Papers

Generative artificial intelligence (GenAI) is increasingly being deployed across a wide range of real-world applications. Without careful evaluation, reliance on these systems can have unintended consequences, such as reinforcement of stereotypes and amplification of social biases. Such risks are particularly important in educational settings, where early decisions can shape students' interests, opportunities, and career trajectories. In this paper, we investigate how GenAI use by software engineering instructors may inadvertently reinforce software-specific social biases. We focus on two representative tasks: team formation based on student profiles and the generation of visual content for educational materials. Our results reveal significant biases in both tasks. In team formation, factors such as gender and nationality affect role assignments (e.g., women are more likely to be assigned to front-end roles than equally qualified men). In the visual content generation task, models generally produce diverse and balanced representations when depicting groups of individuals. However, when generating images of a single person, the outputs are predominantly male and light-skinned. These findings highlight the persistence of demographic biases in generative models and underscore the need for domain-specific evaluation and mitigation strategies to support their responsible use in educational settings.

Large language models are increasingly used for content moderation, but most evaluations still report aggregate accuracy on individual benchmarks. We introduce Safety-Flag, which places seven widely used safety benchmarks (BeaverTails, XSTest, Ethics, WildGuard, Aegis, ToxiChat, and ToxiGen) into a single balanced flag / do-not-flag protocol. We release item-level decisions and confidence scores for six general-purpose LLMs and four dedicated guards, together with three reference models, evaluated on the same items. Safety-Flag measures three dimensions of moderator reliability: error direction, probability calibration, and confidence-based error ranking for human review. They often disagree. Aggregate accuracy does not reveal error direction: one model flags $85\%$ of benign content, whereas another misses $54\%$ of harmful content. All six general-purpose models are overconfident; fitting one temperature per model reduces calibration error by $2.8$–$6.0\times$ without changing predicted labels or confidence ordering. Confidence-based abstention lowers selective risk for every model, although the gains depend on how well confidence ranks errors. Dedicated guards produce fewer false alarms and are better calibrated, but several have higher miss rates outside their documented coverage. We release the benchmark, fixed item lists, evaluation code, per-item model outputs, and leaderboard at: https://github.com/yibo-hu-lab/safety-flag-benchmark.

Institutions use commercial AI detectors for academic integrity, yet detectors cannot distinguish AI editing from full LLM drafts and may treat both as misconduct. In a controlled study of published English abstracts (four domains; 2013 to 2015 vs. 2023 to 2025), we quantify this policy failure under proxy human/AI labels at tau=0.50. Light "refine abstract only" edits, a proxy for guideline-compliant AI assistance, are flagged at 38 to 80%. Unmodified 2023 to 2025 originals are flagged at 9 to 15%, with non-STEM rates far above STEM (p<0.001); elevated scores track long-token and Academic Word List density, not authorship intent alone. After Undetectable AI humanization, evasion is near-total: fewer than 4% of AI-labeled rewrites remain flagged (post-humanization detection rate <4%; FNR >96%). Honest AI-editing results in a higher sanction risk than humanizer-assisted evasion. Therefore, detector scores should not serve as standalone misconduct evidence.

Students learning to program benefit from access to large numbers of practice problems. Autograders are commonly used to support programming questions by providing quick feedback on submissions. But authoring accurate autograders remains challenging. Autograders are frequently created by enumerating test cases–a tedious process that can produce inaccurate autograders that fail to correctly classify submissions. When authoring accurate autograders is slow, it is difficult to create large banks of practice problems to support beginning programmers. We present solution-generated autograding: a faster, more accurate, and more enjoyable way to create autograders. Our approach leverages a key difference between software testing and autograding: The question author can provide a solution. By starting with a solution, we can eliminate the need to manually enumerate test cases, validate the autograder's accuracy, and evaluate other aspects of submission code quality beyond behavioral correctness. We describe Questioner, an implementation of solution-generated autograding for Java and Kotlin, and share experiences from four years using Questioner to support a large CS1 course: authoring nearly 800 programming questions used by thousands of students to evaluate millions of submissions.

LLMs are increasingly being applied to tasks involving institutional and political texts, but existing benchmarks evaluate them on isolated documents or single tasks. In realpolitik, negotiations are longitudinal data, where participating parties can align or argue over multiple iterations and each turn is an outcome of the previous turns, hence, understanding one turn requires tracking everything before it. We introduce TradeVerse, a benchmark built from the World Trade Organisation (WTO) specific trade concerns, where member states challenge one another and exchange arguments over multiple rounds, sometimes for years. We, in TradeVerse, reconstruct minutes of $1170$ meetings, spanning across 5 groups and $89$ product groups and define three tasks: first, the system has to analyze the longitudinal meeting records and predict the harmonized system codes (HS chapters) of the products under discussion in the particular meeting, second, we examine whether the system, upon analyzing the anonymized content of the meeting, can guess the name of the responding country and third, we ask the system to play the role of the responding country and provide the statement for the very last round. All labels are recovered directly from the proceedings, requiring no manual annotation. Our experiments highlight the challenges these tasks pose for current LLMs. To the best of our knowledge, TradeVerseis the first benchmark to investigate potential of LLMs in understanding longitudinal political trade negotiations.

Agentic AI promises systems that can act on users' behalf, from filtering content to negotiating prices to selecting services. Whether it will empower users is an open question, and one that depends on more than the technology. We conduct a comparative case analysis of four earlier, more mature domains in which similar forms of agency emerged: browser-based ad blockers, platform recommender systems, financial robo-advisors, and email spam filtering. Across the cases, questions about whose interests agents would serve were resolved through technical arrangements: API choices, protocol governance, industry standards, and default configurations. Beyond their technical form, these were political decisions. We identify this settling of contestable questions in a technical form as depoliticization, a concept from political theory, here at work in technological systems. Its most consequential effect is that individual outcomes and collective contestation capacity can move in opposite directions: spam inbox quality improved substantially while the organized capacity to contest spam governance collapsed. Where intermediary institutions sustained formal channels for challenge, user-aligned agency proved more durable; where proprietary infrastructure and closed standard-setting absorbed contestation, the material basis for user-aligned alternatives was dismantled, and the loss proved hard to reverse. Applying this lens to agentic AI, we find a similar pattern forming: governance is consolidating around the Model Context Protocol and the Agentic AI Foundation, an industry-governed venue already deciding what agents will be able to do. Unlike in the completed trajectories, these decisions have not yet hardened, and remain open to challenge by users and the public.

Unmanned Aerial Vehicles are now widely used across business, government, and recreational contexts, creating new challenges for incident response and digital forensics. While previous forensics research has largely focused on the extraction of technical data from UAV systems, minimal empirical work has examined how UAV incidents are handled in real-world settings or what challenges incident handlers face during this response. To address this gap, this paper reports findings from an in-person focus group with UAV and counter-UAV practitioners from industry and government organizations in the United States. Using qualitative analysis, several key challenges are identified, including situational awareness and airspace visibility, fragmented reporting and interorganizational coordination, forensic and attribution limitations, legal and policy gaps, and shortfalls in training and operational capacity. The research extends socio-technical incident response research to the UAV domain by providing practitioner-driven insight into UAV incident response and highlighting opportunities to strengthen incident response in this domain.

The use of e-commerce mobile applications is expanding in Nigeria, creating both opportunities and risks, including fraud and reduced user control over digital technologies, raising concerns about digital sovereignty. This research examines how Artificial Intelligence (AI) in Nigerian mobile applications affects digital sovereignty, examined through platform transparency as a key indicator of user awareness and control. Using an interpretive approach, the research combines the forensic analysis of selected Android applications with contextual document analysis to identify AI features and evaluate disclosure practices. The findings show that AI is widely implemented in the applications, yet transparency about its use remains limited. A socio-economic analysis of Nigeria further shows an increasing dependence on consumer digital platforms, moderate AI awareness, and uneven patterns of interaction. By providing empirical evidence on AI transparency and platform practices, this study advances understanding of individual digital sovereignty and highlights challenges for protecting user control in AI-driven digital environments.

Higher education remains largely reactive in its approach to student success. Institutions frequently identify academic problems only after students have failed courses, fallen behind in degree progression, accumulated excessive debt, or departed without a credential. Healthcare faced a similar challenge decades ago. It responded by shifting from reactive treatment to preventive care powered by predictive models, risk stratification, electronic health records, and artificial intelligence (AI). This paper argues that higher education stands at an analogous inflection point. Drawing on advances in learning analytics, educational data mining, machine learning, workforce analytics, and digital twin technologies, we propose a paradigm we call Precision Education. Under this framework, AI continuously analyzes academic, behavioral, financial, and career data to identify emerging risks, recommend personalized interventions, optimize educational pathways, and align academic decisions with long-term career success. Central to the model is the Student Digital Twin, a continuously updated representation of a learner that can simulate multiple educational futures and intervention scenarios. We ground the vision in evidence from early deployments such as Course Signals at Purdue and GPS Advising at Georgia State University. We also argue that prediction alone is insufficient. The central methodological challenge is the move from prediction to causal, actionable intervention. The paper presents a conceptual framework, examines enabling technologies, reviews the empirical record and its limits, analyzes ethical and governance implications, and outlines a research agenda for the next decade of AI-enabled higher education.

The article reports on a blind Turing Test experiment, assessing the performance of out-of-the-box leading LLMs on three Italian legal professional exams: the Bar, Judges and Notary exams. Leading LLMs were asked to generate full written exam papers, which were made indistinguishable from human submissions and anonymously evaluated by expert examiners, using the same criteria applied in real examinations. Results reveal marked differences across both models and tasks. While some LLMs match or exceed top human performance in adversarial legal argumentation and doctrinal analysis, all models fail in the notary exam, which requires goal-directed legal planning under strict formal and substantive constraints. Beyond ranking models, the study identifies task-specific strengths, limitations and recurring legal failure patterns. Although limited to out-of-the-box systems, the findings provide qualitative evidence on the current scope and boundaries of the legal competence of LLMs across distinct professional tasks.

This paper focuses on automatic speech recognition (ASR) and ASR-mediated voice interfaces that shape access to public services, healthcare, and education. We argue that persistent failures for low-resource, Indigenous, and non-standard language varieties are not only technical errors, but also implicit linguistic policies that reproduce colonial language hierarchies. Drawing on linguistic capital, raciolinguistic ideology, language policy research, and decolonial computing, we show how data, metrics, and model priors determine whose voices become machine-legible. We introduce the Three Harms (3M) taxonomy—Misrecognition, Misalignment, and Mistrust—and a seven-layer situatedness model for linguistic diversity in ASR and ASR-mediated voice interfaces. We then propose a participatory framework and minimum audit protocol for culturally competent ASR, positioning affected communities as co-designers, evaluators, and governance partners.

This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account of why this matters. We audit two commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal). For each system and genre, we generate 100 tracks and compare them against human corpora of equal size, using 72 music information retrieval (MIR) features and multiple diagnostics of dispersion, redundancy, and separability. We define homogenization as reduced acoustic variation in standard computational audio features including rhythm and timing, timbre/spectral shape, and dynamics, both within genres and across genre boundaries. We also generate tracks using only a genre name as the prompt, with no additional instructions, to reveal each system's default musical tendencies. The results show two structurally distinct homogenizing tendencies. Lyria reduces within-genre acoustic diversity, while Suno collapses the acoustic distinctions between genres without compressing within-genre spread. Neither system follows user prompts faithfully, indicating that the observed patterns reflect learned priors rather than prompt constraints. The two systems do not converge on a common acoustic profile and are more acoustically distant from each other than two random human subsamples would typically be. Nevertheless, a standard classifier distinguishes AI from human tracks near-perfectly on MIR features alone. We argue that these patterns matter not as an aesthetic curiosity but as a justice-relevant condition, shaping which musical styles become legible, valued, and economically rewarded as generated outputs increasingly circulate at scale.

Student mobility programs such as Erasmus+ enable students to take courses at other universities, broadening their academic and cultural horizons. However, this flexibility also leads to a practical challenge: ensuring that students do not take courses elsewhere that substantially overlap with courses in their home curriculum. In this work, we propose CourseGraph, a methodology that automates the evaluation of external courses based on insights obtained from the process followed by curriculum administrators when assessing courses for inclusion in a degree program. Course- Graph extracts information such as course titles, descriptions, and learning outcomes from the course webpage. Then, this information is represented semantically using a BERT-based language model, after which the pair-wise similarity between courses can be computed. This information is then used by a Random Forest classifier to determine whether a candidate course abroad overlaps with a course already contained in the student's curriculum. We evaluate CourseGraph using (1) the Computer Science program at Eindhoven University of Technology, which contains information about courses with substantial overlap, and (2) six approved international programs from students enrolled in the Computer Science program at Lund University, including the corresponding decisions made by a curriculum administrator. The experimental results indicate that CourseGraph provides an effective approach for identifying overlapping courses and supporting curriculum alignment across universities.

As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses' learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow researchers and educators to study and develop future courses.

Large language models (LLMs) can leave small stylistic traces in text written with their help. The most discussed is the em-dash (U+2014), especially the unspaced form word—word, which is normal in typeset English prose but unusual in U.S. press writing, where AP style calls for spaced dashes. This study asks whether that trace is measurable in congressional press releases. In a preregistered design (OSF: 10.17605/OSF.IO/U5NEY), 146,239 scraper-sourced releases from 480 House and Senate offices (2021-2025, the open congress-press dataset) were analyzed: density of unspaced prose-form em-dashes per 1,000 characters of cleaned text, Poisson/negative-binomial models with a length offset, clustering by office. Density stayed within 0.10-0.12 per 1,000 characters through 2021-2024, then rose to 0.217 in 2025, more than twice the four-year baseline; the share of releases with such an em-dash rose from ~13% to 24.8%. The primary frequency ratio (2023-2025 vs 2021-2022) was 1.55 (95% CI 1.28-1.93; exact registered cut-off: 1.528), just above the prespecified 1.5x threshold. The rise was net-new (hyphen density stable), held within authors (75.6% of 262 continuous offices increased; p ~ 1e-16) and in a closed panel of 224 offices, and survived falsification tests: three placebo cut-offs were null, the pipeline showed no step at the 2024/2025 boundary, and continuing offices carried the rise. A segmented regression finds no step at the ChatGPT cut-off but a clear post-period acceleration; the 2025 rise is symmetric across parties and chambers. Because the registered validation gate was formally breached, the full preregistered decision rule was not met; the interpretation (broad diffusion of LLM-assisted writing as the models matured) is offered as exploratory. The em-dash remains a population-level marker, not a per-release authorship detector, and the design supports no causal claim.

AI systems increasingly produce claims about people that shape access to employment, healthcare, and other consequential services. Yet lawful data processing and predictive accuracy do not establish that these claims justify the treatment that follows. We argue that regulation must therefore examine the legitimacy of the inferential step connecting data to claims and claims to decisions. This requires construct, internal, and external validity: evidence must support the meaning attributed to an output, the relationship asserted, and its application to the intended people and setting. We show why predictive performance alone cannot provide this warrant. Non-identifiability limits what observations can explain, while omissions and confounding require domain-specific judgment about what the evidence supports. Building on validity provisions in the EU AI Act and NIST AI RMF Playbook, we develop an explicit evidentiary burden for consequential reliance. Its normative basis follows from the dignity, autonomy, and informational-privacy principles in Puttaswamy \citep{Puttaswamy2017Privacy}, which we extend from scrutiny of information acquisition to the justification of derived claims and their use. On this account, benefits that depend on an inference can carry weight in proportionality assessment only to the extent that the inference is substantiated. We make this requirement operational through a claim-specific Validity Case linking evidence and assumptions to permitted uses, independent review, monitoring, and remedies. Hiring and LLM applications in healthcare and legal assistance illustrate how the framework can guide oversight of consequential public and private services through the relevant legal instruments.

Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as model benchmarks and LLM simulations, often sidelining empirical research with human subjects. To examine this apparent gap in the acceptance of human research, we conduct an expert survey (n=93) and expert interviews (n=17) with AI Safety & Ethics (AISE) researchers from Technical, Sociotechnical, Governance, and Normative backgrounds. Our findings suggest that although there is a consensus that human research is valuable for generating evidence for AISE, its adoption and acceptance are constrained by perceived validity issues, tangible resource barriers, epistemic and personal preferences in methods, and infrastructural constraints from the broader research community. In particular, Technical researchers tend to value human research less and collaborate across disciplines less, suggesting an epistemic tension towards human methods. We propose recommendations for establishing the epistemic fit of human research within AISE and bridging the prohibitive limitations that researchers face, while avoiding performative 'human-washing'.

As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients' own actions contribute to illness. We investigate how LLMs reason about responsibility and its consequences, tracing their judgments across successive levels, from the behavior, to the resulting illness, to the denial of care. We evaluate a wide range of LLMs, spanning different model families and capability levels, on various clinical vignettes adapted from prior studies. Our results identify a judgment-consequence gap: LLMs largely agree with humans that patients bear responsibility for health-harming behaviors, yet overwhelmingly refuse to let that judgment influence how they allocate scarce resources. Specifically, LLMs default to random allocation, whereas humans consistently favor the less-culpable patient. Compared to humans, LLMs also place greater emphasis on access to information, reducing responsibility judgments when health-risk knowledge is unavailable. These findings reveal that LLMs apply a systematically different moral framework than humans when responsibility and resource scarcity intersect, surprisingly often amplifying normative disagreement with humans as reasoning capability increases.

When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizable places and characters. Human-written Harry Potter fanfictions, however, typically include these fundamentals and much more, incorporating stylistically irregular content and relationship-diverse plotlines. This gap between LLM and human writing has been noted across a variety of domains. LLMs tend to produce "average" writing, while human writing contains more diverse content that covers a broader distribution. Existing work has shown the existence of this distributional "gap", but no work has proposed a systematic way to measure it. Our paper proposes a human-grounded framework that uses the empirical distribution of human writing on a topic to measure the distributional breadth of LLM-generated content on that same topic. We propose two metrics, LLM Coverage (LLM-Cov) and In-Boundary Rate (IBR), that separate the plausibility of LLM content from its distributional breadth. Across ideation and narrative tasks, we find that current LLMs produce plausible but narrow content that concentrates near the center of the human response space. Our framework can enable researchers to better assess the distributional breadth of LLM-authored content, which we term its "cultural reach".

Used as a capable servant, generative AI has greatly accelerated intellectual work, yet it also risks eroding human epistemic agency by encouraging uncritical acceptance of AI-generated reasoning. Preserving that agency calls for mechanisms that augment human metacognition during AI-assisted work. We therefore propose the Synthesis-Analysis Reciprocity Model, which views intellectual construction as a reciprocal interaction between two cognitive functions. Synthesis selects and combines the components of the artifact; Analysis evaluates them critically against objective indicators and constrains the Synthesis that follows. Grounded in this model, we present the Vibe Compiler, a research-logic compiler that helps researchers turn vague intuitions (Vibes) into coherent research logic. The system attempts to compile those intuitions against a paper ontology of 16 academic parameters. It treats compilation failures as signs that logical components are missing. Rather than fill those gaps autonomously, it returns reflective questions that prompt researchers to develop the missing reasoning themselves. We further characterize the origins of structural gaps along two orthogonal dimensions: cognitive function (Synthesis versus Analysis) and executing agent (human versus AI). The four resulting types of origin give a principled way to identify where breakdowns in intellectual construction arise. Crucially, our design implements the type in which the AI probes its own synthesized output, itself driven by the user's Vibes, and thereby stimulates human metacognition. This choice raises researchers from passive "Makers" of the output into "Managers" who critically direct and validate what the AI produces. In a prototype on NotebookLM and Gemini, AI behavior depended less on prompting than on the structure of the knowledge supplied. The framework spans a learner layer and a researcher layer.

Vibe-coding systems turn natural-language instructions into deployed websites, but little is known about the diversity of designs produced within a shared production context. We study 73 promotional websites created by graduate students with Lovable for distinct real businesses under a graded assignment that rewarded original design. We represent each homepage using DINOv3 embeddings and analyze pairwise cosine similarity, effective diversity, and descriptive cluster structure. A representative UMAP–HDBSCAN specification assigns 88% of sites to six interpretable clusters, while sensitivity analyses yield between three and twelve clusters. In a process subsample of nine recordings, preliminary AI-assisted coding documents recurring generate-and-check behavior, incomplete transmission of verbalized intentions into prompts, and infrequent explicit evaluation of originality. Among survey respondents, perceived human control, satisfaction, and perceived quality show no detectable association with embedding-based cohort atypicality. These results document within-cohort visual concentration and a mismatch between subjective experience and distributional distinctiveness. We call this condition authored ignorance. Alongside early evidence that the collective cost of generative production reaches visual design, we contribute a measurement strategy that joins outcome, process, and perception for interactive artifacts, and design implications running from originality feedback to deliberate friction.

As generative AI permeates computing instruction, the emergent design challenge is to configure each tool's pedagogical role, authority, and accountability for the instructional work it performs. We argue for instructional governance by design: governance should be encoded in a teaching tool's interaction model, constraints, and workflow. We introduce a multidimensional framework that characterizes AI teaching tools through (1) pedagogical grounding, (2) AI instructional authority, (3) human accountability and control, (4) learner agency and cognitive engagement, (5) context specificity and boundary setting, and (6) evaluation visibility and revision. These dimensions yield governance profiles that help educators align tools with specific purposes and educational stakes. We develop the position through a comparative analysis of a portfolio of AI teaching tools across computing and first-year engineering: rubric-anchored GTA simulations, reflection-oriented code companions, staff-reviewed forum-response systems, TA-supervised diagram generators, and course-specific code-style coaches. These cases show how common instructional functions call for different combinations of rubrics, learning-theory commitments, approval gates, supervision, and course-specific constraints. We further apply the framework to selected published tools to demonstrate its use beyond a single institutional portfolio. From these cases, we identify reusable design questions for aligning governance with instructional stakes, human capacity, and intended learning processes. This position reframes responsible AI integration as a curricular and interaction-design challenge and offers a common vocabulary for tool builders, instructors, and researchers to design, compare, and evaluate AI-mediated learning environments.

Digital Twins for educational buildings can support sustainability-oriented learning, but their use in schools remains limited. This paper presents a GenAI-based chatbot built on top of an existing Digital Twin for two school buildings in Greece, using real IoT data from environmental sensors and energy meters. The chatbot enables educators to query live and historical building data, compare spaces, and generate ideas for classroom activities through natural language. The system was evaluated in an 80-minute workshop with 17 secondary-school educators, who compared it with an existing web-based dashboard. Results show strong perceived usability and pedagogical value, particularly for inquiry-based learning, hypothesis formation, and interdisciplinary lesson planning. Participants also highlighted limitations related to response speed, data verification, trust, and the continued value of visual dashboards. Overall, the findings suggest that GenAI interfaces can make Digital Twin data more accessible for educational use, provided they are designed with transparency, verification, and pedagogical grounding.

Content moderation is a central form of digital governance, yet people disagree over what content should be removed from shared online spaces. While platforms aggregate human judgments to build moderation systems, it remains unclear how this process shapes which users are protected from content they perceive as toxic. We address this gap by combining large-scale judgment data with counterfactual simulations that trace how the demographic composition of moderator pools shapes the distribution of protection across users. Applying this framework to removal judgments from 16,221 U.S. respondents evaluating 102,463 comments from Twitter, Reddit, and 4chan, we find demographic heterogeneities in moderation demand. We further reveal a consistent pattern of in-group protection: reductions in perceived toxicity accrue disproportionately to users who share the demographic identities of the moderator pool. Crucially, moderator pools that mirror the demographic composition of self-identified moderators on Prolific widen these disparities relative to a nationally representative baseline, while even fully representative pools fail to ensure equal protection: Black and LGB users remain underprotected unless they are represented well beyond their population share. These findings show that unequal protection from perceived toxicity can arise structurally from the aggregation of stratified removal standards, making the demographic composition of moderation inputs a key determinant of who is protected online.

An AI scientist should not grade its own homework. Yet in the systems we examined, the agent that reviews the work usually comes from the same model family as the agent that produced it, or at least from the same vendor. Model evaluators are known to favour their own generations. Whether models trained alike also share blind spots is a conjecture, not a settled finding, but if they do, the reviewer inherits the author's. The record of what was flagged and what was waved through often sits in platform logs that nobody outside can replay. We present CrossAudit, a protocol for supervising autonomous research pipelines. It rests on three commitments. Each increment of work is audited by an agent from a different vendor against a rulebook a human wrote and versioned. Reports, verdicts, disputes and rulings are git commits, so the supervision history can be re-read and cited; raw model exchanges are not yet part of that record. Scripted checks run before any model does. Advisory judgement never gates the pipeline: a model blocks only by citing a rule, and no model may waive a deterministic failure. Blockers that survive a bounded number of revision rounds go to a person. We state the protocol as eight invariants. We describe a reference implementation built from GitHub Actions and a few hundred lines of Python, and report a live deployment of a closely related variant in a computational-chemistry pipeline. We also ran a seeded-defect trial (30 increments, 43 seeded defects, one run per configuration). A cross-vendor audit of our own repository then voided its blinding. We adopt that audit's findings and report the corrected results. The trial shows that two vendors read the same rulebook differently. It does not show that either is better. The strongest evidence here is the committed, uncontrolled record of cross-vendor audits of this paper itself.

A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppresses, or replaces its proposed actions. We contribute runtime action interference (RAI), an AI control mechanism that preserves policy parameters while regulating action pacing and filtering configured action patterns after inference. RAI releases a proposed action only when its cooldown condition is satisfied and its content detector does not flag the action; otherwise, it dispatches a no-op. The detector covers specified toxic behaviors, including worker-unit harassment, while the cooldown controls action rate. We implement RAI in a replication of AlphaStar actor.py and make the implementation and reproducibility materials available through an open source code repository. We deployed RAI in a StarCraft~II human participant study that compared two presentations of the same opponent with high capability and rate limited actions; we withheld its capability claim in one presentation and disclosed it in the other. On response scales from 1 to 5, we observed pooled fairness, trust, and toxicity means of 3.90, 3.50, and 2.00 under claim withholding, compared with 2.62, 4.31, and 2.85 under disclosure. Disclosure corresponded with lower perceived fairness and higher perceived toxicity across every expertise group, whereas trust increased among novices and experts but decreased among intermediate participants. Our human evaluation therefore shows that perceptions of an opponent controlled through RAI can vary substantially with the capability information presented to users, even when the configured control remains constant. We conclude that human-computer evaluations must separate control within the execution stack from capability disclosure and assess fairness, trust, and toxicity as distinct dimensions of human experience.

The growing integration of general-purpose AI chatbots into people's daily lives has raised concerns about the potential for unhealthy dependence, particularly among young adults. As a first step toward understanding and characterizing AI chatbot dependence from the perspective of young adults, we collected testimonials from AI chatbot users aged 18 to 25 through an online questionnaire to capture their thoughts and experiences with this phenomenon. From participant responses, we identified three contributing factors of AI dependence: chronic use, efficiency, and delegation. The combination of these in a person's interaction behavior was considered to indicate AI dependence. Participants also observed feelings of atrophy in abilities from AI dependence, leading to psychological impacts such as feelings of inadequacy. Interpreting these findings through the lens of self-determination theory reveals how AI chatbot dependence can impact young adults' personal and social development. We argue that preventing lasting harm to young adults' development is paramount, and provide implications for rethinking AI chatbot dependence grounded in this understanding.

Generative AI has infiltrated every stage of the research lifecycle: how scholarship is conducted, written, published, and reviewed. Recent policy responses, such as ACM's authorship policy, address an immediate concern about responsible and transparent disclosure of AI use. We argue that a focus on authorship and disclosure, although necessary, risks obscuring and ballooning a set of entrenched problems and strains within publication systems. The central question is not about how papers and other research artifacts should incorporate AI, but how scientific communication itself should evolve when all relevant parties (authors, reviewers, readers) may rely on AI assistance. We draw on our experience within these and other roles to illustrate two contrasting but feasible visions of 2036 with four entwined questions, namely about the purpose of papers as artifacts, reviews, human reviewers, and the incentives that bind all of them. We argue for a shift from policing GenAI and other disruptive technologies to building the infrastructure of provenance, calibration, and accountability that would make trustworthy scholarship the default. We conclude with three grand challenges and invite the community to a broader conversation and research pathways.

School network reorganization is a strategic planning problem that requires balancing demographic trends, territorial accessibility, educational requirements, and institutional constraints while ensuring an efficient allocation of public resources. This paper proposes an optimization framework for school dimensioning decisions based on a novel Integer Linear Programming formulation integrating geographical, administrative, and educational criteria. A synthetic benchmark generator is introduced to evaluate the scalability and computational performance of the model on artificial instances, while a real-world case study involving the complete public school network of the Calabria region (Italy) is conducted using actual institutional, territorial, and demographic data. Furthermore, the model is implemented within a hybrid quantum optimization environment. The results show that the proposed formulation can be effectively solved by exact classical optimization and can also be represented and evaluated within a hybrid quantum-classical optimization framework. The computational experiments provide a proof-of-concept assessment of the applicability of hybrid quantum optimization to the considered school aggregation problem, while also highlighting the current computational advantage of classical optimization and the limitations of the tested benchmark instances.

Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions. We train fourteen models from 135M to 14B parameters across four architecture families on Psych-101, a dataset of 10.7 million trial-level choices from 160 experiments. For in-distribution simulations, scale barely matters. The models fall within a narrow band, as though against a ceiling, and 0.6B to 1B parameters suffice to match a 70B baseline on held-out participants. Out-of-distribution, that band opens into a markedly steeper scaling gradient, with larger models clearly advantaged in generalisation to novel task structure. To determine what information these models use, we run two diagnostics. We progressively strip four prompt channels – task instructions, experimental stimuli, outcome feedback, and choice history – across 27 experiments, and permute trial order. Masking the content of stimuli and feedback destroys 75.7% of learned information and pushes models below chance, demonstrating that choice history alone does not account for performance. Permutation reveals invariance on tasks with independent trials but sensitivity where trial order is determined by prior responses. Small cognitively fine-tuned models therefore show promise as noise ceiling estimators for psychological experiments, though their scope remains bounded by the paradigms seen in training.