Papers

Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave. Prior accounts establish these patterns by prompting black-box LLMs, leaving open whether the gap is a prompting artefact or a fact about how the underlying constructs are represented internally. We investigate risk-taking, a consequential dimension of agentic decision-making, using activation steering to measure self-report and behavior under the same internal intervention. We survey nine steering-vector extraction methods spanning task-specific directives, the model's own task behavior, and dispositional descriptions at two granularities, evaluated on two behavioral tasks and two psychometric instruments across four open-weight LLMs. We find that (1) a shared internal intervention does not ensure shared responsiveness: directions built from trait descriptions move self-report but leave behavior at chance, directions built from the model's own task choices do the reverse, and only task-specific directives reach both, weakly. (2) Diagnosis dissolves that exception: removing surface confounders leaves the directives only 32% of their behavioral effect. The two channels are otherwise steered by near-orthogonal directions, each channel reachable by several independent constructions. (3) An intervention composing one behavior-moving and one self-report-moving direction moves both together; flipping one sign sets them in opposition, with reported and enacted risk pointing in opposite directions on 69-89% of flipped compositions in all models. These findings move the self-report-behavior relationship from a black-box observation to a representational one that can be inspected and controlled, motivating representational checks alongside behavioral evaluation.

AI agents can take actions. At times, those actions can go beyond what is intended. Agent reliability can be defined as assurance that an agent will stay within intended bounds and operate within limits. Today, there is no shared framework or language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of agent reliability would fill the gap. This paper introduces the Agent Reliability Profile, a per-agent unit of assurance evidence for agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.

Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals' decisions. We examine what information helps synthetic respondents predict each individual's later choices, using five conditions that add progressively richer information: no personal information, demographics, personality traits, cognitive scores, and finally the respondent's earlier survey choices as behavioral history. We use a two-wave panel of 845 US adults who completed measures of 14 behavioral biases (spanning risk, time preferences, overconfidence, and reasoning), so each respondent's earlier answers provide a human test-retest benchmark; in the behavioral-history condition, all items that score the target bias are withheld. At the population level, the average number of biases per respondent in every condition is close to the human average (7.1-8.1 biases, against 7.1 for humans). This aggregate similarity masks differences in variance: persona descriptions recover only 53-67% of human between-person variation, whereas adding behavioral history restores it to approximately the human level. At the individual level, description-based personas achieve only 7-12% of the informedness observed in human test-retest responses, while adding behavioral history raises this to 28%. The condition including behavioral history has the highest estimated informedness in all 17 demographic groups, whereas description-based conditions provide little or no information for some groups. Synthetic responses also exhibit stronger education- and income-related differences than human responses. For LLM synthetic personas, a respondent's past answers add more to individual-level prediction than a description of who they are.

Public sector agencies have adopted participatory design as a prominent procedural safeguard proposed against algorithmic harm. Across methods, institutional contexts, and well-intentioned implementations, the participation gap persists. Communities engage, input is documented, and systems are deployed largely unchanged. This paper locates the persistence of that gap in the mechanism institutions use to document participation itself. We develop the concept of the legibility trap, in which institutional mechanisms for documenting participation systematically select for forms of community input that fit institutional categories and filter out the forms that would most constrain state power, including foundational dissent, demands for non-deployment, and what Kelly Oliver calls witnessing to structural conditions of harm. Drawing on James Scott's theory of state simplification, we demonstrate the legibility trap through critical discourse analysis of twenty-two official, community-produced, and public-record documents across three public sector AI deployments: the Allegheny Family Screening Tool, Detroit Project Green Light, and the Community Control Over Police Surveillance (CCOPS) ordinances in Oakland. We identify the structural conditions under which participation partially escapes the trap, and we theorize that repeated non-response constitutes a harm to community subjectivity that AI ethics frameworks have not yet named.

Computational tools for biological research (biological tools, or BTs) are advancing rapidly in both quantity and demonstrated capability. While these technologies offer many benefits for scientific advancement, malicious actors may seek to exploit them to design and develop biological weapons (BWs). Generally, BTs require specialized expertise to operate successfully, presenting a barrier to nefarious use by nonexperts. Large language model (LLM) Agents may erode this barrier by enabling actors with limited domain expertise to successfully use BTs. Assessments of how AI could change bioweapons development pathways should examine how LLM Agents interact with BTs on relevant biological design tasks, but such assessments are limited. In this report, we present an assessment of the abilities of LLM Agents in the early stages of interaction with BTs. First, we evaluate seven frontier LLM Agents on their ability to select appropriate BTs for a given task. Second, we assess how these LLM Agents engage with two BTs, EVEscape and ESM3, through targeted case studies. These case studies probe the enabling capabilities required for autonomous BT operation, a potential entry point to a wide range of risk pathways. We identify and assess technical barriers at the intersection of LLM Agents and BTs, offering the biosecurity community and AI developers a foundation for further risk and capability assessments as technologies progress.

This report is a continuation of previous efforts to test the ability of large language model (LLM)-driven artificial intelligence (AI) agents to interface with AI-enabled biological tools (BTs). While rapid advancements in BTs in recent years have brought promise to accelerate scientific discovery, they also raise significant biosecurity concerns about potential misuse. The biosecurity community is particularly interested in the extent to which LLMs can lower technical barriers and assist non-expert users in accessing and operating BTs. Despite this interest, few evaluations have focused on LLM-BT interactions in the context of a defined threat model. To address this gap, this report describes a test of frontier LLM-driven AI Agents on their ability to use BTs to redesign peptides and proteins to evade nucleic acid synthesis screening measures. Highly relevant to biorisk, this task assesses a potential capability of AI agents that could enable a breach of a critical early defensive layer designed to prevent a multitude of biological misuse scenarios. The findings presented here intend to offer a foundation for biosecurity researchers and AI developers to conduct or further risk and capability assessments as these technologies progress.

Large language models (LLMs) are increasingly used to support legal practice, education, and research, yet their reliability in national legal systems outside the United States remains largely undocumented. We introduce an expert-validated benchmark for evaluating LLM reliability on the Colombian legal system. The benchmark comprises 1,042 items spanning ten areas of law and three question formats (closed multiple-choice, semi-open, and open-ended IRAC), built through a human-in-the-loop pipeline with multi-stage expert review. We evaluate 15 contemporary proprietary and open-weight models with format-appropriate metrics. Accuracy on closed questions ranges widely, from 0.905 (Gemini 3.1 Pro) to 0.577, but on free-text legal answers factual correctness never exceeds 0.45 (on a 0-1 scale) for any model. We find a dissociation between answer relevancy and correctness (Spearman rho = -0.46): models reliably sound responsive while frequently being wrong, a pattern of particular concern for non-expert users. Closed-question accuracy and free-text correctness are strongly rank-correlated (rho = 0.94), so cheap multiple-choice screening predicts model ranking but overstates absolute reliability. An independent rubric-based LLM judge and blind human expert scoring both reproduce the free-text ranking (rho >= 0.88). The judge further reveals that only about half of the norms models cite are correct; the rest are wrong or non-existent. Reliability varies systematically by legal area and follows an inverted-U across question complexity. Our results indicate that current LLMs require expert supervision for Colombian legal tasks, and that grounding answers in authoritative sources is a promising path to higher reliability. We release the benchmark construction pipeline to support reproducible evaluation.

Funders know their portfolios through software that classifies award text. The US National Institutes of Health (NIH) reports its spending in more than 300 categories mined this way, and work on classification and indicators treats the text as the applicant's to write. In 2025 NIH required "DEI language" removed from awards not supporting DEI activities, and so policed the words it also counts. We followed population names through 37,790 continuing awards, checking them against practice records. Names were informative: in new awards with trial baselines, a title naming Black populations predicted a 60-percentage-point higher enrolled share. Text features predicted which names survived, and practice records added little. NIH's minority health category followed the names: of continuing projects carrying it, 99.4% with unchanged summaries kept it, against 15.7% of those whose summaries no longer named a racial or ethnic population, while the projects kept their funding. Blinded reviewers judged 42 of a random 50 such losses to have met its definition in FY2024. Comparing both summaries of 70 name losses, they found aims concerning the population recast in 49. Read alone, 63 FY2025 summaries no longer met the definition. Awards naming sexual and gender minorities were recorded as terminated 37.8 points more often after adjustment for listed terms, institute and activity. We call the mechanism word targeting, its boundary population targeting, and its product uncounted science: funded research the count no longer records. The count followed the edited text, and the edited record cannot say whether the research changed with it.

Ethereum's electricity use fell by about 99.95% after the move from proof of work to proof of stake. Service providers still need to report operational energy use, e.g. under the EU Markets in Crypto-Assets Regulation (MiCAR). Existing estimates either apply one typical wattage to every node or start from aggregated monitoring counts. Both ignore attributes that nodes already advertise on the peer-to-peer network: client software, ARM or x86 hardware, hosting location, and validator role. We crawl the consensus and execution layers, assign each peer a wattage from those attributes using published measurements, and estimate the remaining incomplete peers with a Random Forest. On 6,934 peers from two Nebula crawls (19 and 22 June 2026), reachable nodes sum to 415 kW, or 3.63 GWh if that draw were held for a year. The same Lighthouse+Nethermind x86 wattage on every peer yields 431 kW. Observed attributes lower the total by 3.9%, mainly because nodes at Hetzner and other non-AWS clouds draw less than that home-desktop figure. AWS accounts for 15.6% of watts from 12.2% of peers, and validator-flagged nodes for 31.4% of watts from 25.7% of peers. The 415 kW snapshot is about 46% of the Cambridge Centre for Alternative Finance (CCAF) estimate of about 0.90 MW. Both use about 60 W per node, so the gap is mostly how many nodes each estimate includes. Rules cover 3,110 peers and the forest the other 3,824. On held-out labeled peers with client, architecture, and OS hidden, the forest's mean absolute error against the rule wattage is 4.3 W. Twenty-four-hour measurements on a gaming desktop differ from the predictions. After subtracting a 33 W idle graphics card that Ethereum clients do not need, both differences fall to about 19%.

We model an academic curriculum as a generator of a language of feasible study plans: prerequisites are monotone Boolean formulas in conjunctive normal form, degree requirements are credit-threshold covering constraints, and a study plan is a sequence of terms bounded by a per-term credit capacity. Within this model, we settle the complexity of the two natural planning objectives, the number of terms to a degree and the total credit load, and we isolate the structural commitment responsible for each source of hardness. Time to degree is polynomial whenever the per-term capacity is unbounded, for arbitrary disjunctive prerequisites and arbitrary electives, so disjunction never contributes to its hardness, yet it becomes strongly NP-hard as soon as capacity binds, even without any prerequisite. Load is complementary: disjunction and overlapping electives are each strongly NP-hard in isolation and their complexity does not depend on capacity, while load is polynomial on the conjunctive, mandatory fragment. The two objectives therefore have disjoint sources of hardness. We show that the delay-factor component of the standard curricular-complexity metric is a polynomially computable upper bound on time to degree, exact on the conjunctive fragment and loose elsewhere by a quantity we name the disjunctive slack, and we prove that program subsumption is coNP-complete and consensus prerequisite recovery is NP-complete. Instantiating the model on a corpus of twenty-two universities, we find that 88 percent of prerequisite-bearing courses are purely conjunctive and that capacity, not prerequisite logic, is the operative constraint on time to degree. The curriculum corpus is openly available (https://doi.org/10.5281/zenodo.22334674), and the analysis and figure-generation code accompany the paper.

\ textbf{Background.} Ireland's position on the eastern North Atlantic exposes it to moisture-laden westerlies and frequent low-pressure systems, generating abundant precipitation and substantial pluvial and fluvial flood risk. Climate change is intensifying heavy rainfall and compound flood risk, with consequences extending beyond physical damage and conventional economic indicators. \ textbf{Method.} We present a framework for assessing flood-related wellbeing from unobtrusive public discourse over time. The framework develops an assessment instrument comprising three complementary constructs: distress, functional disruption, and institutional alienation, capturing flood-related impacts on affective appraisal, daily functioning, and social and institutional connectedness. To enable population-scale analysis of complex cognitive and psychological responses, we develop a dedicated flood-related wellbeing reasoning model (Wellbeing-Former) that reads evidence of flood-related wellbeing, assigns evidence-grounded scores, and produces transparent rationales. Data, Results & Implications. Using the research platform MCL (Meta Content Library), we construct a flood-related discourse dataset by querying posts from Ireland containing the terms flood, rain, and storm. The resulting dataset comprises approximately \textbf{$224{,}000$} posts produced between 2012 and 2016. We apply the resulting Wellbeing-Former to this dataset to conduct a population-level analysis. This approach provides temporally sensitive evidence of experienced and anticipated flood impacts, complements survey-based assessment, and supports a multidimensional understanding of wellbeing under climate-related hazards for policy development, adaptation planning, public communication, and decision-making.

Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has therefore proposed trigger-tag mechanisms that produce a detectable signal when a model is used under a target condition, such as generating phishing contents. Although these mechanisms borrow from established techniques, their use for conditional misuse detection in open-weight LLMs is relatively new. Therefore, existing research works have not systematically studied the robustness of trigger-tag mechanisms under adversarial attacks. To close this gap, (i)~we formalize trigger-tags and distinguish token-level trigger-tags, which introduce watermark-inspired signals during decoding, from weight-level trigger-tags, which learn backdoor-inspired associations between target conditions and detectable model behavior. Furthermore, (ii)~we introduce \Untag, a unified attack framework that organizes their mechanism-specific attack surfaces into a common taxonomy. We evaluate representative token-level and weight-level trigger-tags using phishing as a case study. We find that while trigger-tags may provide useful evidence in controlled settings, our attacks render the existing trigger-tag mechanisms to be entirely ineffective. Consequently, we argue that these mechanisms should not be treated as robust misuse detectors when attackers can transform outputs or modify open weights.

When two AI agents disagree, who persuades whom? As multi-agent systems increasingly combine language models of different families and sizes, the answer can determine which judgments survive interaction. Measuring persuasion as the probabilistic shift in an agent's decision after a single exchange with a dissenting peer, we test seven open-weight models across three language understanding tasks. We find that persuasion is strong: when models disagree, receivers often abandon their initial judgment after seeing a peer's answer and explanation. Surprisingly, however, neither standalone certainty nor model scale reliably predicts persuasion dynamics. Models producing almost perfectly consistent decisions in isolation can be among the most susceptible to persuasion, and small models can match larger ones as persuaders and resist their influence just as effectively. Furthermore, we show that the size of the shift depends more on the susceptibility of the listener than on the persuasiveness of the speaker. Persuasion patterns are therefore specific to each model pairing, with heterogeneity amplifying persuasion in some combinations and suppressing it in others, allowing a dissenting agent running a small model to overturn the judgments of a much larger one. These findings show that the behavior of interacting models cannot be inferred from their individual properties but must be evaluated in the combinations in which they will operate.

What makes a short story gripping; a news article newsworthy; or a math proof elegant? These constructs resist articulation or verification; their meaning is at least partially tacit. However, modern AI models are improved primarily via articulated constitutions, rubrics and verifiers (i.e. in RLAIF and RLVR); tacit components of preferences are typically understudied. We introduce a large, labeled preference dataset CreativePreferences, containing 2.8M texts labeled by 317M human preference judgments across 7 creative domains, with 42 benchmark tasks. We model these labels with executable programs, rubric banks and densely trained models (V, A and VAT, respectively). We observe robust articulability gaps, VAT-VA; and verifiability gaps, VAT-V; we estimate upper and lower bounds for each gap with a novel measurement approach that discovers articulable and verifiable metrics, identifies spurious variables and estimates the value of undiscovered metrics using capture-recapture. These gaps occur across all domains, even in domains traditionally treated as fully verifiable: correctness-centered domains (i.e. mathematics and software engineering) and claim- and novelty-centric domains (i.e. news, patents, peer review). The size of the gap varies based on domain (e.g. peer review and creative writing have the largest articulability gaps) and widens as more people take part in the judgment, consistent with Collins' collective tacit knowledge. We show two consequences: (1) on human generations, the full model more closely matches human preferences, often in disagreement with articulated criteria, and (2) in an analogy to Goodhart's law, articulating preference shifts it away from the tacit dimension. Articulability and verifiability gaps are consequential; we give recommendations on when tasks can be prompted; how learning mechanisms might improve; and when to leave judgments with humans.

Generative AI weakens the link between assessed performance and the cognitive processes that educational work is expected to reflect. This paper examines observable traces of those processes in a sample of 150 student-AI conversations. The analysis uses the concept of epistemic ownership as an interpretive framework, focusing on three dimensions that can become visible in interaction: Direction, Integration, and Evaluation. The results show that similar assessed performance can coexist with substantially different observable patterns of cognitive participation. Assignment scores therefore provide information about assessed outcomes but do not recover how cognitive work was distributed between student and AI during the interaction. The study provides an empirical basis for asking how assessment should incorporate evidence about cognitive process, rather than relying exclusively on the quality of the resulting product.

Growing volumes of educational real-world data (ERWD) are being collected across learning platforms and institutional systems. Sharing these data within the Learning Analytics community offers substantial research opportunities, yet access remains limited by ethical, regulatory and governance constraints. Prior work has primarily focused on anonymisation techniques, but little attention has been paid to operational design of practical ERWD sharing, particularly how data custodians and researchers interact through privacy-preserving access mechanisms. To address this gap, we propose ReLEAF, a socio-technical framework that bridges data custodians and researchers by operationalising two-stage data sharing: 1) Differentially private synthetic data are shared for exploratory analysis, and 2) controlled real-data validation is performed on demand. Following a design-science research approach, we refine and formatively evaluate ReLEAF through three cycles involving 4 graduate students, 6 researchers, and 90 undergraduate students, respectively. Three design principles emerged through the cycles: P1) position privacy-preserving access mechanisms within the research workflow, P2) make the conditions for acceptable secondary use explicit and actionable, and P3) promote engagement with governance requirements rather than automate compliance decisions. Together, ReLEAF provides a concrete framework for trustworthy ERWD sharing, while the design principles offer transferable guidance for other data-sharing contexts.

AI can help adults produce plausible workplace artefacts while leaving their independent reasoning difficult to inspect. LearnAdapt Praxis addresses this design problem through a five-stage learning workspace: Frame, Learn, Build, Validate and Transfer. The application stores an initial response, versioned specifications, coaching records, validation observations, a separate transfer response and facilitator feedback. Project-level server controls restrict coaching, access to earlier work and learner export during an active transfer attempt; they do not establish that a learner avoided assistance outside the application. This technical report describes the implemented architecture and evaluates selected workflow, authority, failure-recovery and provenance properties. A reproducible synthetic rehearsal exercised 120 serial project episodes across three fictional workplace contexts and four injected provider conditions. All 3,990 recorded checks met their specified expectations. The resulting records contained 240 specification versions, 1,200 activity events and 60 stored synthetic hints; 60 injected provider failures produced no fabricated hints. Separate authentication regressions exposed and resolved a session-expiry defect, and limited staging and production checks verified account access and live model connectivity. The evidence supports the tested engineering properties of the recorded release. It does not establish learning gains, model-output quality, unaided assessment validity, population usability or production capacity. The contribution is an implemented arrangement for separating assisted work, project-level assistance withdrawal and inspectable evidence, accompanied by executable tests and an explicit account of what remains to be validated with adult learners.

Technology-facilitated abuse (TFA) is an emerging gendered public health crisis affecting millions of people across the globe. TFA coincides with other forms of gender-based violence (GBV), such as emotional, psychological, and physical abuse by an intimate partner. Survivors often seek support from GBV advocates who specialize in helping survivors navigate abusive situations and potential pathways for healing and remediation. With the rise in TFA, however, survivors and GBV advocates experience barriers and gaps in knowledge in identifying and mitigating this newer, ever-evolving form of abuse. To address this, we developed trauma-informed training as a critical capacity-building intervention to improve GBV advocates' knowledge and skills for responding to survivors and to encourage multi-stakeholder collaboration toward a structural response. We facilitated 8 training workshops at local, state, and national US-based GBV organizations. Our study demonstrates that the trainings increased advocates' perceived understanding of TFA abuse vectors and their impacts on survivors, along with advocates' confidence in providing support, safety planning, and referrals through collaboration with community stakeholders. We then conducted retrospective reflections through autoethnography to surface key lessons, contestations, and tensions that emerged from our experiences in designing and coordinating trainings with host GBV organizations. Our analysis of autoethnographic notes revealed three key design decisions: coordinating logistics flexibly, reducing reflexive distance from host organizations and attendees, and using our field experience to tailor content for accessibility. We contribute a concrete checklist to guide future HCI scholars in conducting training interventions for stakeholders in high-stakes contexts.

Professionals are increasingly turning to artificial intelligence (AI) for career advice. We tested whether large language models (LLMs) provide different advice to female and male job candidates in the context of career negotiations. Across two large-scale audit experiments, including four leading models, the LLMs not only recommended lower salary targets for female than male personas, but also constructed distinct negotiation agendas, prioritizing flexibility, caregiving, and well-being more highly for female candidates, and financial rewards, mobility, and advancement more highly for male candidates. These differences closely tracked independent human ratings of employment terms associated with primary caregiver and breadwinner roles, respectively. Our findings identify a subtle pathway through which personalized AI advice may reinforce gender inequality at work: steering otherwise equivalent users toward traditional gender roles.

Online students often face emotional challenges such as frustration, isolation, and fluctuating motivation, which can hinder sustained engagement in online learning. While affective chatbots have shown promise in healthcare and wellness, most learning support chatbots focus primarily on cognitive assistance with limited attention to learners' emotional and motivational experiences. In this work, we investigate how adaptive affective chatbot support can be designed for online learning through an iterative design process. Through a mixed-methods needfinding study (n=38), we identified substantial variation in how learners respond to affective support, with no single strategy universally preferred. This led us to develop three adaptive design alternatives: user-controlled, AI-driven, and hybrid co-controlled adaptation, and examine key trade-offs between automation, transparency, and user control. A final evaluation (n=25) showed that 88% of participants favored the adaptive hybrid design over a non-adaptive alternative, highlighting the value of balancing transparency, user control, and interaction simplicity. We discuss design implications for adaptive affective chatbot systems in emotionally challenging online learning contexts.

Online education has enabled learners worldwide to take up courses from reputed institutions. However, a self-paced online course cannot guarantee the motivation and engagement that a learner experiences in a real-time classroom. Self-paced access also makes platform load unpredictable, which drives up the compute cost. We propose a Commitment Scheme for course access that aligns learner commitment with platform capacity. Learners book their study slots in advance; the instructor sets the budget of learning hours available for the course; and learners who use a full window earn additional watch hours. A booked window records an intention to study at a stated time, and a learner who appears in that window implements it. The byproduct is a platform load that can be forecast and bounded. The study involved two courses taken in sequence by the same learners, with the slot booking system activated only in the second. In-window study was observed on 86.8% of booked windows, and the median committer placed 95.5% of all study time inside self-booked windows. Among learners who studied across the launch, study regularity improved from 1.01 to 1.33 active days per week, with a supporting difference of +1.37 days per week against the same learners' preceding course. Commitments made on the same day as the study slot were honoured more often than advance bookings (88.8% against 62.5% two days ahead), which is consistent with the classic intention-behaviour gap.

Research on information operations has focused primarily on the coordinated accounts involved in such campaigns and in the content they promote. Bad actors manage the activity of inauthentic accounts through programmatic interfaces (APIs), but the role of this underlying infrastructure in enabling and uncovering such coordinated behaviors remains largely unexplored. This study investigates how the Twitter (now X) API was used to coordinate influence operations on the platform. Since API access is managed through developer apps, we extract app metadata from 43 operations taken down between 2018 and 2021 and apply three complementary computational approaches to study diverse forms of manipulation. Our analysis reveals several recurring patterns: API-as-a-service infrastructure reused across campaigns, gaming apps performing unauthorized engagement without user consent, spoofed app names to hide activity origins, and synchronized app networks indicating centralized control. Adding app usage as a coordination indicator provides a modest but consistent improvement in detection. These findings suggest that social media APIs play an important role in enabling online manipulation, and that the removal of app-level metadata by Twitter/X has eliminated an important tool for platform transparency and moderation.

Metropolitan concentration has motivated policies for more balanced regional development. We use agent-based simulation to examine trajectory diversity, the variety of people's recurring activity orientations toward neighborhoods, urban centers, and wider networks. The model maps 2010 population shares to 2026 distributions in five country cases, China, Russia, Japan, the United Kingdom, and the United States, and simulates change to 2036. Trajectory diversity declines by 52.6-79.0% over 2026-2036. Russia and Japan show the largest increases in the share of the most common trajectory, while US diversity contracts despite a slight decline in this share. Cost burdens rise throughout, whereas welfare falls in the Russian and Japanese cases but rises in the other three. These findings suggest that regional development should be assessed through activity diversity, cost burdens, and welfare together.

LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational aggregates. We introduce O*NET-BENCH, an audit suite derived from an existing survey of 45,796 worker ratings, and evaluate 33 pre-existing judge configurations across six model families on 4,501 test ratings. Twenty-five configurations achieve tie-aware pair accuracy of at least 0.60, although a train-fitted response-only TF-IDF baseline nearly matches the strongest judge. Despite this ordering agreement, judges estimate that 3.0%-97.9% of responses are acceptable, compared with 61.1% for occupation-matched workers. In one fine-tuned lineage, changing from pointwise scoring to a bundled few-shot/listwise protocol improves response ordering while reducing agreement with worker means at the task and occupation levels; this reversal replicates on a task- and worker-disjoint validation split under prespecified criteria. Cross-validated calibration largely removes mean bias, but calibrated scores explain at most 8.5% of individual worker-rating variance. Prediction-assisted estimation yields at most small precision gains at the studied label budgets. These results show that ranking agreement alone is insufficient for occupational measurement. Judges should be validated against the acceptance rates and aggregates their scores will be used to estimate.

We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based chatbots. By examining the state of the art in social bot detection techniques and the more salient real-world application to date, we identify gaps and emerging trends in the field, with a focus on addressing the unique challenges posed by AI-generated conversations and behaviors. We suggest potentially promising opportunities and research directions in social bot detection, including (i) the use of generative agents for synthetic data generation, testing and evaluation; (ii) the need for multimodal and cross-platform detection based on network and behavioral signatures of coordination and influence; (iii) the opportunity to extend bot detection to non-English and low-resource language settings; and, (iv) the room for development of collaborative, federated learning detection models that can help facilitate cooperation between different organizations and platforms while preserving user privacy.

Personalized social media commonly relies on explicit follow graphs to shape what content users encounter; yet how much attention crosses ties they have not formed remains largely undocumented at scale. We study this question on Bluesky, a large decentralized microblogging platform whose default feed relies on a simple, reverse-chronological content recommender. We analyze 173 million user-author interactions (likes, reposts, replies, and quotes) collected from a near-complete platform dump between February and September 2023. We decompose each interaction by attention-path length (already followed, relayed by a followed account, reachable within two follow-hops, or beyond) and find that 74.5% of interactions reach the user through an account they already follow. Measured by distance in the follow graph rather than by route, 80.6% of interaction lands within two follow hops, far beyond the 22.4% an expected-degree null predicts. We then characterize how exploration varies across users and over tenure. A broad-reaching minority generates three quarters of all exploratory activity, while aggregate declines in exploration with tenure mask three distinct individual trajectories. Finally, attention reaching beyond two hops converts into new follow ties at less than one third the rate of two-hop-local exploratory attention. Together, these results depict a platform where out-of-network exploration is substantial in volume but strongly constrained by network proximity and unlikely to translate into new social ties.

Topological Data Analysis (TDA) offers novel methods for understanding temporal dynamics in complex systems, yet its application in information systems design faces a fundamental challenge: how should systems present analytical outputs when interpretation frameworks are still developing? This paper reports on the development of TopoLA, a dashboard system applying Zigzag Persistent Homology to learning management system data, and proposes three early design principles for interpretation support in emerging analytics: (1) separation of objective measurement from contextual interpretation, (2) graduated disclosure from metrics through patterns to reflective prompts, and (3) explicit acknowledgment of methodological uncertainty. The system implements a modular three-stage pipeline–feature extraction, topological computation, and interpretation support–enabling extension to additional analytical methods. This work contributes to information systems research by articulating preliminary design knowledge for systems that must communicate analytical insights from methods lacking established interpretation norms–a challenge increasingly common as novel computational techniques enter applied domains.

Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. $\beta_0$, the number of disconnected behavioral clusters from Zigzag Persistent Homology, serves as a cohort-level indicator of this structure. Two questions remained unverified at scale: (1) does apparent $\beta_0$ convergence reflect genuine behavioral alignment or learner dropout? and (2) do assessment deadlines produce reproducible fragmentation-convergence cycles? We address both across all 22 OULAD courses (N > 22,000; 857 week-pairs). Changes in $\beta_0$ strongly co-vary with active learner changes (pooled r = 0.387; median per-course r_delta = 0.459, 20/22 courses), identifying $\beta_0$ as a participation-sensitive indicator: $\beta_0$ and active learner counts co-respond to deadline events rather than one causing the other. Deadlines produced fragmentation in 82.6% of assessments and the full Fragment First, Converge Later (FFCL) cycle in 60.2%. 3-phase analysis confirmed structural fragmentation as the dominant long-term trajectory (90.9% of courses), moderated by curriculum structure. These findings establish $\beta_0$ as a participation-sensitive structural indicator with direct implications for AI-augmented learning analytics design.

Artificial intelligence presents an unusually difficult problem for global governance. The technology develops rapidly, crosses borders easily, and is shaped by actors whose resources and capabilities may rival those of states. Yet international responses remain fragmented, unevenly representative, and overwhelmingly non-binding. The challenge is therefore not simply to identify appropriate rules or institutions, but to understand who has the capacity and incentive to create, enforce, and adapt them. This review essay examines these questions through Matthijs Maas's Architectures of Global AI Governance. Maas offers an ambitious framework for thinking about AI governance through the lenses of sociotechnical change, governance disruption, and regime complexity. His account usefully resists both technological determinism and the search for a single institutional blueprint, emphasizing instead the possibilities of a fragmented and evolving governance architecture. The essay argues, however, that institutional design cannot be separated from the distribution of power. Maas frequently invokes what "we" should do about AI, but that collective subject obscures important differences among states, international institutions, and technology companies. States retain formidable powers over markets, infrastructure, strategic inputs, and firms themselves. At the same time, many consequential decisions about frontier AI - what is built, how quickly, with what safeguards, and when it is released - are concentrated within a small number of private companies. The central problem of global AI governance may therefore be less architecture without an architect than an emerging architecture shaped by multiple actors possessing different forms of power, divergent incentives, and no common set of plans.

Decentralized finance (DeFi) vaults are smart-contract-based asset management systems that pool deposits, execute programmable strategies, and mint tokenized shares representing claims on underlying assets and strategy performance. As vault designs have evolved from early yield aggregators to modular, actively managed systems, a new control layer, curation, has emerged to select strategies, configure risk parameters, and coordinate operational execution, introducing principal-agent dynamics and new failure modes. This paper systematizes DeFi vault architectures and curator-mediated control planes through (i) a unified system model and formal definitions for share accounting, roles, and operational dependencies, and (ii) three complementary taxonomies covering vault exposures and objectives, curator governance and accountability mechanisms, and strategy execution patterns together with their failure modes. We further map a representative set of production protocols to the proposed dimensions. The frameworks in this work aim to support rigorous analysis and safer design of blockchain-based financial applications.