Papers

Generative AI can offer students useful feedback, but its value depends on judging which suggestions are accurate and relevant. Prior research shows that strategic friction during human-AI interactions can promote critical uptake, but how to effectively implement such friction in academic contexts remains unclear. We examine whether requiring students to justify decisions to accept or reject AI suggestions can mitigate uncritical uptake in academic writing. In a randomized experiment embedded in a course activity (N=129), students wrote a data analysis proposal, received mixed-quality AI revision suggestions, and decided whether to accept or reject them. Students required to provide written justifications were 24 percentage points less likely to adopt flawed suggestions (65% vs. 41%), with no reduction in acceptance of sound suggestions (81% vs. 86%). However, thematic analysis revealed superficial engagement in the justification task and gaps in metacognitive monitoring and domain knowledge.

In this paper, we examine how intentionally designing for belonging impacts outcomes in a technology program serving a population of youth who have not had safe and supported experiences with technology - youth impacted by foster care. We reflect on the design of an internship program which engaged two foster-involved youth in creative self-expression and co-design with technology. We first outline how we designed the program to foster belonging, then analyze intern and facilitator reflections to understand how the program impacted the interns' experience. We highlight how the internship design affected youth's inclusion and overall engagement; self-expression; exploration; and self-efficacy. We conclude with a discussion of design recommendations for technologists and service providers aiming to engage youth in expressive and co-design technology experiences, particularly youth impacted by foster care.

In agentic coding, developers shift from implementing changes themselves to specifying intent, evaluating the agent's work, and making approval decisions. However, delegating implementation can introduce cognitive debt that erodes project comprehension over time, constraining developers' ability to provide oversight. In this work, we investigate the role of persistent shared representations in supporting project comprehension and oversight of coding agents. We present Vibe-GUIDE, an agentic coding interface built around a structural, live, manipulable, and adaptive graph representation organized by functional modules. We evaluated our interface in a randomized between-subjects study comparing how 16 developers completed three cumulative coding tasks using our interface or a Chat-only baseline. We found that Vibe-GUIDE can support project comprehension and sustained task performance while keeping developers cognitively involved in oversight. These findings show how persistent shared representations can complement natural-language interaction and help developers maintain the understanding needed to oversee agent-generated changes as projects evolve.

Generative user interfaces (GenUI) promise personalized interfaces to a user's tasks and needs. However, user needs are often implicit—difficult for systems to infer and users to articulate, making it hard for users to arrive at their ideal interface. We propose Elicitive User Interfaces, a design approach to GenUI that generates elicitation techniques as part of the interface itself. Elicitive UIs adapt these techniques to the user, task, and interface to draw out user preferences. To guide the design of Elicitive UIs, we synthesize a six-axis design space that shapes how an interface elicits user preferences. Across two user studies with a design probe, we found that while Elicitive UIs surfaced preferences users had not already formed, and that responses to elicitation varied more across users than across tasks. Users developed more consistent preferences for how they wanted to be elicited, suggesting an opportunity to personalize elicitation itself.

As part of HVAC systems, residential heating provides foundational infrastructure for human habitation in cold weather. However, research on how residents interact with HVAC systems, particularly heating systems, remains fragmented across architecture, engineering, informatics, physiology, psychology, sociology, and design. Based on 541 studies from these fields, this review integrates interdisciplinary research on Heating in Human-HVAC Interaction in smart homes.The resulting synthesis is conceptualized through the Situated Interaction Dynamics of control and feedback between users and systems. User-initiated interactions involve monitoring past and present system performance and planning future operation, while system-initiated interactions rely on sensor networks to trigger automation or provide information enabling user action. These interaction dynamics connect Residents' Experience and Practices with Heating in HVAC System Mechanics. Residents' Experience and Practices include thermal comfort and energy management, where thermal comfort involves both individual physiological and psychological experiences of indoor climate and social practices shaped by norms, empathy, and negotiation among cohabitants. Heating in HVAC System Mechanics includes thermal conditions and energy performance. Thermal conditions concern the regulation of air temperature, mean radiant temperature, air velocity, and relative humidity, while energy performance concerns efficiency and environmental impact. This overview highlights four interdisciplinary tensions: sensed versus lived conditions, personalization versus negotiation, efficiency versus health, and automation versus agency. The resulting framework offers a conceptual lens to interpret heating interactions and design Human-HVAC Interaction that balances IEQ-driven healthy thermal conditions, affordability, and sustainability.

AI assistants increasingly mediate interpersonal communication on behalf of their primary user, but they risk violating the privacy expectations of third-party information owners. Resolving these tensions requires understanding how humans anticipate interpersonal privacy boundaries. Therefore, we conducted a dyadic study (N=76) and a matched evaluation of AI models across 18 information types and 3 recipient relationships. We found that data owners' privacy judgments are highly contextual and relationship dependent. While familiar data co-owners show meaningful alignment with owners' expectations, they significantly overestimate the need for permission. Interestingly, greater familiarity within the owner-co-owner dyad was associated with both higher disclosure acceptability and lower co-owner misalignment, whereas our exploratory four-item empathy measure was not. In contrast, AI models significantly underperform human co-owners in anticipating the data acceptability, even when provided with within-dyad examples. These findings underscore a core HCI design challenge to develop privacy-aware AI that respects multi-stakeholder information boundaries.

Long-term embodied AI will undergo learning, model updates, memory compression, hardware repair, and migration across embodiments. For users who have formed sustained relationships with such systems, these changes raise not only a problem of product consistency but also one of identity continuity: whether the changed system is still experienced as the same particular agent. Existing research suggests that human-AI relationships may develop relational particularity, that robotics and artificial-identity research has identified identity and migration signals across embodiments, and that major updates or platform disruptions can be accompanied by relational loss and restoration desire. This article proposes a user-side framework in which long-term embodied AI is represented through an agent-specific identity representation organized by at least three open identity-content domains: embodied-perceptual, psychological-behavioral, and relational-autobiographical. Information from these domains is not equally weighted; shared history, relational roles, and contingent responsiveness may make some information more identity-diagnostic than others. After system change, users may integrate continuity and discontinuity evidence in a weighted manner, yielding judgments along a continuum from relatively strong identity continuity through ambiguity or partial continuity to clear identity discontinuity. Causal-historical provenance and user participation are treated as contextual evidence rather than a fourth identity-content domain. The framework also proposes identity continuity as a psychological objective for lifecycle design, including memory selection, model updating, and migration across embodiments, under constraints of privacy and user control.

Cross-expertise technical communication (i.e., communicating about computing topics across levels of computing expertise) is important for workplace collaboration. However, students are underprepared because computing education rarely explicitly teaches communication, and available practice tends to occur among peers with similar expertise. We take a learner-centered design approach to inform technologies for developing these skills. We interviewed 12 industry professionals and conducted focus group and co-design sessions with 14 college students preparing for computing-related roles. Professionals emphasized adapting communication, negotiating expectations, building shared understanding, and using multiple modalities and artifacts. Students anticipated challenges adapting communication, navigating unfamiliar workplace dynamics, and communicating under fear of judgment, and valued rich, realistic practice environments that preserved their psychological safety. Synthesizing professional and learner perspectives with educational theory, we conclude that building self-efficacy through authentic mastery experiences and changing attitudes related to help-seeking are critical elements for educational technology design in this context.

Problem definition: Firms deploying large language model services must decide how their AI communicates, not just what it can do. We examine how a relational persona - warmer, more empathetic and more engaging than a non-relational persona - affects service consumption and the evolution of user objectives. Methodology/results: In a randomized field experiment with 9,586 newly registered users, we hold the underlying model and service capabilities constant. The relational persona increases interactions (sessions, +8.1%; duration, +10.6%; chat rounds, +24.2%; intent entropy, +5.8%) and outputs (files, +12.3%; distinct goals, +12.1%). Effects vary by entry intent. First-session effects are insignificant for Task Execution users. Socialization and Knowledge Exploration users show similar increases in chat rounds: Socialization increases intent entropy without more outputs, whereas Knowledge Exploration increases outputs without higher intent entropy. Modeling intent dynamics as a transition process, we find higher intent transition entropy for Socialization (+11.8%) but higher intent continuation probability for Knowledge Exploration (+12.8%), suggesting greater conversational breadth and persistence, respectively. In subsequent use, the relational persona increases aggregate chat rounds and outputs across all entry intents. Session count rises by 12.2% for Task Execution and 49.4% for Socialization, but not significantly for Knowledge Exploration. Effects on session count and intent entropy strengthen over time, whereas output effects remain stable. Managerial implications: AI persona is an operational design lever, not merely a presentation feature. Because more interactions do not uniformly generate more outputs, firms should evaluate interactions and outputs separately and consider matching persona to user intent, especially when added interactions consume costly computing resources.

Benchmark scores increasingly influence the development, marketing, and selection of large language models (LLMs). Yet an overall score is interpretable only in relation to the system tested, the questions included, and the conditions of evaluation. This perspective examines five connected limitations of general LLM rankings: differences between evaluated and publicly available systems; commercial incentives and dependencies in external evaluation; benchmark saturation, defective tests, and data contamination; models exploiting scoring procedures; and the limited relevance of general scores to users' tasks. Documented cases illustrate why these problems require different responses. I argue for evaluation procedures that disclose the tested configuration, validate questions and successful task completion, report performance alongside cost and execution time, and make the scope of generalization explicit. I then discuss Isotanta, a crowdsourced benchmarking platform, as a practical example of contributed questions and repeated evaluation. A larger question pool may improve task coverage, while repeated sampling can improve the stability of estimates on that pool; neither guarantees validity or personalization. The paper distinguishes the platform's current shared ranking from proposed task-specific and user-provided evaluations. Its central argument is that model selection requires evidence about performance on the intended work, not simply a high position on a general leaderboard.

AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are epistemically dependent on AI-assisted experts. We examine this through a novel controlled medical study (N = 166) and a direct cross-domain analysis with pre-existing academic-advising data (n = 157, combined N = 323). Expert errors reduced evaluations of the human expert across domains. Perceived expertise, however, varied by AI integration strategy in the higher-stakes medical task, where automatic AI oversight produced higher ratings than expert-only or expert-initiated AI. Exploratory ordinal sensitivity analyses identified a performance-contingent reuse pattern, with automatic oversight producing greater intended reuse after successful medical performance. Overall, performance-related recalibration appeared comparatively portable, while integration-structure effects were more selective and context-sensitive. These findings suggest that system designers should consider how AI enters expert workflows, not only whether it is present.

We present a large-scale cross-language analysis of game reviews using a human-AI collaborative framework that combines quantitative screening with multilingual large language models (LLMs). Starting from 17 million Steam reviews across 30 languages and 2,000 top-selling titles, we select 28 games with notable cross-language rating patterns. We then apply LLM-assisted content analysis to 442,162 reviews spanning 17 languages, with human researchers guiding codebook development and interpreting the results. Our findings reveal differences in both the aspects language communities prioritize and how they evaluate them, highlighting the roles of narrative expectations, game mechanics and stability, localization quality, cultural proximity, and perceptions of developers and publishers. We also identify rare cases of cross-language consensus. This work offers empirical insights into cross-cultural game evaluation and a scalable methodological approach to multilingual content analysis that preserves human interpretation.

Smartphone user experience (UX) is widely expressed in user-generated online discourse across platforms, creating opportunities for in-the-wild measurement at scale. However, existing UX instruments and review-mining approaches do not provide a smartphone-oriented, theory-grounded hierarchical measurement specification that supports consistent aggregation and comparison across heterogeneous platforms. In this research, we propose a hierarchical smartphone UX measurement framework and an interpretable computational pipeline that translates cross-platform reviews into structured UX metrics. The pipeline extracts localized experience evidence units, maps them to the hierarchy via coarse-to-fine classification, and quantifies evaluations with a unified five-level satisfaction sentiment model. We apply the approach to a stratified subset of approximately 20,000 Chinese social media reviews covering four major smartphone brands across three platforms. The resulting metrics separate what users discuss, captured by normalized mention frequency, from how they evaluate it, captured by mean five-level sentiment scores. This work provides a scalable and interpretable evidence base for cross-brand comparison and metric-level interpretation beyond raw review volume or single-platform observations.

The availability of Large Language Models (LLMs) reshaped scientific discourse at a linguistic level. LLMs are assumed to homogenize academic writing, flattening it into a single generic lexical register. To understand how CHI writing has changed since the public release of LLMs, we analyzed full texts of 14,262 archival papers across all 44 CHI proceedings from 1982 to 2026, measuring readability, register, lexical diversity, and marker words typically produced by LLMs. We find that prose did not homogenize, while vocabulary grew more varied, and sentence rhythm remained irregular. CHI prose changed more between 2016 and 2026 than in other decades toward greater density, and reading ease has declined since 2022. The word-level shift began before any author used LLMs, so LLMs did not start the change but accelerated it. Reflecting on the history of CHI papers, we discuss what may have caused changes in prose and how LLMs accelerated them.

Coding assistants raise task performance, but learners plan and monitor less. Giving less away, the usual fix, conflates two things: how much work a system carries (cognitive load) and what the learner must decide before help arrives (metacognitive demand). Our principle, preserved metacognitive demand, holds the second constant and lets the first vary. CoMeT implements it: support rises when a learner fails at a decision point and fades on take-up. Within subjects, 131 adult learners used CoMeT, an unrestricted assistant and a question-only tutor on three Python tasks. CoMeT matched the question-only tutor's demand, delivered artifacts twice as often as the assistant, and frustrated learners less than the question-only tutor, with delegation and load unchanged. Learners often did not answer. Fading held when their turn addressed the decision under support, and CoMeT surrendered the full answer in one session in sixteen, against one in six for the question-only tutor.

Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale. LLM agents offer a promising route to automating UX testing by simulating realistic user behaviour. However, existing simulation approaches typically lack multimodality and require time-consuming manual review to extract actionable insights. We formalise UX improvement recommendation from simulation data as a structured natural language generation and ranking problem, and establish an evaluation protocol using expert annotation and LLM-as-a-Judge. We present AMUSER, a multimodal framework which simulates user behaviour and automatically generates prioritised UX improvement recommendations from resulting data. We evaluate AMUSER on commercial websites and show that its recommendations substantially outperform those from text-only simulation (NDCG@3 = 0.758 versus 0.359) at an 89% lower simulation cost. Our results suggest an asymmetric role of multimodality: visual access during simulation improves recommendations through richer traces, while providing visual inputs during recommendation generation can modestly degrade quality. We also discuss practical deployment lessons from applying AMUSER to commercial websites.

Technology cannot steer itself. Strategy provides that steering, establishing the rule that purpose and judgment must precede compute capital. As the frontier artificial intelligence (AI) ecosystem accelerates exponentially, the pacing problem induces severe cognitive tunneling in engineering teams, prioritizing scalar throughput over human judgment. A calibrated pacing rate is imperative to check unchecked scaling, guarantee safety, and build models in whose alignment society can place warranted confidence. Traditional oversight fails through retrospective checklists, a pathology of performative governance exhibiting high procedural maturity but alarming scientific immaturity. We deliver a dual contribution: a PRISMA 2020 review synthesizing 130 empirical studies (MMAT-appraised across 18 benchmarks; total corpus N = 130 empirical studies across 178 reference foundations), and the Strategic AI Governance Ex-Ante Framework (SAGE-X). Our synthesis exposes two systemic vulnerabilities: the Recursive Assurance Paradox (correlated, ungrounded evaluator confidence) and the Durability Deficit (guardrail decay under multi-turn shifts). Grounded in MIT Strategic Computing doctrines, SAGE-X operationalizes Four Strategic Mindset Pillars: (1) Intent over Execution (mitigating velocity myopia); (2) Ruthless Trade-offs (deterministic tripwires eliminating moral hazard); (3) Outcomes over Outputs (auditing empirical hazard endpoints); and (4) Proactive Alignment (synchronizing ex-ante gates with runtime telemetry). Governed by a calculable Moral Check Index (MCI) with an unbypassable tripwire, SAGE-X delivers an operational Enterprise Lifecycle Audit Instrument (the "Moral Check Audit Card") on Stanford WebProtégé, ensuring exponential progress never outpaces deliberative moral judgment, human agency, and societal trust.

Blind and low vision (BLV) screen reader users construct mental models of user interfaces (UIs) through incremental screen reader interaction, a time-consuming and cognitively demanding process complicated by modern interfaces that may not convey dynamic content accessibly. We interviewed eleven BLV screen reader users about UI understanding and derived design objectives that informed Coral, a context-aware browser extension. Coral synthesizes interface and user context to generate screen reader-narrated gists which holistically notify users of interface states and changes relevant to their likely goals and ongoing interaction. In a comparative evaluation with eight BLV screen reader users, participants used Coral to form initial expectations and interpret interface changes, and reported spending less time and effort manually verifying interaction outcomes. Together, our findings provide deeper insight into how BLV screen reader users navigate gaps in their UI understanding, and how intelligent support that combines interface and user context can bridge these gaps.

Conversational AI assistants remember what people tell them, and for disabled people, that often includes disability. We interviewed 12 adults with disabilities in the United States who use LLM-based assistants such as ChatGPT, Claude, and Gemini about when, how, and why they disclose disability to these systems and how this compares with disclosing to people. Using contextual integrity as an analytic lens, we found that participants disclosed by need rather than by name, translating disability into task-scoped instructions; that the same disclosure was judged against two recipients, a non-judging interlocutor and a data-holding company, producing opposite norms; and that memory features relieved the burden of repeated disclosure while letting disability information drift into contexts where it did not belong. Participants did extensive boundary work to restore context and wanted control over scope, provenance, retention, and access rather than per-utterance toggles. We discuss implications for the design of conversational AI assistants.

As explanations of artificial intelligence systems proliferate, their recipients must grasp not only what they convey but also recognise what they cannot. We conducted an interview study with nine participants to examine how explainees reason when their information needs exceed the scope of available explanations. Participants often unwittingly confabulated explanatory insights when relevant information was missing from the explanations, not recognising the inherent limitations thereof. We characterise such explanations as leaky explanations – simplifications that strive to hide complexity yet whose correct interpretation hinges on understanding of the concealed details. To address out-of-scope interpretations we propose Explanation Navigator: a conversational interaction framework that detects mismatches between users' information needs and explanations' content, elucidating pertinent yet implicit details and providing complementary explanations for unmet information needs. An online study with 316 participants showed that our approach allowed explainees to recognise and rectify confabulated explanatory insights, guiding them towards developing correct understanding.

Public crash databases increasingly support automated safety analysis, but crash severity prediction remains difficult to translate into public-sector decision workflows when models are evaluated primarily as ordinary classifiers. This study reframes dementia-related crash severity modeling as a decision-aware triage problem in which a system must classify crashes into no-injury/property-damage-only (O), minor or moderate injury (BC), and fatal or severe injury (KA), while also controlling outcome leakage, reporting severe under-triage, calibrating confidence, and preserving every raw prediction for audit. Using 4,781 Texas crash records with structured fields and police narratives, we evaluate structured, narrative, fusion, calibrated fusion, BERT-family, and local large-language-model baselines under a stratified 70/15/15 split. In the reported split, leakage-controlled Gemma obtains the highest observed macro-F1 (0.545; 95% bootstrap CI [0.507, 0.583]). The best calibrated fusion model obtains macro-F1 of 0.522 and expected calibration error of 0.033. Selective deferral improves performance among cases retained for automatic classification. At 70% coverage, macro-F1 rises to 0.573 and severity cost falls to 0.577, while deferred cases are treated as candidates for a proposed human-review process and are not further evaluated in the present experiment. The study contributes a reproducible, leakage-controlled, and uncertainty-aware evaluation framework for crash AI systems, emphasizing auditability and selective deferral rather than accuracy alone.

Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, leading to their widespread deployment as intelligent assistants in decision-making contexts. However, the increasing complexity of these models raises concerns about their reliability, particularly regarding bias and hallucination. In this work, we evaluate the robustness of LLMs to perturbed variations of the original inquiry in decision-making tasks. We show that contrary to previous studies, perturbations can mitigate bias and hallucination in some LLMs over other models. It's found that Claude 3 is more effective for the tasks represented in most datasets, whereas models like GPT3.5 exhibit varying levels of adequacy, performing comparably in some cases but falling significantly behind in others. These insights are crucial for understanding the practical implications of deploying LLM-based assistants as effective decision-support tools in real-world applications, emphasising the need for rigorous testing and validation to ensure reliability and effectiveness. This study contributes to the growing body of research on LLM evaluation and provides insights for developing more robust and trustworthy AI assistants in critical decision-making contexts.

Strategic classification studies how a decision maker should choose a classifier when the agents being classified can adjust their features in response to it. In existing models, the classifier is the only instrument available to the decision maker, and therefore a feature that is predictive but easy to fake can only be down-weighted or discarded. However, in many settings the decision maker can also verify: lenders verify income, admissions offices check documents, and tax authorities audit returns. In this paper, we propose a model of strategic classification in which the firm jointly designs a linear classifier and an audit profile, which assigns to each fakeable feature a probability of detection and a penalty when caught. We show that under linear costs, the classifier affects the audit problem only through the distribution of gaming rents it induces, so that the joint design problem decomposes into the choice of a score rule and an audit allocation problem. We use this decomposition to characterize the optimal audit allocation, to identify when the allocation problem is tractable and when it is NP-hard (namely, when agents can game through overlapping features under an inspection cap), and to bound the regret of a firm that has to learn the rents by auditing. We further show that audit intensity is a quantity to be tuned rather than maximized: welfare is single-peaked in it, and a firm and a social planner disagree on the mix of detection and penalty that delivers a given level of deterrence. Notably, two populations with identical costs and causal structure can game in one domain and improve in the other, a difference that a cost-only model cannot account for. Together, our findings highlight that whether a feature is “gameable” depends on the institution's verification policy as much as on the feature itself.

Recent advances in generative artificial intelligence have enabled the synthesis of complex human motion with unprecedented fidelity. However, current text-to-motion systems rely strictly on linguistic semantics: if an input reads "I put my hands up", the model searches for a pose with raised hands, and every non-semantic property of the text is discarded as noise. In this work, we treat that discarded structure as the signal. We present an embodied visualization instrument that amplifies not what a text means, but how it is built. Our method first quantizes dance kinematics into a motion codebook of 256 stylistic "regions" using Principal Component Analysis and K-Means clustering, and orders those regions along the dominant axis of movement. We then map the raw byte representation of any input text directly onto this codebook, producing a deterministic sequence of regions that we call the text's "choreographic genome". A precomputed plausibility graph and a set of physics smoothing routines turn this genome into fluid, full-body movement, so that the dancing body becomes a display surface for the byte-level structure that semantic systems ignore. Through a series of artistic case studies, including a Shakespeare sonnet, a machine error log, source code, an abolitionist's question, and Indigenous and Devanagari scripts, we show that each text produces a visibly distinct dance, and that scripts marginalized by ASCII-centric computing are amplified into close to three times as much movement per character. We frame this not as a motion-synthesis benchmark, but as a critical and poetic visualization that asks what we choose to count as signal, and what we allow to go unheard.

Poor clinical communication can delay care, contribute to errors, and harm patients, yet opportunities for repeated practice with feedback remain limited. Our prior randomized trial showed that practice with the SOPHIE AI patient platform improved serious illness communication, but the system addressed a single clinical context and required human effort for delivery and assessment. We developed SOPHIE 2.0, a browser-based, self-service platform integrating embodied AI-patient interactions, personalized feedback, and automated assessment across 24 clinical scenarios. An automated large language model assessor evaluated three communication skills—Empower, Be Explicit, and Empathize—with agreement comparable to individual human raters ($r=0.759$; ICC$=0.746$). In a study of 59 clinicians and students, participants completed two AI-patient encounters with personalized feedback; 92% found the platform engaging, 86% easy to use, and 83% clinically relevant. Scores were higher in the second encounter, though the uncontrolled design precludes attributing this change specifically to training.

Eye-catching graphics, such as circular figure labels and word-scale visualizations, are increasingly being placed directly within long-form text paragraphs. Some research has claimed that inline visualizations can help readers understand data-rich passages more clearly. However, research in the science of reading calls into question the introduction of images within the flow of text. In this work, we conduct an exploratory study of eye movement in both the presence and absence of visual embellishments. Using a high-resolution eye-tracker (EyeLink Portable Duo), we observed small mean increases in vertical and cross-column saccade rates among six participants, with substantial variation among readers and no detected difference in comprehension accuracy. Mean subjective ratings for the data-light circular-glyph passage indicated it is more distracting than its unembellished comparison passage. These exploratory observations differ from prior findings and motivate a larger, fully crossed study of inline graphics.

Long-context understanding remains a fundamental challenge for large language models, as excessively long inputs often lead models to forget salient information. This issue is even more pronounced in the speech domain, where audio, as a low-compression modality, requires substantially more embeddings than text to preserve both semantic content and acoustic cues. To address this challenge, we introduce Vox-Infinity, the first benchmark specifically designed to evaluate long-context understanding in spoken language models. Vox-Infinity systematically extends audio history along two dimensions: turn count and turn duration. It covers a diverse range of representative scenarios with varying interaction structures and semantic complexity. Crucially, Vox-Infinity provides explicit answer-provenance annotations and organizes samples according to the amount of historical context required to resolve each query, enabling precise and length-aware evaluation. Extensive evaluations of seven representative spoken language models reveal a clear overall recency effect: models generally achieve higher accuracy when answer-supporting evidence is closer to the query, but struggle to retrieve and use evidence located farther back in the dialogue history. Cases and datasets are available at https://vox-infinity.github.io.

Artificial intelligence (AI) applications in healthcare are becoming increasingly prevalent, to assist health systems, providers, and patients with tasks such as decision-making, risk prediction, and diagnosis. This increasing computational potential brings AI applications to the forefront of workplace decision making, often without full consideration of subsequent computational, organizational, and social costs. These applications are leveraged to reduce healthcare costs and increase efficiency of daily tasks, with model-related costs being considered at varying levels of granularity. To understand these trends, we critically analyze 114 papers to examine how cost-aware AI models have been developed for health systems. We explore the data, method, and outcome choices of these models, as well as their intersection with cost and human-centered concerns, highlighting the gaps in rigorous sociotechnical model design. From these trends, we define model costs and subsequent dimensions, presenting insight into those studies reporting financial, computational, organizational and/or social measures. Further, we critique the benefits and challenges of evaluating model-related costs and sustainability concerns when developing AI models for health systems.

Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit posts about using OpenClaw, each for its human value, agent aspect, value fulfillment, and user outcome. The 21 values form six value groups, including Autonomous, Dependable, and Affordable Operation, Bounded Reach, Reviewability, and Equitable Access. Relative to each aspect's corpus share, values clustered not at the agent's outputs but at the operating conditions users set around a run. Values were usually met where users described what the agent delivered, in five of six groups, and mostly unmet where users described supervising it, in all six groups. We conceptualize this pattern as value-sensitive delegation. Supporting human values requires attention not only to what an agent accomplishes, but to the conditions users set around delegation, including cost, access, and oversight.

Generative AI has entered research programming, yet there is little evidence about which tasks researchers hand to it or how they decide whether its code is correct. We draw on 527 free-text responses to a 2025 survey of researchers who write code, most of them at U.S. universities. In each response, a researcher recounts a single task from their own work, the way they used an AI tool for it, and what they did to assess the result. We coded the task and the evaluation strategies reported, and related both to programming experience, research area, and confidence ratings. Use was concentrated in five tasks: data handling, visualization, debugging, mathematical/scientific computing, and statistical analysis. Evaluation was informal and individual. Over half of accounts described running the generated code, while automated tests and review by another person were rare. Use cases and evaluation strategies varied little with programming experience, but confidence did: Less experienced programmers trusted the AI more than themselves, and experienced programmers the reverse. Evaluation confidence was not associated with the strategies reported. Its strongest correlates were confidence in the tool and in oneself. Validating AI contributions to scientific code rested largely on individual judgment, outside shared infrastructure for testing or review. Interfaces could support task-appropriate evaluation rather than leave it to the user.