Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2608.02615v1 Announce Type: new Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata. However, most medical large language model (LLM) and vision-language model (VLM) benchmarks focus on isolated modalities or narrow image-text tasks, leaving patient-level oncology assessment across multiple evidence streams largely untested. We introduce OncoTriad-QA, a patient-level radiology-pathology-genomics benchmark for pan-cancer question answering. OncoTriad-QA contains 86.1k semantic questions across 9,281 TCGA patient cases from 32 cancer cohorts, aligning CT/MRI radiology, whole-slide histopathology, somatic mutations, copy-number alterations, DNA methylation, bulk RNA-seq, and clinical metadata. Case-specific annotations are constructed through a source-grounded LLM-assisted pipeline using curated labels, diagnostic reports, molecular profiles, and modality-derived evidence as primary sou
arXiv:2608.02613v1 Announce Type: new Abstract: Edge-deployed personal memory assistants must handle private interpersonal conversations on-device with open-weight models. Yet, existing memory benchmarks often under-test the combination of activity-dense interaction, ego-centric perspective, and coherent multi-session worlds. MemArena fills these gaps with a single-world conversational benchmark built with its MASim agent simulator, for 50 agents over 15 days (10.3M dialog-text tokens, 24.1K text-only ego-observed tokens/agent/day). With the interaction history, it co-generates ground truth over six recall, reasoning, and trustworthiness evaluation dimensions. We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends. Three results stand out: (1) Memory-backend choice matters more for content accuracy: At Qwen3-0.6B, Memobase-to-MemSearch gains +32.5/+19.2 pp, exceeding MemSearch reader scaling (+10.6/+6.8 pp). (2)
arXiv:2608.02612v1 Announce Type: new Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions. Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable. In BBO, the search space design, a part of the problem formulation, and the selection of the optimization algorithm are crucial for problem-solving. Automating these processes with large language models (LLMs) is a significant challenge. This paper introduces Black-Box Optimization Word Problems (BBOWP), a novel problem setting in which a system must infer bo
arXiv:2608.02609v1 Announce Type: new Abstract: Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed. Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants. We present TabletCraft, the first open-source system that enables bidirectional interaction with Mesopotamian writing. Users can read ancient tablets (Akkadian to English) and compose new messages as cuneiform clay tablets (English to Akkadian to cuneiform to rendered tablet). The system integrates a ByT5-based translation model trained on 116K bidirectional samples, a cuneiform sign converter with 14,240 mappings (95.3% cov
arXiv:2608.00868v2 Announce Type: replace-cross Abstract: Many open educational resources are lacking in accessibility, especially in-depth image descriptions. In subjects like Science and Mathematics, however, it can be particularly difficult to write image descriptions since there can be many complicated expressions and names depending upon the course level. To help fill that gap in a small way, we introduce Math Image Descriptions for Accessible Learning (MIDAL), a math image-description dataset of 2,020 mathematical images spanning multiple educational levels, to aid in training vision language models to create image descriptions following accessibility best practices. We hope MIDAL is a valuable resource in enhancing the conversation and innovation regarding accessibility of STEM content in higher education. This dataset is however not just limited in math description generation but can also be used to fine-tune language models that can have improved mathematical reasoning and ans
arXiv:2603.20381v2 Announce Type: replace-cross Abstract: Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, thoughtful, engaging, and empowering human-agent interactions. If meaning is constituted rather than retrieved, then the search for context-independent features or circuits in the pursuit of mechanistic interpretability may be fundamentally limited. Experiments in cognitive science and social psychology have demonstrated that human semantic processing exhibits contextuality more consistent with quantum logical mechanisms than classical Boolean theories, and recent works have found similar results in large language models---in particular, clear violations of the Bell inequality in experiments of contextuality during interpretation of ambiguous expressions. In this work, we explore the CHSH $|S|$ parameter---the metric associated with the inequality---across the inference parameter spac
arXiv:2502.11140v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions. Despite improvements from techniques like few-shot prompting and query expansion, existing methods often struggle when requests are underspecified in actionable details (e.g., data preprocessing assumptions, solver or library choices, etc.), frequently necessitating manual intervention. To overcome these limitations, we propose VisPath: a Multi-Path Reasoning and Feedback-Driven Optimization Framework for Visualization Code Generation. VisPath handles underspecified queries through structured, multi-stage processing. It begins by using Chain-of-Thought (CoT) prompting to reformulate the initial user input, generating multiple extended queries in parallel to surface alternative plausible concretizations of the request. These queries then generate candidate visua
arXiv:2605.00240v2 Announce Type: replace Abstract: Thermal referral enables thermal sensations in locations lacking thermal actuators--this is achieved using vibrotactile actuators to redirect a nearby thermal sensation to where a tactile sensation is applied. However, we found that its reliance on vibration introduces critical limitations: it struggles to produce cold referral, and the inherent strong tactile "buzz" makes it unsuitable for simulating non-contact thermal events, such as the chill of an open freezer in VR (in contrast to contact-based thermal events like touching the freezer's cold handle). To improve this, we propose a shift from vibrotactile to electrotactile-based thermal referral. We evaluated in two user studies--a psychophysics experiment (N=22) and a VR deployment (N=20)--where we contrasted electrotactile with vibrotactile-based thermal referral. Our results reveal key advantages of the electrotactile based thermal referral: (1) increases the referral rate for
arXiv:2507.20720v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) are beginning to enable new user experiences from generated content across a range of media, including images, text, speech, and video. These capabilities have the potential to enrich learning by enabling users to interact with information using a variety of modalities, but little is known about how \textit{educators} envision how MLLMs might shape the future of learning, what challenges they encounter when using these models, and what practical needs should be considered for future implementation in educational contexts. We investigated educator perspectives through workshops with 12 K-12 educators, where participants brainstormed learning opportunities, discussed practical concerns, and prototyped MLLM learning applications using Claude 3.5 and its Artifacts feature. Through this work, we uncover how educators imagined MLLMs as a way for themselves and their students to author multimedia cont
arXiv:2608.03464v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have made significant progress in chart understanding, generation, and editing, but their ability to annotate existing charts remains underexplored. Annotating charts is a common yet challenging communicative task, requiring models to infer intended messages, interpret chart semantics, and place appropriate textual or graphical elements. To address this gap, we introduce ChartAnno, a benchmark for evaluating MLLMs on chart annotation generation. It contains 1,200 real-world charts with paired code and annotation instructions across three levels of instruction specificity. We evaluate 10 representative MLLMs under two primary input settings: (1) chart code alone and (2) both chart code and chart image, and further include a chart image-only ablation study. Results show that proprietary models remain stronger overall, although large-scale open-source models narrow the gap. More specific instruction
arXiv:2608.03462v1 Announce Type: cross Abstract: Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specializ
arXiv:2608.03176v1 Announce Type: cross Abstract: Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remains difficult because scalp EEG is noisy and internally generated stroke sequences vary across individuals. The Multimodal Brain-Computer Interface Grand Challenge provides synchronized EEG and fNIRS for four-class subject-independent handwriting-trajectory classification. We propose FRED, a task-adapted system that models imagined handwriting as a multi-second motor sequence and trains a compact multi-scale temporal network on three complementary EEG frequency views. With three seeds per view, cross-band members produce substantially less-correlated errors than same-band replicas, yielding a clean nine-member ensemble accuracy of 0.8076/0.7242/0.7492 on the public/private/overall test partitions without test-set adaptation or output constraints. The submitted pipeline further incorporates tran
arXiv:2608.03151v1 Announce Type: cross Abstract: Contactless keystroke inference via WiFi sensing highlights severe privacy threats, yet its real-world feasibility is hindered by two fundamental physical and deployment bottlenecks: the strict requirement for network privileges to acquire stable sensing streams, and the inherent "waveform fusion" ambiguity of pure WiFi signals during rapid, muscle-memory typing. To overcome these limitations, we propose AirKey, a novel cross-modal sensing framework that achieves highly stealthy, zero-training PIN eavesdropping. First, to bypass network deployment barriers, AirKey exploits fundamental IEEE 802.11 mechanisms to predictably elicit Acknowledgment (ACK) responses from unmodified target devices. By passively harvesting Channel State Information (CSI) from these ACKs using a low-cost microcontroller, AirKey secures a continuous spatial sensing stream entirely without network association. Crucially, to resolve the WiFi waveform fusion bottlene
arXiv:2608.03039v1 Announce Type: cross Abstract: Self-Sovereign Identity (SSI) promises to decentralize digital identity, but widespread adoption remains hindered by integration complexity and tooling immaturity. This paper investigates the Developer Experience (DX) of open-source SSI tooling through an exploratory probe study. Nine developers with prior knowledge of decentralized identity concepts, representing early integrators building SSI applications, attempted core credential lifecycle tasks using Walt.id, Traction, and MetaMask. Our goal was to surface recurring integration barriers through qualitative thematic analysis of open-ended developer reports, complemented by task-level difficulty ratings. Our findings reveal a critical abstraction gap: while passive operations like credential receipt are relatively mature, active construction tasks, particularly schema customization, expose significant architectural friction. We identify that these barriers stem from inadequate API ab
arXiv:2608.02780v1 Announce Type: cross Abstract: In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic
arXiv:2608.00289v1 Announce Type: cross Abstract: Recognizing Human-Object Interactions (HOI) is essential for intelligent systems, underpinning applications in virtual and augmented reality, embodied AI, and assistive robotics. However, vision-based HOI methods face challenges in privacy concerns and poor light conditions. In this work, we introduce RF-HOI, the first framework that only uses radio frequency (RF) signals for HOI recognition. A key challenge of RF-HOI is that single-modality RF sensing is insufficient to recognize both actions and the objects being interacted with. RF-HOI addresses this through a novel modality fusion that combines mmWave radar and RFID, enabling simultaneous action recognition and target identification. Another challenge is limited training data across diverse setups, which impairs the generalizability of the recognition model. To overcome this, we develop a simulator that synthesizes multimodal RF data for diverse HOIs at scale, allowing us to fine-tu
arXiv:2608.04006v1 Announce Type: new Abstract: LLMs are reshaping educational technology, yet evaluating their responses for pedagogical alignment remains underexplored, relying heavily on the expertise of learning engineers building the technology. To bridge this gap, we explore trustworthiness as a structured lens for evaluation, leveraging existing measures of LLM trustworthiness to systematically identify potential pedagogical disruptions. Through a longitudinal co-design process with learning engineers developing an LLM-powered digital textbook, we: (1) co-constructed five trustworthiness metrics comprising 20 measures tailored to pedagogical use; (2) designed visualizations that map trustworthiness violations onto LLM responses; and (3) evaluated how these tools help learning engineers make A/B comparisons of LLM responses. Making trustworthiness explicit increased inter-rater reliability while helping learning engineers resolve conflicting objectives and produce more consistent
arXiv:2608.04002v1 Announce Type: new Abstract: We present Semantic Bundling, a visual analytics technique for making sense of text documents represented as knowledge graphs (KGs). Representing a document corpus as a KG makes relationships between entities explicit, making KGs useful both to analyze directly and in computational workflows including ML pipelines and generative AI backends. However, as KGs grow they become difficult to interpret and visualize for specific tasks (e.g., the ``hairball problem''), with the meaning of each relationship often buried in dense source text. Semantic Bundling uses large language models (LLMs) to support user-driven bundling of nodes and edges in a KG into higher-level graph structures: super nodes, which collapse and summarize a region of the graph, and super edges, which summarize the connection between two entities. Results are linked to underlying triples and source documents, grounding summaries in evidence. We implement Semantic Bundling in
arXiv:2608.03804v1 Announce Type: new Abstract: Geospatial foundation models (GeoFMs) offer transformative potential for environmental monitoring, yet adoption among ecologists is uneven. Most evaluations are model-centric, focusing on architecture and benchmark accuracy, which overlooks whether the systems are usable by their intended audiences. To address this gap, we first conducted a pilot expert elicitation survey with ecology and conservation scientists that helped us identify misalignments between current GeoFM development priorities and their needs. Informed by these findings and based on HCI theory, we created a seven-dimension evaluation covering Access & Deployment, Interaction & Customization, Trust & Transparency, Community & Support, Scientific Permanence, Multilingual Support, and Offline Usability. Then, two raters applied this rubric to 89 GeoFMs. We found distinct accessibility gaps where nearly a third provide no support to practitioners beyond their source code. Dim
arXiv:2608.03533v1 Announce Type: new Abstract: Dynamic consent can promise end users with greater control, but little is known about how older adults with chronic conditions navigate the tradeoff between control and burden in granular consent mechanisms in health data life-cycles. Using a two-stage design process we evaluated this tradeoff. An expert workshop (n=5) informed the design requirements for granular dynamic consent prototype. We evaluated single step vs multi-step granularity in dynamic consent using prototypes with cardiac patients (n=7) using a mixed-methods study. Quantitative measures showed no significant differences between low- and high-granularity consent screens in usability, workload, perceived information control or willingness to share data. However, qualitative findings revealed a control-burden paradox and trust-dependent engagement with granularity. Participants sought greater transparency and control over AI-mediated data processing. We contribute implicatio
arXiv:2608.03433v1 Announce Type: new Abstract: Sonic seasoning research has shown that listeners attribute systematic gustatory and emotional meaning to sound, and text-to-music generative artificial intelligence has recently been used to render gustatory prompts as musical stimuli. Whether the taste-sound correspondences acquired by such models hold beyond the cultural context in which they were validated remains untested. We extended a single-country study to a three-country online experiment conducted in Argentina, Italy, and Japan (N = 361). Participants first indicated their preference between base and fine-tuned MusicGen excerpts generated from four taste prompts (sweet, sour, bitter, salty), and then rated fine-tuned excerpts on twelve taste, emotion, and thermal descriptors. Preference for the fine-tuned model was confirmed in Argentina and Italy but not in Japan, and the salty prompt yielded the weakest correspondence in all three cohorts. Ratings differed substantially betwe
arXiv:2608.03251v1 Announce Type: new Abstract: Commercial wearable devices continuously capture rich physiological data (e.g., heart rate, respiration), opening new possibilities for monitoring health conditions, notably around stress. Despite their promise, turning raw wearable physiological data streams into visualizations that surface stress-related insights in daily activities, and that ultimately foster reflection, awareness, and better stress management, remains a significant challenge. The data are noisy and context-dependent: the same spike in heart rate can come from sprinting, a tense presentation, or laughing with friends. To address these challenges, we propose a framework that combines user annotations with wearable data to support better stress management. We introduce a web framework offering interactive visualizations that layer daily activities, stress events, and interventions onto raw physiological streams, enabling users to reflect and identify trends. In a four-we
arXiv:2608.03168v1 Announce Type: new Abstract: The optimal sensor position on a computer mouse can improve pointing performance, but existing calibration methods require time-consuming repetitions of pointing tasks. In this paper, we propose a novel calibration approach that dramatically reduces the time and effort required to determine a user's optimal mouse sensor position. Our method simulates cursor trajectories for different sensor positions using a dual-sensor mouse, eliminating the need for repetitive measurements with multiple sensor placements. By analyzing the straightness of the simulated paths, quantified by the mean absolute error (MAE) relative to an ideal straight-line path, we estimate the sensor position that would yield the most efficient pointing motion for the user. Our preliminary results indicate that the proposed simulation-based calibration method could reduce the calibration time from an hour to just five minutes, while providing a better identification of the
arXiv:2608.03152v1 Announce Type: new Abstract: Collecting richly labeled wearable activity data in everyday settings remains difficult because retrospective annotation is costly and often imprecise. Prior data collection apps rely on a labor-intensive self-reporting strategy and primarily treat participants as crowd labelers. We present Pebbl, a feasibility-stage system that incentivizes in-situ labeling through opportunistic crowdsensing. Pebbl lets users author trigger-action recipes on a smartphone and receive just-in-time reminders for beneficial actions when a trigger is detected. In the prototype, triggers are a limited set with four common audio cues, while actions are described in open-vocabulary natural language. Each confirmed execution yields a short sensor window with explicit start/end boundaries and a user-authored action label. We evaluate Pebbl through an expert workshop with wearable Human Activity Recognition (HAR) researchers (N = 6), a within-subject in-lab study (
arXiv:2608.03132v1 Announce Type: new Abstract: Immersive Computational Notebooks (ICoN) extend traditional notebook environments into immersive spaces, enabling analysts to interact with multimodal artifacts, including code, narratives, data tables, and visualizations. By integrating multimodal artifacts into a single immersive workspace, ICoN enables analysts to transition between analytical tasks seamlessly. Meanwhile, understanding organizational strategies is critical for designing effective interactions to further support analysts. However, prior research on immersive computational notebooks has primarily examined organizational strategies centered on single-modality artifacts. Systematic investigations of how analysts spatially organize the complex relationships among multimodal artifacts in a single immersive workspace remain underexplored. To address this gap, we conducted a user study to examine organizational strategies for multimodal artifacts in immersive computational not
arXiv:2608.02784v1 Announce Type: new Abstract: Across cohorts of older adults and generations of technology, (dis)trust has continued to shape the acceptance of technologies for aging. Yet, we still know little about if or how to design for older adults' distrust in technology. Towards this, we reviewed empirical studies in which older adults discussed trust and distrust in relation to technology. Our analysis shows that (dis)trust is constituted in three key ways: emerges when material arrangements of digital systems become illegible, such as when older adults cannot determine where information is stored or trace its movement; is temporally constituted and fluid, building and shifting through prior experience and breakdowns, and at times distrust can operate as boundary work through which individuals limit technology's reach into their autonomy, relationships, and everyday lives. Our work yields design directions for supporting trust across different phases of technology use and by d
arXiv:2608.02749v1 Announce Type: new Abstract: We introduce Sensus Pond, an interactive system that reconfigures water not merely as a static natural element but as an active sensing medium for registering more-than-human traces. In response to the limitations of anthropocentric approaches in interaction design, we propose a methodological framework of observation without translation, resisting the tendency to stabilize, decode, or humanize nonhuman presence. Drawing from critical theories of more-than-human design and ecological entanglement, Sensus Pond is a materially embedded and site-specific system that employs Swept Frequency Capacitive Sensing and an Artificial Neural Network to detect ephemeral interactions between the pond's surface and surrounding life forms. Rather than classifying or interpreting these events, the system visualizes temporal accumulations of overlapping traces, producing a layered archive of spatial and temporal entanglements. This approach emphasizes attu
arXiv:2605.20956v2 Announce Type: replace-cross Abstract: Conformal triage converts predictive scores into deployment actions that either release a case, flag it for urgent attention, or defer it to human review. Under an observed change in target-event prevalence, however, marginal coverage and human-review rate can miss whether patients who experience the target event are released without review. To address this gap, we introduce a leakage-aware deployment audit for release-side conformal triage. It first assigns target subjects to three non-overlapping roles: prevalence correction, conformal calibration, and held-out release-side evaluation. This separation then lets the audit evaluate release directly: how many event-positive patients are cleared without review, whether the pilot has enough event labels for calibration, and how the release-review trade-off shifts. Applying this audit to a retrospective non-small-cell lung cancer (NSCLC) target cohort shows why lower review can be m
arXiv:2512.20775v3 Announce Type: replace-cross Abstract: In this paper, we introduce Sark, a reference architecture for transferring unforgeable, stateful, oblivious (USO) assets. We describe the motivation, design, and implementation of the core subsystems of Sark, Porters, which accumulate and roll-up commitments from Clients, and Sloop, a permissioned, crash fault-tolerant (CFT) blockchain system. We analyse the operation of the system using STRIDE threat analysis, and the `CIA Triad': Confidentiality, Availability, and Integrity. We then introduce the concept of \textit{local centrality} and use it to address design trade-offs related to decentralization. Finally, we point to future work on Byzantine fault-tolerance (BFT), and mitigating the local centrality of Porters.
arXiv:2408.02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations. Our framework employs a multi-agent artificial intelligence approach, utilizing various machine learning techniques including large language models, to analyze complex, longitudinal datasets. The model aims to optimize multiple patient outcomes simultaneously while addressing biases and ensuring generalizability. We demonstrate how this framework can be implemented to create a learning healthcare system that continuously refines strategies for optimal patient care. This approach has the potential to significantly improve the translation of digital health innovations into real-world clinical benefits, addr
arXiv:2605.09680v2 Announce Type: replace Abstract: Workplace norms in computer science have received growing attention due to a series of recent ethical scandals. One response has been a push to improve the ethics education provided to computer science students. Evidence for the effectiveness of ethics education remains mixed; some evidence suggests that norms are changing, others point to persistent gaps between stated values and practice remain. In this paper, we explore whether students, who have received some contemporary CS ethics education, are able to effectively apply ethical reasoning to their own decision-making in what is typically the first significant ethical decision of their careers: the job search. Our study examines the ethical decision making of 129 computer science students and recent graduates during their job searches. We find that most students prioritize factors like compensation, location, and workplace culture over ethical and social issues. Even when expressi
arXiv:2602.12913v2 Announce Type: replace Abstract: Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) the scalability bottleneck raised by the exponential decision variables in large-scale fleets. To address these challenges, we propose HRL4AG, a Hierarchical Reinforcement Learning framework for cooperative Air-Ground delivery. Specifically, HRL4AG employs a high-level manager to tackle the scalability bottleneck by decomposing the joint action space, and mode-specific workers that encode distinct flight and road dynamics to address the heterogeneity. Furthermore, a novel internal reward mechanism is designed to guide the hierarchical policy learning, addressing the credit assignment problem in sparse-reward settings. Extensive experiments
arXiv:2602.02882v2 Announce Type: replace Abstract: Large language models are increasingly used to predict human preferences in both scientific and business endeavors, yet current approaches rely exclusively on analyzing model outputs without considering the underlying mechanisms. Using election forecasting as a test case, we introduce mechanistic forecasting, a method that demonstrates that probing internal model representations offers a fundamentally different - and sometimes more effective - approach to preference prediction. Examining over 24 million configurations across 7 models, 6 national elections, multiple persona attributes, and prompt variations, we systematically analyze how demographic and ideological information activates latent party-encoding components within the respective models. We find that leveraging this internal knowledge via mechanistic forecasting (opposed to solely relying on surface-level predictions) can improve prediction accuracy. The effects vary across
arXiv:2601.17003v2 Announce Type: replace Abstract: Mental-health AI safety is typically evaluated with small, simulation-based benchmarks that may not reflect the linguistic and contextual diversity of deployment. We pair four benchmark replications with an ecological audit of real-world conversations to evaluate a purpose-built mental-health AI alongside six frontier general-purpose models spanning four families (OpenAI GPT-5, GPT-5.1, GPT-5.2; DeepSeek V3; Google Gemini 3 Flash; Moonshot Kimi K2). The purpose-built system produced significantly lower overall potentially harmful content rates than every frontier comparator on suicide/self-harm, eating-disorder, and substance-use prompts (CCDH Benchmark: Ash 6.2% vs frontier models 18.0-52.0%, all p < .001). In an audit of 20,000 deployment conversations, clinician review within the audit pipeline confirmed no suicide-risk conversations lacking crisis resources and three NSSI-related conversations without crisis intervention, a within
arXiv:2608.03811v1 Announce Type: cross Abstract: We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed student-item interactions with internal memory to produce evolving latent representations of student knowledge. These representations support accurate predictions of future responses while enabling explicit control over the smoothness of estimated learning trajectories. UNVaMP can be configured as either a purely neural model or a hybrid model that predicts responses through an interpretable measurement function over the latent space. We show that a pure neural configuration (UNVaMP-MLP) achieves the strongest predictive performance among compared models on three out of four datasets. Meanwhile, a hybrid configuration (UNVaMP-MIRT, using a 1PL MIRT measurement function) lags only slightly behind UNVaMP-MLP, indicating that the predictive cost of interpretability is modest. Beyond predictive ac
arXiv:2608.03700v1 Announce Type: cross Abstract: Persona skills distill personal interaction histories into portable and executable artifacts for downstream agents. While enabling flexible personalization, this process concentrates fragmented personal signals, amplifies their impact through reuse, and challenges defenses designed for individual records or retrieval-based memory. To systematically investigate the safety of the persona-skill pipeline, we introduce AntiSkillBench, an end-to-end benchmark for evaluating risks and defenses across the persona-skill pipeline. It comprises: (i) a dataset of 7,500 persona-grounded dialogue traces, constructed from 50 behaviorally rich profiles spanning diverse task scenarios; (ii) an evaluation suite that measures skill-level privacy leakage and agent-level attribute disclosure and behavioral impersonation across three skill-distillation strategies; and (iii) a defense evaluation covering four configurations across online and post-hoc interven
arXiv:2608.03626v1 Announce Type: cross Abstract: Large language models are being integrated into critical infrastructure and enterprise workflows at unprecedented scale,yet the lifecycle frameworks governing their development and operations were designed for operational efficiency rather than security analysis. As a result, security-relevant activities such as data provenance verification, artifact signing, agentic permission control, and decommissioning are often left implicit or assumed to receive due care. Governance frameworks, in turn, organise requirements around risk levels or management processes without clearly linking them to the lifecycle stages where they apply. This paper addresses both deficiencies. We propose a lifecycle model for LLM systems that supports security analysis by structuring it around security-relevant boundaries rather than workflow optimisation. The model comprises 32 stages across four core pipeline layers (Data, Model, Distribution, Application), suppo
arXiv:2608.03585v1 Announce Type: cross Abstract: Open-source software communities are a form of digital public infrastructure that not only produces code, but also generates public knowledge and interpersonal relationships through visible collaboration. Generative coding agents (CAs) are an advanced tool to improve development efficiency while shifting part of activities from public human interaction to private human-agent loops. We study this shift using an LLM-based multi-agent simulation initialized with real GitHub data from 1,084 active developers and their repository relationships. After a warm-up with historical commits, we branch the same community state into parallel No-CA and CA conditions for 4-week simulations. CA introduction increases planned and completed tasks by 34.0% and 39.0%, respectively, and reduces median completion time from 45 to 20 minutes. However, adoption reaches only 26.0%, and the gains concentrate among developers who are already more active and well co
arXiv:2608.03569v1 Announce Type: cross Abstract: Benchmarking the ability of AI scientists to generate novel ideas is notoriously difficult. Existing benchmarks in this field have made progress in evaluating scientific reasoning and research replication, but often rely on synthetic tasks or retrospective targets, which may be confounded by prior exposure. We hypothesize that complex, adversarial, fast-moving real-world domains where expert practitioners independently generate observable outputs can provide a practical solution to fill this gap and evaluate the capabilities needed for AI scientists, including reasoning, novelty, and hypothesis formulation. We instantiate this framework in two structurally different domains, Formula 1 (F1), where models ideate around car design concepts for the 2026 season, and real pre-season innovations provide a ground truth, and Magic: The Gathering (MTG), where models propose decks from a recently updated card pool and are evaluated against 19 Pro
arXiv:2608.03531v1 Announce Type: cross Abstract: Institutions increasingly rely on browser lockdown, webcam monitoring, and behavioral analytics to secure high-stakes digital assessments, yet these mechanisms are commonly designed and evaluated independently and often overlook learner accessibility. This paper introduces Behaviorally-Adaptive Visual Diversion (BAVD), a theoretical framework in which a synthetic, non-semantic visual field is composited with assessment content and adaptively modulated according to observed candidate behavior. The underlying assessment content is never altered; only its visual presentation is modified to reduce the usefulness of unauthorized screen capture or screen sharing while remaining minimally intrusive for legitimate candidates. The framework further incorporates an accessibility-aware attenuation mechanism that reduces or suppresses diversion intensity for candidates with approved visual-processing accommodations. We formulate the model using a c
arXiv:2608.03114v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into medical decision-making, yet its liability implications remain complex, particularly when physicians differ in diagnostic skills and their quality is unobservable. This paper develops a principal-agent model in which a social planner designs medical liability to regulate a physician with private quality information who chooses between a standard treatment, a personalized judgment-based treatment, or following an imperfect AI recommendation. Our analysis yields several novel insights. First, we show that the optimal mechanism under asymmetric information is surprisingly simple: a uniform, one-size-fits-all liability level for all physician types who deviate from the standard of care. Despite physician heterogeneity, this simple policy often achieves the full-information first-best outcome, particularly when standard care is reliable or AI is highly accurate. Second, the relatio
arXiv:2608.03017v1 Announce Type: cross Abstract: There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes. In this study, we explore the use of survival prediction models trained on deceased donor kidney transplant data from the Scientific Registry of Transplant Recipients (SRTR). We propose a novel paired recipient-based evaluation framework that compares graft outcomes between two recipients who received kidneys from the same deceased donor, allowing us to evaluate the counterfactual benefit of changing the recipient for a certain donor. We find that five different survival prediction models, ranging in complexity from linear to deep learning-based models, all result in ~60% paired reci
arXiv:2608.02955v1 Announce Type: cross Abstract: This research paper describes an exploratory study on the effectiveness of Chat Debugging: troubleshooting malfunctioning analog circuits on breadboards and printed circuit boards (PCB) by undergraduates through conversations with public-domain large language models (LLMs). Through thematic analysis of students' voluntarily shared chat logs when debugging pre-determined buggy circuits under exam and time pressure, we discovered multimodal usage patterns by students and considerable domain knowledge and sensible debugging suggestions offered by off-the-shelf LLMs. Meanwhile, we also identified major gaps in LLM technologies and students' skills during human-AI collaborative debugging, such as LLMs' limitations in 2D/3D image-based reasoning, unjustified tone of confidence, and students' deficits in fundamental concepts and critical thinking.
arXiv:2608.02939v1 Announce Type: cross Abstract: Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer. We evaluated federated training of tokenized generative event models (GEMs) across 122,251 intensive care hospitalizations from three independent health systems harmonized to the Common Longitudinal ICU Data Format. Models were assessed on 12 post-24-hour clinical prediction tasks using within-site, cross-site, centralized, and federated training configurations. GEMs achieved the highest mean within-site and cross-site ROC-AUC and were substantially more transportable than conventional supervised models: their average cross-site penalties were 0.025 ROC-AUC and 0.027 PR-AUC, compared with 0.079 and 0.089 for LightGBM. Federated Learning (FedAvg and FedAvgM) approached the performance of centralized GEM training, with most gains obtained within 5-10 communication rounds. However, central
arXiv:2608.02699v1 Announce Type: cross Abstract: When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right to Explanation. Yet whether (and how) Explainable AI (XAI) can satisfy this right in practice remains poorly understood, with direct implications for individuals' ability to contest automated decisions that affect their lives. This paper presents a systematic literature review of XAI in the context of the EU Right to Explanation, with particular focus on Art. 15(1)(h) GDPR, Art. 86 AI Act (AIA), and related instruments. We consider papers published from 2024 onwards, as the final version of the AIA was published in July 2024---with Art. 86 being added late. From 2643 initial records identified by a deliberately broad search, we review 57 full texts, of which only 19 papers demonstrate substantive integration of both legal and technical perspectives, showing gaps in the interdisciplinary s
arXiv:2608.02669v1 Announce Type: cross Abstract: Docker Hub is the registry underneath most container deployments, and a flaw in a widely reused base image is inherited by every image built on it. Prior ecosystem-scale measurements each rely on a single detector, leaving the tool-dependence of their counts unquantified, while the studies that do compare scanners use samples of tens to hundreds of images. We present ChimangoScan, a pipeline that crawls the Docker Hub namespace (12,716,568 repositories, 663.8 billion cumulative pulls), reconstructs the image layer graph (54.4 million IS_BASE_OF edges), ranks images by an exposure score that folds an image's own pull count and those of its entire downstream subtree into one scalar, and scans the 52,895 highest-exposure repositories (84.7% of all recorded pulls) with six independent scanners, yielding 170.4 million findings. Vulnerabilities are near-universal: 96.3% of images carry a known package vulnerability, 93.4% a critical one, and
arXiv:2608.02607v1 Announce Type: cross Abstract: Organisations maintain large corpora of process models expressed in the Business Process Model and Notation (BPMN), yet the normative content encoded in those models, the obligations, permissions, and prohibitions that govern participant behaviour, remains inaccessible to policy infrastructure. The Open Digital Rights Language (ODRL) is the emerging lingua franca of machine-readable policy, but authoring ODRL at scale is slow, expert-intensive work, and generation by large language models introduces well-documented risks of structural invalidity. We present a pipeline that resolves this gap by extracting ODRL policies automatically from BPMN XML, grounded in the observation that BPMN control flow encodes deontic modalities by construction. The pipeline traverses the BPMN process graph, classifies each task as an odrl:Duty or odrl:Permission via a reachability check, and introduces a novel treatment of intermediate catch events as odrl:P
arXiv:2608.03973v1 Announce Type: new Abstract: Generative Artificial Intelligence (GAI) is increasingly being integrated into marketing education and is reshaping the skillsets required in marketing careers. While research has highlighted the promise and perils of incorporating GAI into education, there remains a need for a comprehensive framework to guide its effective use. In this research, we conduct a multipronged analysis, including a review of marketing course syllabi, a survey of marketing educators, and follow-up qualitative interviews. Building on Role Theory and the Community of Inquiry (CoI) model, we propose that GAI can assume three roles in marketing education: tutor, teammate, and tool. Each role influences teaching, social, and cognitive presence differently, shaping the learning experience and preparing workplace-ready marketing graduates. For instance, as a tutor, GAI can aid students in grasping theoretical concepts, while as a teammate, it can foster collaboration
arXiv:2608.03968v1 Announce Type: new Abstract: The rapid growth of learning analytics (LA) in higher education has expanded institutional capacity to monitor engagement, predict academic difficulty, and target support using student data. While these practices offer important educational benefits, governance has often remained compliance-first, centered on meeting baseline legal requirements such as the Family Educational Rights and Privacy Act (FERPA) and the General Data Protection Regulation (GDPR). Legal compliance is necessary, but it does not by itself resolve questions of fairness, student agency, accountability, or educational purpose. This paper proposes the LEAGUE framework, a six-pillar model for ethical governance of student data in LA: Lawfulness, Equity, Agency, Governance, Utility, and Ethics by Design. The framework is developed through a conceptual synthesis of scholarship in learning analytics, educational data mining, data ethics, educational policy, value-sensitive
arXiv:2608.03940v1 Announce Type: new Abstract: The dissemination of security and privacy education and guidance has been and still remains a challenge today. Social networks represent a potential avenue for sharing best practices, and experimentally they have been found to be effective at this task. While this appears promising, in the real world, security and privacy discussions would need to reach a wide range of people to be effective, avoiding the "interest bubbles" that commonly occur. We sought to understand how the communities surrounding security and privacy discourse operate, with a focus on what challenges need to be overcome to enable security and privacy discourse and advice to reach a wider audience. Indeed, we found that in-the-wild security and privacy discussions in social networks portray quite a different picture than in experimental settings. We built and analyzed the structure of a graph containing over 13 million users on the "X" platform (formerly "Twitter"), inc