. We developed a GDPR-grounded privacy preference measurement item bank by extracting 669 statements from all 99 GDPR articles, validated by: (1) two-round expert review achieving full consensus on accuracy, (2) semantic clustering into 10 parent themes and 87 subthemes, and (3) consensus review with 50 privacy experts (5 per theme) using a larger or equal than 4/5 vote retention threshold. The final 527-item bank comprises 9 parent themes and 73 subthemes (18 to 112 items per parent theme, 1 to 29 per subtheme), enabling targeted measurement across granularities while covering GDPR

arXiv:2605.24332v1 Announce Type: new Abstract: Current generative AI systems are increasingly effective at processing explicit knowledge, including retrieving information, summarising documents, generating explanations, and supporting codified workflows. However, high-level expertise also depends on tacit sensing: perceiving weak signals, recognising emerging tensions, detecting coherence degradation, and anticipating instability before formal indicators appear. Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition. This vision paper argues that next-generation AI systems should move beyond explicit knowledge processing toward the longitudinal modelling of expert tacit sensing. It introduces Tacit Signal Infrastructure as a layer for capturing, structuring, modelling, interpreting, and validating expert tacit signals over tim

arXiv:2605.24337v1 Announce Type: new Abstract: Voice is a central element of identity. We recognize people by their voice, and we uniquely express who we are with it. For people who rely on augmentative and alternative communication~(AAC) systems, such as speech-generating devices~(SGD), the device's voice becomes an identity marker others associate with them. Yet, it is hard to find a voice that truly aligns with one's identity both linguistically and culturally. Although modern AI-generated voices can reproduce diverse accents and speaking styles, AAC users still lack accessible ways to articulate how they want an identity-aligned voice to sound like. We first conducted a survey of AAC users (across eight countries) to characterize current voice representation, finding that non-binary, transgender, and non-US-born respondents rated their current voice support identity alignment consistently lower than other respondents. To examine how AAC users respond to voices designed to reflect

arXiv:2605.24729v1 Announce Type: new Abstract: While human-AI collaboration systems have increasingly been built to increase efficiency or support creativity, little work has examined how the design of interactions shapes the social connection between human and artificial agent. We examine how the temporal and visual dimensions of collaboration shape the experience of a writing task. Specifically, we built three variants of an AI-assisted text editor along a spectrum of simulated humanlike interaction (synchronous and with a cursor) to machinelike interaction (asynchronous and without a cursor), and conducted a comparative user study (n=48). Our exploratory findings suggest that synchronous suggestions increased efficiency but led to contextual misalignment, while a visual cursor increased intent understanding but evoked feelings of surveillance. Taken together, humanlike design of artificial agents can create positive social expectations but also elicit social costs, especially witho

arXiv:2605.24830v1 Announce Type: new Abstract: As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck. Generative UI emerges as the necessary new interface layer, dynamically synthesizing the right controls, options, and state from the interaction context in real time. We present Macaron-A2UI, a model for Generative UI in personal agents. Our goal is to move beyond text-only interaction by enabling agents to generate natural language together with lightweight, executable UI actions for information collection, preference refinement, confirmation, and multi-goal organization. We build a large-scale Generative UI corpus from heterogeneous dialogue sources, introduce A2UI-Bench for controlled evaluation, and train 30B, 235B and 754B models with parameter-efficient LoRA-based supervised fine-tuning followed by reward-driven reinforcement learning. The best Macaron-A2UI model reaches 75.6 overall on A2UI-Bench without explici

arXiv:2605.25260v1 Announce Type: new Abstract: This paper offers a concept of working relations as a complement and extension to existing theories of maintenance, care and repair. Building on the cases of an umbrella, a tractor and a pond, it advances seven propositions that might guide and inform further work and thinking in this space. It concludes with the challenging figures of Chernobyl, nickel extraction, and AI, and argues for the centrality of working relations to more generative and pluralistic relations with the things and worlds around us.

arXiv:2605.25296v1 Announce Type: new Abstract: Large Language Models (LLMs) have become increasingly popular for coding tasks, with subjective coding preferences being an essential element to adapt to programmers' personal needs. Existing work overlooks such characteristics and mainly focuses on code correctness. In this study, we propose a typification of four subjective coding preference axes - complexity, commenting, modularity, and readability - motivated by common engineering habits and validated by 25 software engineers. We collect a dataset of ~3,000 paired Python code snippets reflecting these axes, annotated by 73 experts who rate their preferences on a Likert scale. Using our dataset, we study how LLMs handle subjective coding preferences. We present 13 LLMs with pairs of solutions to the same programming task, first as textual descriptions and then as concrete code snippets. We find that models often prefer one option in natural language but the opposite when evaluating cod

arXiv:2605.24313v1 Announce Type: cross Abstract: Current high-performing intracortical speech neuroprostheses achieve low word error rates but typically rely on external language models during inference, increasing memory, computation, and latency. In this work, we investigate whether meaningful character-level decoding is achievable without such models. We propose an end-to-end Conformer-based neural decoder trained directly on intracortical recordings from a participant with amyotrophic lateral sclerosis (ALS). Without any external language model, the system achieves a character error rate (CER) of 23.80\% on held-out validation data. Analysis shows that performance variability is driven by inter-session signal degradation, while dominant errors arise from incorrect word boundary segmentation. These results demonstrate that effective character-level decoding is possible in a fully end-to-end framework, providing a strong neural signal for downstream linguistic processing.

arXiv:2605.24551v1 Announce Type: cross Abstract: Cybersecurity awareness training has historically adopted a one-size-fits-all approach, despite established individual differences in how users process and retain security information. Personality has been proposed as one axis along which training content might be tailored; yet no prior study has implemented and empirically evaluated a complete personality-conditional system end-to-end. This paper reports the design, implementation, and quasi-experimental evaluation of \emph{TailoredSec}, a mobile cybersecurity awareness application that routes training content based on a user's dominant Five-Factor Model (FFM) personality trait, as measured by the ten-item Big Five Inventory (BFI-10). Seventy-four UK-based adults were allocated to a traditional video-training condition ($n = 40$) or a personality-conditional condition ($n = 34$). Both groups completed a four-item scenario-based pre-assessment (scored 0--40), a single training session,

arXiv:2601.05822v2 Announce Type: replace Abstract: The Fast Healthcare Interoperability Resources (FHIR) standard has emerged as a widely adopted specification for exchanging structured clinical data across healthcare systems. However, raw FHIR resources are often complex, verbose, and difficult for clinicians and analysts to interpret without specialized tooling. This paper presents a lightweight, browser-based system that improves the accessibility of FHIR data by automatically transforming raw JSON resources into human-readable PDF and Excel reports, along with interactive data visualizations. The system supports both remote retrieval of FHIR resources from server endpoints and the upload of local FHIR JSON files, enabling both online and offline analysis. Using a modular React architecture with jsPDF, xlsx, and Recharts, the tool parses, normalizes, visualizes, and exports FHIR data in an intuitive format. Evaluation results demonstrate that the system enhances interpretability an