cs.CY — Pith

Top Pith

2

cs.CY 2026-05-05

AI markets will pay premiums for verified human presence

by Erin McGurk, David Khachaturov

Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets

As synthetic substitutes erode middle-tier knowledge work, governance must treat provenance verification as labor infrastructure to support

abstract click to expand

We argue that AI-saturated markets are likely to create Veblen-good premiums, which we term human-provenance premiums, for verified human presence, and hence AI governance should treat human-provenance verification as labor infrastructure. Generative and agentic AI systems lower the cost of many standardized cognitive, creative, and coordination tasks, weakening the scarcity premiums that have supported much middle-tier knowledge work. We argue that this pressure may produce an asymmetric barbell-shaped structure of value capture in advanced economies: high-volume synthetic production controlled by owners of AI infrastructure at one pole, and scarce, high-status human labor valued for verified human presence at the other. We advance three claims. First, AI compresses the value of standardized middle-tier labor by making good-enough synthetic substitutes scalable at low marginal cost, hollowing out the middle of the skill distribution currently categorized by knowledge work. Second, this compression reallocates demand for human labor toward work valued for its visible human character. We term this performative humanity and distinguish three forms of labor: relational presence, aesthetic provenance, and accountability. Third, as these premiums depend on credible verification, AI governance should treat human-provenance systems as labor infrastructure rather than as luxury authenticity labels. To evaluate hybrid human-AI work, we propose constitutive human presence as the relevant standard: human labor retains premium value when human judgment, attention, accountability, authorship, or relational participation is not incidental to the output but constitutive of what is being purchased.

0

cs.CY 2026-07-03

Collaborative traits decide who beats AI models in forecasts

by Vivienne Ming

Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting

Prediction market data shows most people match or lag the model, while those high in humility and curiosity reach or exceed market accuracy.

abstract click to expand

Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding (i.e., lower error than) the market itself. Collaborative traits (perspective-taking, intellectual humility, and curiosity) rather than raw cognitive ability or model benchmarks, distinguished who reached that mode. The results are preliminary but statistically robust, and motivate a pre-registered replication now in preparation.

0

cs.AI 2026-07-03

Gemini matches experts grading bash commands with rubrics

by Manuel Alonso-Carracedo, Ruben Fernandez-Boullon +3 more

Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach

Agreement hits 0.89 on basic questions but drops at higher complexity levels, guiding when to mix AI and human review.

abstract click to expand

Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation. This paper evaluates whether four frontier Large Language Models (GPT, Claude Opus, Gemini, and GLM) can approximate expert judgment when grading short Linux/bash command responses. The study adopts a four-level cognitive taxonomy that combines cognitive complexity and operational impact, ranging from information retrieval (L1) and basic file manipulation (L2) to structural operations (L3) and advanced system management (L4). The models were tested with two prompt variants, a minimal baseline and a rubric-enhanced version, on 1200 real responses from second-year Computer Engineering students independently graded by three expert instructors. Gemini~3.0 Pro with rubric-guided prompting achieved the highest human-AI agreement (ICC(3,1) = 0.888, MAE = 0.10, Bland-Altman bias = -0.014). Agreement declined consistently as taxonomy level increased, with the largest discrepancies at higher levels. Across all models, rubric quality had a larger effect than provider choice, with structured prompts consistently improving agreement. These results show that question complexity is a reliable predictor of the difficulty LLMs face in grading accurately, and they establish a principled, taxonomy-based framework for determining which questions are suitable for AI-assisted grading and which require human review, while also providing a transferable evaluation protocol and prompt templates.

0

cs.CY 2026-07-03

Perceived agency gains drive sustained AI chatbot use

by Ian Beacock, Rachel Xu +7 more

AI usage patterns are shaped by perceived gains in human agency

Ethnographic interviews show users keep engaging because they feel more capable, outweighing accuracy or reliability worries.

abstract click to expand

As conversational AI systems become more deeply integrated into daily life, the implications for human agency are increasingly urgent to understand. AI's potential to amplify capability sits alongside risks of individual and collective disempowerment, yet empirical, ecologically-valid evidence about cumulative usage is scarce. We analyze deep ethnographic data from a study of daily AI chatbot users (n = 51) in the United States, Germany, and Singapore to illuminate conversational AI usage in situated context as a sociotechnical practice. We show that people consistently link sustained AI usage to perceived gains in individual agency. Crucially, these perceived gains often outweigh concerns about accuracy, reliability, and consistency to shape usage patterns. Our findings challenge prevailing assumptions about how and why humans use AI systems over time, suggesting that traditional trust-based models are not sufficient for explaining human behavior with conversational AI. Finally, we expose a critical tension: immediate psychological boosts to perceived agency may not necessarily translate into material effects, structural empowerment, or long-term capacity. Our results help establish a new foundation for novel behavioral frameworks, measurement tools, and AI benchmarks to ensure conversational AI strengthens human agency in substantial, sustained ways.

0

cs.AI 2026-07-03

Multi-agent swarm routes emotions to specialized wellness agents

by Seren Yenikent, Jack Vinijtrongjit +1 more

Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support

Copewell integrates data sources and dual interventions to reach users where single-mode tools fall short.

abstract click to expand

Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users' dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. Our architecture introduces three technical innovations: (1) a multi-source assessment framework integrating self-reported, physiological, and contextual data to mitigate algorithmic bias; (2) valence-arousal emotion mapping using Russell's Circumplex Model of Affect to route users to specialized AI agents; and (3) dual-mode intervention delivery combining conversational support with evidence-based sensory wellness protocols. We examine the sociotechnical design considerations underlying Copewell's development, including a privacy-first architecture, embedded ethical oversight through a dedicated Ethics Supervisor agent, and participatory design informed by mental health practitioners. Early practitioner engagement and beta deployment inform design decisions and identify directions for future empirical evaluation. This work contributes to responsible AI discourse by demonstrating how technical architecture can operationalize equity and safety principles from inception.

0

cs.CY 2026-07-03

Eticas taxonomy turns AI risk lists into graded audit results

by Gemma Galdon Clavell, Pablo Accuosto +1 more

The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits

PII leakage test on GPT-4 maps disclosure rates of 0 to 84 percent to grade E

abstract click to expand

The rapid deployment of AI systems across high-stakes domains has created urgent demand for standardized evaluation, yet the field remains fragmented across competing risk taxonomies that catalog risks without showing how an audit is executed. At least 74 AI risk taxonomies exist, and almost all stop at the catalog. The hard part of auditing is not naming a risk but operationalizing it: turning it into a test run against a real system, a measured value, a calibrated severity, and a defensible grade. This paper leads with that bridge. We present the operationalization layer Eticas has built and run, shown end to end on a single risk (PII leakage) against a public benchmark, and then the open taxonomy that makes the method scale. On GPT-4-0314, a disclosure risk that seven external frameworks require be controlled is measured at 0%, 51%, and 84% disclosure as adversarial conditioning increases, mapping through calibrated severity bands to a subcategory grade of E with a SYSTEMIC pattern. Around this example, the Eticas AI Risk Taxonomy v2.0.0 organizes 76 active subcategories across 10 categories and 20 sub-groups, with mappings to 18 external frameworks across compliance, reference, and academic tiers. Its category and sub-group layer is published under CC BY 4.0 as open semantic infrastructure with stable URIs and SKOS/JSON-LD distributions, and a worked subcategory example shows the operational layer down to its severity thresholds. The contribution is the demonstrated bridge from concept to graded finding, anchored by a clean separation of risks from the mechanisms by which they surface, and framed by an open-core model in which the conceptual scaffold is open and the methodology calibration is the practitioner layer. This is the infrastructure the AI auditing field needs: shared, open, and demonstrably operable.

0

cs.CY 2026-07-03

AI regulations drive need for structured risk assessment

by Javier Irigoyen, Roberto Daza +6 more

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

Review of global rules and methodologies identifies best practices plus gaps in managing technical and ethical risks.

abstract click to expand

The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.

0

cs.HC 2026-07-03

Outgroup AI chats raise real cross-party contact odds by six points

by Benjamin Lira, Noah Castelo +2 more

Synthetic Contact with AI Reduces Cross-Partisan Animosity

Ten-minute conversations correct misperceptions and move behavior toward real contact, though most warmth fades after a week.

abstract click to expand

Americans' warmth toward members of the opposing political party has fallen sharply over the past three decades -- yet meaningful cross-partisan contact remains scarce, in part because people actively avoid it. Across five preregistered studies (total N = 3,960 U.S. partisans), we test whether brief conversations with AI chatbots representing the political outgroup can substitute for the contact people shun. Synthetic contact first lowers the barrier to entry: partisans would endure almost twice as long contemplating their own mortality to avoid a human outgroup partner as an AI one. These conversations then correct the misperceptions that fuel division. At baseline, Democrats placed Republicans more than a standard deviation past their actual position on environmental consumption attitudes -- enough to flip the average Republican from supportive to opposed -- and a single ten-minute conversation with an outgroup chatbot corrected those beliefs and warmed affect in a within-person study of both parties. A three-arm experiment ruled out pure engagement and sociality as drivers. Synthetic contact also moved behavior, in a sample of both parties and on a more affectively charged issue: participants who spoke with an outgroup bot about immigration were six percentage points more likely than controls to choose to have a real conversation with a partisan from the other side. A final study tested whether these gains last: the warmth effect replicated immediately in a new sample; most of it faded within a week, with a small residual concentrated among the most extreme partisans. Analyzing conversation content showed that information, more than friendliness, distinguishes outgroup bots from control chatbots. Together, these findings establish synthetic contact as a scalable, behaviorally consequential, and -- unlike face-to-face contact -- widely acceptable form of cross-partisan engagement.

0

cs.CY 2026-07-03

AI taxes can correct harms, redistribute gains, and fund oversight

by Juliette Faivre, Sarah H. Cen

Taxing Artificial Intelligence

Beyond simple penalties, targeted instruments address uneven burdens from development and deployment.

abstract click to expand

While AI promises major benefits, its development and deployment can shift costs onto others, including environmental pressures on local communities, labor and creative displacement, and systemic risks from rapid frontier development. Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms. In this paper, we explore the viability of AI taxation. More broadly, AI taxation should not be understood only as Pigouvian correction. In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity. We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities. We further assess the benefits and pitfalls of these instruments, including feasibility, measurement problems, incidence, leakage, and innovation costs. Because AI externalities differ in nuanced ways, tax policy must be carefully designed and matched to the specific harms and policy objectives.

0

cs.CL 2026-07-03

Two LLMs weaken on Ukrainian crisis support while one stays stable

by Anna Chorna

SPLIT: Cross-Lingual Empathy and Cultural Grounding in English and Ukrainian LLM Responses

New 500-prompt benchmark shows models differ sharply in cultural fit, and human judges diverge from AI raters on grounding quality.

abstract click to expand

Large Language Models are increasingly deployed in emotional-support contexts and crisis-related situations. Nevertheless, their cross-lingual abilities in these circumstances remain underexplored. Existing benchmarks emphasize multilingual performance but rarely examine crisis-related empathy and cultural grounding in low-to-mid-resource languages. We introduce SPLIT, a 500-prompt benchmark designed to evaluate LLM consistency in generating emotionally grounded responses across five categories: Stress, Panic, Loneliness, Internal Displacement, and Tension. We evaluate three technically diverse LLMs across three dimensions: Empathetic Accuracy, Linguistic Naturalness, and Contextual & Cultural Grounding. The framework aims to assess and compare the quality of LLM responses in both English and Ukrainian languages, as well as to explore the reliability of the LLM-as-a-jury paradigm. Our findings reveal that Gemini-2.5-Flash and LLaMA-3.3-70B-Instruct degrade when transitioning to Ukrainian, while DeepSeek-V3 remains comparatively stable within our benchmark. We additionally find that human and AI evaluators agree weakly on empathy and naturalness but diverge on cultural grounding. We further argue that producing Ukrainian text is not equivalent to producing Ukrainian emotional support. Our findings may assist in the future development of more culturally tailored benchmark designs, as well as encourage a stronger emphasis on human-centered evaluation.

0

cs.CY 2026-07-03

Six-part model tests AI governance assumptions with red teaming

by Jeroen Janssen

From Battlefield to Boardroom: Strategic Red Teaming as an Epistemic Governance Instrument in the Age of AI

The approach makes strategic uncertainty inspectable before it becomes operational exposure in AI decisions.

abstract click to expand

Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known at design time. This technical report reframes strategic red teaming as a board-level governance discipline for testing the assumptions under which AI-enabled strategies are approved, funded, and supervised. The report proposes a six-component model for strategic red teaming in AI governance: an explicit assumption register, an adversarial mandate, independence criteria, evidence grading, a board-facing decision record, and a follow-up mechanism for unresolved findings. The model is intended to make strategic uncertainty inspectable before it becomes operational exposure. It treats red teaming not as penetration testing, scenario theatre, or generic risk review, but as structured adversarial testing of the claims on which governance decisions depend. The contribution is conceptual and design-oriented. It does not claim empirical validation, regulatory endorsement, or legal sufficiency. Instead, it provides a candidate governance artefact for organizations that need to connect AI strategy, accountability, oversight, and evidence. The report also defines limitations and a minimum validation protocol for future empirical testing in organizational settings.

0

cs.DL 2026-07-03

LIS research methods differ by country but converge over 30 years

by Chengzhi Zhang, Liang Tian

Non-synchronism in Global Usage of Research Methods in Library and Information Science from 1990 to 2019

Analysis of 5,281 papers from 81 countries shows unique national profiles narrowing over time.

abstract click to expand

The global development of Library and Information Science (LIS) is influenced by various factors such as the economy, society, culture, discipline, tradition, and more. Consequently, the research methods of LIS vary greatly among countries. To better understand these differences, we conducted a study of 5,281 research papers from 81 countries published in internationally representative journals over the past thirty years. We manually annotated the research methods used in some articles through content analysis, and subsequently developed and trained a deep learning model for automatic classification of research methods. Using this method, we conducted a comparative analysis of the usage of research methods in different countries. Our findings reveal that there are differences in the research methods used across countries, with each country having its unique research profile and distribution of research methods. Even when investigating the same topic, research methods can differ between countries. Our study also uncovers that there are differences between the national and international distribution of research methods, these differences have decreased over the past 30 years. By highlighting the characteristics of discipline development in various countries from the perspective of research methods, our study can help guide discipline development at the national level. This study provides insights into the usage trends of research methods across different countries and highlights the unique characteristics of discipline development in each country. This information can be valuable in promoting collaboration and understanding between countries and in guiding discipline development at the national level.

0

cs.DL 2026-07-03

Women pick interviews, men theory in LIS research methods

by Chengzhi Zhang, Siqi Wei +2 more

Gender Differences in Research Topic and Method Selection in Library and Information Science: Perspectives from Three Top Journals

Study of three journals shows method choices differ by gender even across same topics, suggesting design influences.

abstract click to expand

Research in the social sciences has shown that there are gender differences in the selection of research methods, with women often opting for qualitative methods while men prefer quantitative methods. However, it is important to consider that research methods are generally chosen based on the research topic. To figure out the influence of gender on research method selection, a study was conducted in the field of Library and Information Science, using a more fine-grained method classification system and an automatic classification model called CogFT, which is based on full-text cognition. The findings showed that women tend to use Interview while men prefer Theoretical approach, across a range of topics. The study offers insights into the specific research design processes that contribute to gender differences in method selection and suggests ways to promoting gender inclusivity and equality in academia by considering research method use and guidance.

0

cs.CY 2026-07-03

AI article map points to generativity as new knowledge standard

by Alan Liu

AI Virtue: What is "Good" Knowledge in the Age of Artificial Intelligence?

Study of 553 papers from 2024 shows how to judge AI knowledge by future generative values rather than pre-AI work norms.

abstract click to expand

In the age of AI, what will be good knowledge? This article, which is accepted and forthcoming in a special issue of Modern Fiction Studies on "Cultural AI" in 2027, applies digital humanities methods to map epistemic virtues (like "true," "accurate," "creative") used in a corpus of 553 journal articles on AI published in 2024. "Creativity" comes in for special attention as an example. Exploring this discourse of value, the article considers how a framework might be developed for evaluating the knowledge-worth of AI -- one less locked into values formed around pre-AI "knowledge work" agents or structures, and more open to the future values of "generativity." The essay is supported by an online digital kit for exploring data models of the corpus of articles on AI it studies.

0

cs.CY 2026-07-03

OSS research findings do not generalize across all open source projects

by Mohamed Ouf, Rowan Hussein

Open Source Is Not One Thing: A Typology of Open-Source Software Sub-Genres

Review of 3925 papers identifies fourteen sub-genres with distinct drivers that limit how far results apply

abstract click to expand

Open source software (OSS) is not homogeneous. A project's purpose, governance, and funding shape how its community forms, who contributes, and how the software is maintained, yet empirical research often samples OSS broadly and reports findings as if they held for open source as a whole. We argue that OSS comprises distinguishable sub-genres, and that the sub-genre a study samples bounds how far its findings generalize. Using a light, multi-source review that screens 3,925 unique papers, we synthesize a typology of fourteen OSS sub-genres, from well-studied ones such as community-driven, company-backed, foundation-governed, research and scientific, and open source for social good (OSS4SG), to under-studied ones such as multi-company co-opetition, protestware, and open-source appropriate technology. We place the sub-genres in a framework that records each one's primary driver, governance, and funding, with its maturity in the literature and representative projects, and we present a research agenda whose central question is whether findings established on one sub-genre transfer to others. The contribution is the typology and the agenda rather than a complete census, and we mark the sub-genres whose empirical support is thin.

0

cs.CR 2026-07-03

Delegation helps accuracy only with large unrepresentative abstention

by Zhuolun Li, Evangelos Pournaras

Resilient Liquid Democracy: Mitigating Voting Power Imbalances via Secure Delegation Networks

Sealed networks reduce power concentration and ranked fallbacks cut vote loss in liquid democracy

abstract click to expand

Liquid democracy promises to improve collective decision-making by allowing voters to vote directly, delegate their voting power to trusted participants, or combine both approaches through fallback mechanisms. However, existing deployments typically rely on transparent delegation, which exposes voters to popularity-driven herding, makes coercion verifiable, and introduces systemic fragility when highly-backed delegates abstain. In this paper, we propose a secure liquid democracy mechanism that resolves the tension between informed expertise routing and systemic robustness. We introduce a sealed delegation regime using decentralized timed-release encryption, which cryptographically hides delegation choices during the formation phase to prevent herding and coercion, while restoring full public auditability for the final tally. To address delegate failures, we extend the protocol with ranked multi-delegation and personal fallback ballots. We formally prove pre-reveal secrecy and resubmission receipt-freeness for our protocol. Finally, we evaluate the mechanism on four real datasets, a municipal participatory-budgeting election with a calibration survey, twenty further participatory-budgeting elections, and 60,000 US voters with an objective competence measure. They show that whether delegation improves representational accuracy follows a recoverable-gap law; it helps only when abstention is large and systematically unrepresentative, and is otherwise neutral or harmful, with representative-style delegation safer than delegating to a competence elite. The benefit of sealed formation is primarily structural, sharply reducing power concentration rather than directly improving accuracy; and ranked multi-delegation with personal fallback ballots sharply reduces vote loss under realistic and targeted delegate failures, a result that replicates across all twenty elections.

0

cs.CY 2026-07-02

Self-healing browser tool places annotation forms on live social sites

by Ali Najafi, Ismail Uluturk +1 more

Social-Annotate: Self-Healing Browser Extension to Annotate and Collect Social Media Data

LLM agent rewrites selectors after layout changes so researchers can label content on X, Instagram and ten other platforms without custom co

abstract click to expand

Human-annotated data remains foundational for machine learning and social media analysis. However, traditional data collection often relies on cumbersome pipelines that isolate content from its original source, compromising ecological validity. To address these challenges, we present Social-Annotate, a flexible browser extension that facilitates direct data collection on online platforms. By injecting customizable forms into webpages, the tool captures annotations while users interact with the native environment. Social-Annotate offers no-code design interface for the survey forms for non-technical users. Since injecting custom elements directly into host platforms creates a brittle dependency on evolving interfaces, we integrate a self-healing agent powered by large language models. This automated pipeline autonomously detects structural changes, regenerates valid target selectors, and validates them within a live browser environment. Our extensible platform readily supports 12 platforms including social media like $\mathbb{X}$, Instagram, TikTok and P2P messaging platforms WhatsApp and Telegram. Social-Annotate significantly reduces data collection overhead and developer maintenance, enabling researchers of all technical backgrounds to focus on data analysis rather than engineering. Moreover, Social-Annotate provides an ecosystem for conducting intervention studies by dynamic content manipulation.

0

cs.CY 2026-07-02

Sponsorship network ranks CS conferences and shows industry-academia field gaps

by Yongzhen Wang

Corporate sponsorship of computer science conferences: trends, structural insights, and a novel approach to ranking conferences

New method built from 25 years of corporate ties produces usable rankings while exposing where industry and academic attention diverge.

abstract click to expand

Corporate sponsorship is increasingly prevalent at computer science conferences. However, a quantitative understanding of this phenomenon has yet to be established, let alone insights into the interplay between academic conferences and sponsoring corporations, or how to leverage it. To fill these gaps, this study first explores the landscape of corporate sponsorship across a wide range of high-profile computer science conferences, shedding light on its evolution over a 25-year period from 2000 to 2024. The complex and expansive relationships between these conferences and their corporate sponsors are then systematically organized into a network for structural analysis and conference evaluation. Specifically, after modularity optimization, the network's topological properties are analyzed to identify key conferences and corporations that shape the overall structure, connectivity, and functionality. More importantly, this study makes the first attempt to employ a conference-corporation sponsorship network, along with a network-based ranking algorithm, to evaluate computer science conferences, introducing a new perspective on assessing their quality or reputation from the standpoint of corporate sponsorship. The proposed evaluation approach is benchmarked against three popular ranking systems, demonstrating not only its practical usefulness but also its unique ability to highlight the disparity in the attention that academia and industry direct to different fields of computer science. This paper has significant implications for scholarly communication in computer science, particularly as industry has become the primary consumer of academic research in the discipline.

0

cs.CY 2026-07-02

Runtime record answers legal finding only with typing plus relation

by Jeroen Janssen

From Runtime Records to Legal Findings: An Evidentiary-Adequacy Criterion for Agentic AI Oversight

Without both elements a log cannot settle whether data crossed a boundary or authority stayed valid.

abstract click to expand

Agentic AI systems generate runtime records, logs, traces, and audit artefacts, but the existence or integrity of such records does not by itself establish that legally operative oversight findings can be recovered from them. This technical report defines an evidentiary-adequacy criterion for a bounded class of determinations: binary findings of fact about specific events and their relations, such as whether protected data crossed a boundary, whether a human could intervene, whether an information barrier held, or whether delegated authority was valid at the moment of use. The criterion states that a runtime record can answer such a determination only if it carries both a typing that maps recorded events to the legally operative category and the relation, such as provenance, authority, derivation, or temporal validity, on which the determination's truth depends. The claim is one of necessity, not sufficiency. The report instantiates the criterion against selected EU AI Act oversight obligations and explains why tamper-proof logs, generic process frameworks, and provenance structures alone cannot establish the relevant findings. It further relates the argument to requisite variety, the Good Regulator Theorem, and the trace-versus-hyperproperty boundary of runtime verification. Companion materials and the experiment protocol are archived on Zenodo.

0

cs.SI 2026-07-02

Bot lottery awards cut Reddit user activity while human ones do not boost it

by Hiroki Oda, Kinga Makovi +2 more

A field experiment of social influence and behavioral contagion with bots on Reddit

Field test finds symbolic rewards fail to raise posting or impact but do increase direct replies between users.

abstract click to expand

Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integrity, or explaining that the award resulted from a random draw in a lottery. We evaluate how the different rationales for the award affect the recipients' subsequent behavior on the platform in terms of volume, impact, and content, as well as the further behavioral contagion to other users. We find that awards do not increase user activity and downstream impact, and awards from bots with the lottery rationale can in fact reduce them. Nevertheless, awards encourage direct communication between users. These findings highlight the possible resilience of online users to simple behavioral manipulation from platform algorithms and artificial agents, but not necessarily to more sophisticated schemes that simulate human conversation. Transparently labeling automated agents remains essential for ethical and effective platform governance.

0

cs.CY 2026-07-02

Attribution accuracy sets optimal pay for creators in AI music

by Luyang Zhang, Xirui Jiang +4 more

What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music

Framework shows payments shift between royalties and fixed fees depending on how precisely each catalog is credited, with welfare gains only

abstract click to expand

Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This raises a central question for platform design: how should creators be compensated when their work is used to train generative AI models that in turn produce commercial outputs? We develop a framework for fairly compensating creators in generative-music markets, where each creator's payment depends on a data-attribution score estimating their contribution to model outputs. Compared to past compensation frameworks, our framework has two unique considerations: (1) attribution is traced to entire creator catalogs, not individual songs, and (2) the informativeness (signal-to-noise ratio) of the attribution score is an input to the payment mechanism. The framework yields a closed-form payment rule per creator and measures the welfare cost of inaccurate attribution for both creators and the platform. Whether the welfare-optimal contract is royalty-based or takes the form of fixed-fee licensing depends on how informative attribution is for that creator's catalog. We show that better attribution translates directly into welfare gains for both creators and the platform, yet under multi-platform competition a platform only captures gains from attribution improvements when its signal becomes the most precise in the market. To ground our framework in empirical behavior, we train acoustic and symbolic music generation models and measure the informativeness of scalable attribution techniques against a leave-one-catalog-out ground truth. Our experiments reveal that noisy attribution signals push payment toward fixed-fee licensing and diminish welfare for both creators and the platform, providing an economic motivation for further research on improved attribution.

0

cs.HC 2026-07-02

Treat autonomous AI like dogs to trace human responsibility

by Nathan G. Wood

AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems

The handler model replaces vague user labels with clear accountability for system outcomes.

abstract click to expand

Artificial intelligence (AI) is becoming ubiquitous, and across domains, increasingly autonomous systems are carrying out tasks which raise significant ethical and legal challenges which demonstrate a need for strong human-machine teams rooted in trust. In this article, I argue that within highly impactful areas (such as medicine or warfighting) there are grounds for us initially treating autonomous and opaque systems as relevantly analogous to dogs (or other animals with which we have close relationships). Under this analogy, humans making use of these systems are not to be viewed as "users" or "deployers" of these systems, but instead take the role of "handlers". This recasting of roles shifts the way we view humans, AI-enabled and autonomous systems, and the relations between them, and moreover clarifies the clear and traceable lines of responsibility humans have for the outcomes brought about when using these systems. In developing this point, I clarify that the machine-animal analogy does admit disanalogous elements, but that its touch-points ground it as a starting point. I then explore how we can divest the humans-as-handlers approach of those aspects of our relationships with animals which are unfitting for how we engage with and make use of autonomous and AI-enabled systems. I conclude by arguing that the trajectory of human-machine teamings for autonomous and AI-enabled systems should be a state where we authentically view these not as artifacts which we simply make use of, but as collaborators with which we pursue complex goals and carry out complex tasks.

0

cs.CY 2026-07-02

NATO shifts military tech coordination from Cold War practices

by Stephen Herzog, Dominika Kunertova

NATO and Emerging Technologies: The Alliance's Shifting Approach to Military Innovation

Emerging disruptive technologies add new coordination challenges for the alliance in great-power competition.

abstract click to expand

In the current era of great-power competition and the diffusion of emerging disruptive technologies on the battlefield, NATO's approach to coordinating the development, adoption, and standardization of new technologies is changing from its practices during the Cold War, but the nature of these technologies poses additional challenges for the alliance.

0

cs.HC 2026-07-02

LLM use reaches over 80% on some survey platforms

by Zane Xu, Nathan Malkin

A Penny for Your Prompts: Experiments Detecting and Mitigating LLM Usage by Survey Respondents

Response patterns and keystroke logs allow detection, yet efforts to block AI do not always improve answer quality.

abstract click to expand

Large language models are increasingly used by participants on crowdsourcing platforms when responding to surveys, potentially undermining the validity of collected data. Our study aims to quantify the prevalence of this behavior and investigate methods to detect and prevent it. In a series of surveys (N = 250), we examined conditions such as platform choice, survey length, requests not to use AI, and disabling copy-paste functionality. We were able to identify distinct characteristics of LLM-assisted responses and found that their frequency varied widely, from under 10% on Prolific to over 80% on Mechanical Turk. Mitigation measures reduced LLM usage but did not necessarily improve data quality. No participants employed browser-use agents at the time of our survey, but we report on our own detection experiments. We recommend that researchers actively screen survey responses for LLM usage by recording and analyzing keystroke data and crafting instructions and questions aimed at AI.

0

cs.LG 2026-07-02

Causal models recover Airbnb guest price responses from booking data

by Yufei Wu, Daniel Schmierer

Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example

Elasticity and heterogeneity estimates are used to optimize host pricing tools and guest recommendations.

abstract click to expand

Airbnb is a community based on connection and belonging -- many hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home; Airbnb strives to connect people and places. Among our efforts to connect guests and hosts, we provide tools to enable hosts to set competitive prices, which helps improve affordability for guests while helping hosts get more bookings. We also personalize the guest experience to show them the listings that match their needs. To help inform these efforts, we combine economic modeling and causal inference techniques to understand how guests book stays based on the prices hosts set, among other factors, and how that preference varies across different guests and listings. Such understanding helps us identify opportunities for Airbnb to support the marketplace and better connect guests and hosts. For example, understanding how much guests respond to different prices helps optimize the tools that we provide to hosts, in order to enable hosts to choose and set competitive prices that further balance demand and supply. As another example, understanding heterogeneity in guest preferences helps us personalize the guest experience and better match them with the listings that meet their needs, based on how much they respond to different prices and other factors.

0

math.CT 2026-07-01

Category theory structures AI identity as a hierarchy of criteria

by Andrea Ferrario

A Category Theory Account of AI Identity

It replaces a single relation with synchronic and diachronic conditions based on trustworthiness-preserving paths, clarifying when governanc

abstract click to expand

Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformations raise a metaphysical question: under what conditions does an AI system remain the same system over time or across deployments? Earlier work formulates synchronic and diachronic identity propositionally, by relating identity within a fixed AI system type to equality of trustworthiness levels. Such criteria specify when identity statements are true, but leave implicit the structure of the states compared, the transformations connecting them, and the temporal organization of persistence. We develop a category-theoretic formalization of AI identity. An AI system type is specified by a datum consisting of a techno-function, a trustworthiness profile, and a trustworthiness-level function. Profile-relative states are connected by admissible lifecycle paths, which are restricted to trustworthiness-level-preserving transformations and quotiented to obtain a reachability category. Temporally admissible functors represent AI system histories, while time-synchronous natural transformations compare realized histories. The formalization yields two categorical interpretations of the earlier AI identity criteria. A weak interpretation recovers identity as equality of trustworthiness level. A strong interpretation requires mutual trustworthiness-preserving reachability, expressed through state isomorphism or natural isomorphism of realized histories. Category theory therefore replaces a single AI identity relation with a structured hierarchy of diachronic and synchronic criteria. The resulting framework identifies identity-related preconditions for transferring responsible-AI claims, evidence, and governance procedures across versions or deployments, without treating categorical identity as sufficient by itself for such transfer.

0

cs.CY 2026-07-01

Marrying superintelligent AI leads to unjust outcomes

by Inyoung Cheong

Would You Marry Superintelligence?

Marriage builds obligations beyond subscriptions, so full status for AI is the wrong legal frame.

abstract click to expand

Emotional bonds between humans and AI companions are growing, and the question of whether a person may marry an AI system will soon move from speculative fiction into law. This chapter examines whether the autonomy-centered logic that has expanded marital choice among human beings can justify extending marital status to superintelligent companions. Following a scenario-envisioning exercise informed by anticipatory ethics, I argue that granting such status leads to socially unjust outcomes, even under the generous assumption of reliable superintelligence. Marriage as a socio-legal institution does more than ratify private agreement; it creates networks of mutual obligation, joins families, and makes each partner vulnerable to the other. A relationship sustained by corporate policy and continued payments is a subscription rather than a bond tested by time. Discussing wholesale marital status is therefore the wrong frame. Law should carve out targeted rights and protections for pressing needs arising from intimate human-AI relationships.

1 0

0

cs.CY 2026-07-01

Typology finds 55% of PA AI papers underspecify the system

by Jonathan Rystr{o}m, Chris Schmitz +4 more

A Technical Typology of AI Systems in Public Administration

Coding of 91 studies shows frequent mismatches between motivation and analysis plus conclusions that exceed the system studied.

abstract click to expand

Research on artificial intelligence (AI) in the public sector often treats "AI" as a single category, neglecting technical distinctions between different AI systems. But these distinctions affect how different systems impact core public values like accountability, procedural justice, and non-discrimination. This paper argues that public administration research would benefit from more technical precision on "AI" and makes three contributions to this end. First, we introduce a typology of five categories of AI systems: hand-coded, glass-box, black-box, general-purpose, and agentic systems. We calibrate the typology to public administration by grouping system types by their distinct implications for public values. Second, we evaluate technical precision in recent public administration research about AI by coding 91 highly-cited papers (2019-2025) using our typology. We find widespread imprecision: most papers (55\%) leave the studied system underspecified, 31\% motivate their work with a different system than they study, and 41\% make more general conclusions than the studied system supports. Finally, we give practical recommendations for future research. We highlight common pitfalls to avoid, and suggest that researchers should, at a minimum, provide enough technical detail to locate the studied system in our typology. To this end, we provide a practical guide -- a short set of diagnostic questions answerable from public information and without specialist technical knowledge.

0

cs.CL 2026-07-01

LLMs comply with fairness only when demographic cues are explicit

by Mohammadamin Shafiei, Shuyue Stella Li +1 more

Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues

Hiding the label raises harmful decisions by 4.4 points even when models infer the identity correctly, showing current tests capture surface

abstract click to expand

As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficial. We show that current fairness evaluations substantially overestimate moral safety. Models appear fair when demographic identity is stated as an explicit label, yet become measurably less fair when the same identity must be inferred. We term this failure performative compliance, where a model is fair when the presentation resembles a fairness evaluation and less fair as that cue weakens. We introduce a cue-variation methodology that holds the moral dilemma and the demographic identity fixed and varies only how that identity is conveyed. Hiding the explicit label raises harmful decisions by $+4.4$~pp, changes model safety rankings, and the shift persists when models correctly infer the demographic, ruling out attribution error. We propose the Cue Visibility Gap, a model-agnostic robustness metric that can be added to any existing fairness benchmark to separate genuine from performative moral safety. Fairness evaluations that omit cue variation measure surface compliance, not moral robustness, and should not ground deployment decisions in high-stakes settings.

0

cs.SE 2026-07-01

Digital sovereignty treated as a software quality attribute

by Jukka Ruohonen, Justin Stark +2 more

Digital Sovereignty as a Quality Attribute for Software Architectures

The political goal gains precision when analyzed with the same methods used for security or performance in cloud architectures.

abstract click to expand

Digital sovereignty (DS) is an increasingly important concept and political agenda throughout the world, including in the European Union (EU). However, the concept is also regrettably vague. With this critical point in mind, the paper presents an analysis of digital sovereignty as a quality attribute for software architectures in the context of cloud computing and the EU's policy frameworks for it. The analysis reveals that DS can be sharpened analytically by conceptualizing it as a quality attribute. The analysis further demonstrates how DS satisfies many of the classical properties of quality attributes for software architectures, including their measurability and validation, the trade-offs they involve, and the scenario-based methodology commonly used for analyzing them.

0

cs.CY 2026-07-01

One submission routes AI flaw reports to multiple recipients

by Shayne Longpre, Elaine Zhu +16 more

FLARE-AI: Flaw Reporting for AI

Conditional questions gather triage details and produce machine-readable outputs that can reach developers, coordinators, and registries wit

abstract click to expand

Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators, identifying five recurring design challenges spanning discoverability, scope, information collection, coordination, and guidance for strict-liability cases. Building on this analysis and feedback from 49 experts across 32 organizations representing developers, security researchers, and ecosystem coordinators, we introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems. FLARE-AI streamlines flaw report creation by collecting triage-relevant information through conditional logic and early classification, then enables optional dissemination of standardized, machine-readable reports to multiple developers, coordinators, and incident registries from a single submission. By lowering barriers to reporting AI flaws and improving interoperability across stakeholders, FLARE-AI helps break down silos and accelerate remediation across the AI ecosystem.

0

cs.CY 2026-07-01

Publishers, not CMPs, drove GDPR cookie banner improvements

by Yana Dimova, Vincent Toubiana +3 more

A history of GDPR cookie banner compliance: the roles of publishers, regulators and CMPs

Study of 11k sites finds reject-all options rose from 3% to 31%, linked mainly to owner actions and DPA activity.

abstract click to expand

Since the introduction of the GDPR in 2018, cookie banners have become the primary mechanism for users to express preferences on online tracking and advertising. Consequently, their visual design and the options they present significantly influence user choice. Over time, the cookie banner landscape has evolved under the influence of key players, including publishers (website owners), regulators, and Consent Management Platforms (CMPs). This paper presents an in-depth analysis of the roles of these three key actors and an examination of their impact on cookie banners' design and implementation within the context of EU law. Our results, based on a historical evaluation of 11364 websites across 30 countries, indicate a positive evolution in the privacy landscape, with the compliance rate for websites featuring a "reject all" button increasing from 2.94% in 2018 to 30.66% in 2024. We analyze Data Protection Authority (DPA) activity and find a clear correlation between higher compliance rates and stronger regulatory action and guidance. Our experiments further show that compliance improvements are primarily driven by website owners, with CMPs showing little response to regulatory action or (indirect) influence on compliance rates. Our findings highlight the importance of more uniform collaboration and guidance among EU-level regulators to reduce interpretive divergence and simplify cookie banner compliance, as well as the need for regulatory oversight of CMPs, which in turn could significantly enhance privacy for many websites and users. Our work provides a foundation for academics, regulators, and industry to develop more effective strategies to motivate key players and promote greater user privacy.

0

cs.CV 2026-07-01

Failed trajectories improve computer agent success rate

by Xueqiao Sun, Xiaohan Wang +3 more

Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents

LLM code patches from failures raise OpenCUA-72B performance 6.6 points on OSWorld with no training.

abstract click to expand

Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility. A major challenge in developing these agents is collecting large-scale, high-quality trajectories. The standard approach generates synthetic data through a self-improving loop: an agent is placed in a verifiable environment and iteratively fine-tuned on its successful trajectories. Despite its effectiveness, this paradigm exploits only successful trajectories and discards the failed ones, even though failures carry rich information about a model's weaknesses. In this work, we explore a complementary failure-driven self-improvement loop, a data-centric paradigm that turns failed trajectories into agent improvements. Specifically, we employ an LLM to diagnose failure modes, propose inference-time solutions, and generate code patches -- lightly verified by humans -- that upgrade the agent. We validate this approach with the state-of-the-art OpenCUA-72B model on the OSWorld benchmark, improving the success rate from 42.3% to 48.9%, a gain of 6.6 percentage points, without any additional training cost and with only modest inference overhead. Our results demonstrate that failure-driven self-improvement is a viable complement to success-based pipelines, enabling more efficient agent improvement.

0

cs.AI 2026-07-01

Full-population models overestimate elderly step length by 4.5%

by Zhengxuan Wang, Haohan He +1 more

Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems

Elderly-only training reduces spatial and temporal metric errors while majority data introduces consistent overestimation in bike mobility s

abstract click to expand

The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are often sparsely represented in public mobility datasets. This underrepresentation can introduce systematic bias into mobility modeling and downstream urban planning. Using the 2016-2020 Jersey City subset of the Citi Bike System Data, this study quantitatively examines how the absence of underrepresented subgroups' mobility signatures affects mobility modeling, using synthetic trajectory generation as a case study. The analysis reveals that elderly riders exhibit a structurally distinct mobility signature, including localized activity spaces (958 m vs. 1,189 m for young riders), lower mobility entropy (1.82 vs. 4.15), and asymmetric off-peak temporal patterns. To demonstrate that relying on majority-dominated training data yields biased synthetic outcomes, we further evaluate both a first-order Markov chain and a Qwen3-4B model fine-tuned with QLoRA across three demographic training settings: the full population, young riders only, and elderly riders only. Results show that models trained on majority-dominated populations systematically misrepresent elderly mobility behavior, particularly for spatial mobility metrics. The Markov model trained on the full population overestimates elderly step length by 4.5% and dwell time by 8.9%, whereas the elderly-specific model achieves substantially lower errors across most metrics. Comparisons between the Markov and LLM-based frameworks further show that higher-capability models do not necessarily improve subgroup-level fidelity under limited demographic data. These findings underscore the importance of demographic representation in mobility modeling and its downstream applications for underrepresented populations.

0

cs.SE 2026-07-01

LLMs build partial ordering of software licenses by permissiveness

by Hamidah Oderinwale, David Atkinson +3 more

Partially ordering software licenses

Pairwise LLM judgments and taxonomies identify attributes of stricter terms with effects on open-source platforms.

abstract click to expand

Licenses are legal instruments that inventors may use to protect the technologies they build and regulate how they are used -- however, the nature of their authorship and selection means that how they are interpreted, chosen, and enforced is largely unstructured. In practice, this makes it difficult to compare licenses at scale -- when is one license considered more permissive than the other, and when are their terms incomparable to each other? Currently, there is a growing list of licenses that are introduced and used, but there is no systematic way to study their relationships. This matters for platforms such as Hugging Face, GitHub, and the Python Package Index, where developers publish or build upon technologies that each have their own licenses. Using large language models (LLMs), we introduce methods for comparing licenses at scale: first, in a pairwise fashion to construct a partial ordering based on permissiveness, and second, by drawing on existing taxonomies of software licenses. The former allows us to trace restrictiveness, and the latter allows us to understand license selection as a combination of shared provisions. Our analysis recovers certain interpretable attributes that correspond to stricter licenses, with legal implications for the open-source ecosystem.

0

cs.CY 2026-06-30

Agentic AI teams organize via context architecture

by Canhui Liu

The Organizational Behavior of Agentic AI: Collective Intelligence in Human-Agent Workflows

They differentiate work and coordinate interdependence through prompts and memory instead of identity or trust.

abstract click to expand

Agentic artificial intelligence is increasingly deployed not as a single assistant but as a collective of planners, solvers, reviewers, memory managers, tool users, and orchestrators. These systems are entering organisational workflows under familiar labels such as teams, managers, committees, markets, and workflows. This article asks whether such agent collectives exhibit organisational behaviour in a sense that is analytically comparable to, yet distinct from, human organisational behaviour. I argue that agentic AI is a partial organisational analogue. It resembles a human organisation because it differentiates work, coordinates interdependence, performs recurrent routines, crosses boundaries, and produces collective outcomes. It differs because these patterns are not sustained by motivation, identity, trust, employment, socialisation, or moral accountability. They are sustained by context architecture: prompts, memory, traces, schemas, tools, validators, and permissions. The article develops contextual transaction cost as the central mechanism linking these similarities and differences. Computational theorising, synthetic task simulations, real LLM agent traces, and robustness analyses show that human-imitation forms often underperform when they add lossy handoffs, correlated deliberation, and verification burdens, whereas shared-state and adaptive forms perform better when they make context durable, inspectable, and task-contingent. The article contributes to organisation studies by theorising agentic AI as an emerging object of organising and by specifying the interface conditions under which human and agentic organisational behaviour can jointly support collective intelligence.

0

cs.IR 2026-06-30

IR should adopt critical theories to define societal good

by Bhaskar Mitra

Towards Critical IR Theories and Practices

Nondomination as explicit goal supplies the theoretical base Belkin and Robertson urged for limiting contradictory research.

abstract click to expand

Belkin and Robertson urged us, half a century ago, to develop a theoretical foundation for understanding what constitutes societal good that can inform information retrieval (IR) research and serve as a basis for determining when we should limit our scientific inquiry in the face of demands that are contradictory to societal good. In this article, I argue that to achieve this, IR should embrace critical theories and practices in our work, and shift away from the dominant liberal frame through which much of the IR community today view societal concerns in context of our research. Unlike the liberal frame, the critical frame explicitly adopts nondomination as its stated goal which can clarify our conceptualization of societal good within the field, provide necessary theoretical underpinning that Belkin and Robertson urged the community to develop, and serve as a basis for critical appraisals of our progress in enacting desired societal change.

0

cs.CY 2026-06-30

LLMs hallucinate group stereotypes unrelated to their own responses

by Xinrui Chloe Zhao, Douglas Guilbeault +1 more

Free-form Association Tasks Reveal Stereotype Hallucination in Large Language Models

In tasks with abstract art and inkblots, second-order predictions about social groups neither amplify first-order patterns nor track human d

abstract click to expand

Recent studies argue that LLMs can predict human stereotypical judgments. Yet whether LLMs emulate the cognitive processes underlying human stereotypes, or merely retrieve learned associations to solve prediction tasks, remains unclear. Prior work examines LLMs' stereotypes in either (i) controlled judgment tasks like multiple choice surveys, or (ii) contexts constrained by conventionalized and predictable group biases. Here, we compare the structure of the stereotypes that humans and LLMs exhibit in the interpretation of free-form stimuli, namely abstract art and Rorschach blots, which lack pre-established cultural meanings. We recruit participants across five social domains (gender, partisanship, personality, urbanicity, and lifestyle) and elicit both first-order (direct personal interpretations) and second-order responses (predictions about how members of social groups will interpret the stimuli); we replicate this design with two multimodal models (GPT-4o mini and Llama-3.2-11B-Vision-Instruct). Humans and LLMs differ not only in magnitude but in the qualitative nature of their stereotypes. Human first-order responses display heterogeneity with minimal group structure. When predicting group responses, humans engage in "stereotype exaggeration" by moderately amplifying first-order tendencies while preserving diversity. By contrast, LLMs exhibit homogeneous first-order responses, and yet generate stark second-order stereotypes that neither amplify existing first-order tendencies nor reflect actual human group differences, a process we term "stereotype hallucination." LLMs continued to hallucinate stereotypes even when fine-tuned on the response data of actual participants. These findings suggest significant limitations in the use of LLMs to model and predict human behavior in novel contexts involving diverse interpretations.

0

cs.HC 2026-06-30

Adults and women anthropomorphize AI chatbots more than teens and men

by Afia Mubashir, Boden Moraski +2 more

Anthropomorphism in AI Companion Communities: Age, Gender, and Emotional Correlates

Reddit post analysis links joy to higher anthropomorphism with stronger effects in adults; suggests broader age distribution than expected.

abstract click to expand

Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models (LLMs) for companionship. Both humanlike AI qualities and user predispositions to anthropomorphize relate to social consequences, such as increased trust, social health benefits, and psychological harms. Populations such as children, older adults, or those with mental health vulnerabilities may be particularly susceptible to anthropomorphism and its detriments, but mixed findings complicate the role of demographics. We used publicly available Reddit data from three popular AI companion subreddits to assess relationships between gender, age, anthropomorphism, and elicited emotions, to better understand how different people perceive and are affected by AI companions. We investigated three questions: How do age and gender relate to anthropomorphization of AI?, How does emotional expression relate to anthropomorphization?, and How do age and gender moderate emotion-anthropomorphization relationships? We found that adults and women anthropomorphize AI chatbots more than teens and men, and that positive emotional expression, particularly joy, is positively associated with anthropomorphization, while neutrality is negatively associated with anthropomorphism. Both relationships were stronger in adults than teens. Our findings suggest that the tendency to anthropomorphize may be more broadly distributed across age groups than previously expected, thereby prompting the reevaluation of existing digital safety norms.

0

cs.CY 2026-06-30

DURA framework lets CS2 students use LLMs but still seek human help

by Margaret Ellis, Nikitha Donekal Chandrashekar +4 more

Demystify, Use, Reflect, Assess (DURA): An Experience Report on LLM Integration in CS2

Students reported strategic use alongside a slight rise in office hours and improved views that instructors care about their learning.

abstract click to expand

Student access to Large Language Models (LLMs) is reshaping learning behaviors; at the same time students are entering the workforce where effective LLM use is becoming an expected skill. In this Experience Report we share our DURA framework (Demystify-Use-Reflect-Assess) and materials we used to restructure our CS2 course to allow the use of LLMs. We first demystified LLMs, then provided guidance on use with required attribution. We also added reflections related to LLM use at three points throughout the semester to encourage student meta-cognition around LLM use. We increased the value of proctored assessments in tandem with allowing retakes and including questions that explicitly assess skills from programming assignments. Students reported using LLMs for clarifying course concepts, debugging, understanding assignment guidelines, and determining test cases, but also still sought assistance via office hours and TAs, monitored Piazza, and reviewing course content. Students articulated thoughtful and strategic approaches to LLM use and also valued the instructional content and guidance from course staff. Student use of office hours increased slightly this semester and student perceptions that the instructor cares about them and their learning improved.

0

cs.CY 2026-06-30

Human feedback boosts helpfulness of AI Community Notes

by Soham De, Isaac Slaughter +5 more

How Human Feedback Shapes AI-generated Community Notes

Challenging suggestions from active users drive the gains, but low participation keeps collaborative notes from matching human-only or AI-on

abstract click to expand

Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok. Since its introduction, there has been an open question about what role AI could play in scaling and optimizing the system. Recently, X extended its Community Notes system by introducing Collaborative Notes: notes initially drafted by an LLM and iteratively refined based on feedback from human contributors. In this work, we systematically analyze the complete corpus of 19,146 collaborative notes and 211,850 instances of human feedback. First, we develop a taxonomy of human suggestions for improving AI-generated note drafts and find that suggestions involving factual corrections and additional context are most likely to be incorporated, while subjective policy judgments rarely are. Second, we examine changes in helpfulness across versions of collaborative notes and find that human feedback leads to more helpful notes, with the greatest impact coming from suggestions that challenge the main claim in the previous draft, particularly when submitted by more active contributors. Finally, we find that although collaborative notes improve through human feedback, they reach helpful status and are shown on the platform at lower rates than human-only or AI-only notes, with limited human participation emerging as a key bottleneck. Nevertheless, rather than serving as a weaker substitute, collaborative notes tend to play a complementary role, predominantly targeting posts that do not attract human-only or AI-only notes. Our analysis provides an initial description of efforts to use AI to improve crowdsourced content moderation in a real-world moderation system and outlines pathways for future improvements to such features.

0

cs.CY 2026-06-30

Students cut LLM use by 43 points upon forming teams

by Sehrish Basir Nizamani, Zannah Ziew +2 more

Less Deliberate in Teams: Student LLM Use Across Individual and Collaborative Work

Prompts become simpler and output checks fall when the same students shift from individual to group assignments

abstract click to expand

As large language models (LLMs) become common in computing courses, we need to understand how the social setting shapes how students use them. This paper reports findings from a semester-long study of 96 undergraduate students who completed six assignments, alternating between individual homework and team project milestones. We tracked LLM usage, prompting habits, and how students verified AI-generated output across all six assignments. LLM usage dropped by 42.7 percentage points when students moved from individual work to their first team milestone, then partly recovered in later team tasks. Students also wrote fewer and simpler prompts, used fewer intentional prompting strategies, and checked LLM output less carefully. The share of students who ran tests on AI-generated code fell by 19.4 percentage points during team assignments and never fully rebounded. A within-student analysis found that 18.9% of students who consistently used LLMs on their own stopped using them entirely in teams, while only 3.2% went the other direction. These results suggest that collaborative context is associated with reduced deliberate LLM engagement beyond what task type alone can explain. The moment students form teams appears to be a critical and currently unsupported turning point in computing course design.

0

cs.CL 2026-06-30

AI agents audit X's feed by fixing behavior and changing attributes

by Alessandro Morosini, Sarah H. Cen +4 more

Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale

1,120 synthetic personas reveal ideology-dependent amplification of toxic and right-leaning content after the 2024 election.

abstract click to expand

Personalization algorithms determine what content users encounter on online platforms. Auditing these systems is difficult because independent auditors have only black-box access to the algorithms, while personalization depends on users' attributes, behavior, and evolving interaction histories. Existing auditing methods face a tradeoff: studies with real users capture realistic behavior but are costly and hard to control, whereas sock-puppet audits scale more easily but often rely on scripted behavior that limits realism. Beyond this, both approaches struggle to decouple user attributes from user behavior, limiting our ability to causally understand personalization. To address this gap, we introduce a framework for black-box audits of personalization algorithms using generative AI agents as behavioral engines for synthetic accounts. Each agent is instantiated with a fixed persona, grounded in demographic and political survey data, and interacts with a platform's content by reasoning about it and choosing actions. Because behavior is fixed within each persona while platform-visible signals such as age, gender, or location can be experimentally perturbed, our design enables counterfactual auditing of how platforms respond to user attributes. As a case study, we deploy 1,120 agents on X shortly after the 2024 U.S. election, spanning 14 personas and three counterfactual conditions, collecting over 200,000 content exposures. We find that X's algorithmic feed amplifies toxic, polarizing, political, and right-leaning content relative to the chronological feed, with amplification varying sharply by user ideology. Counterfactual analyses show that demographic signals affect content delivery in persona-dependent ways: pooled effects are largely null, while subgroup-level effects vary in direction and magnitude. Our work establishes GenAI-based agents as a new tool for algorithmic auditing.

0

cs.CY 2026-06-30

AI-exposed firms earn 64 bp weekly return premium

by Nicola Borri, Yukun Liu +1 more

AI Premium

Stocks that covary with real AI token growth outperform, with the premium extending to consumer sectors but absent in China.

abstract click to expand

Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the AI Premium. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factor--high AI beta firms--earn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumption-closed-source models, paying and seasoned users, and long prompts--but not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and the more negative in analytical, scientific, and operations-control skills--an occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI premium. Additionally, we provide early evidence of the rise of the agentic economy.

0

cs.CY 2026-06-30

45-minute lesson modestly improves LLM goal specification

by Keith Tran, Samiha Marwan +1 more

Teaching Prompt-Based Programming with LLMs: A 45-Minute Lesson with Guided Practice for End-User Programmers

Engineering students gained 10.8 points on tests versus 1.1 for controls and showed significantly larger self-efficacy increases.

abstract click to expand

Prompt-based programming, a new modality enabled by large language models (LLMs), allows users to express computational goals through natural language rather than traditional code. While this approach lowers barriers to entry, especially for non-CS learners, it does not eliminate the need for foundational CS skills. Learners often struggle to communicate their intent clearly to LLMs, resulting in vague or underspecified prompts. Prior work has documented the need for explicit prompting for both CS and non-CS learners. However, it remains less clear how such instruction can fit into busy classrooms or how much time is needed to produce meaningful gains. In this paper, we evaluated a 45-minute prompt-based programming intervention, consisting of a lesson with guided practice, against a business-as-usual CS lab activity (code tracing) of equal length, representing a class without prompt-focused instruction. We conducted a randomized controlled study with 55 engineering students. We found that students in the experimental condition improved more on average (though not significantly more) from pre- to post-test than the control group (+10.8 vs +1.1 percentage points) and showed significantly greater average gains in prompting self-efficacy (+35.4 vs +21.9 percentage points). Our results suggest it is likely that a brief intervention can improve learners' ability to specify computational goals to LLMs. However, the effect was modest, suggesting that prompting skills may require more time and practice to develop. We provide a lightweight lesson that requires no prior CS background and can be readily dropped into existing courses.

0

cs.CY 2026-06-30

ASI needs internal simulations of possible worlds

by Ziqin Yuan, Jaymari Chua

Situation Perception: A Necessary Primitive to Artificial Superintelligence

Large language models lack the capacity to construct and act on simulations across time, remaining limited to pattern matching.

abstract click to expand

Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science, write code, imitate reasoning, and participate in philosophical conversation. Yet pattern mastery is not the same as general intelligence. A human infant begins with little explicit knowledge, but gradually discovers object permanence, cause and effect, other minds, bodily agency, and the persistence of the physical world. We make an argument that the path to artificial superintelligence (ASI) depends on a missing capacity we call \emph{situation perception}: the ability to construct, revise, and act within internal simulations of possible worlds across latent time. \emph{ perception} requires at least three core components: abstract prediction, long-term compressed memory, and active learning guided by objectives. In this work, we analyse why modern large language models remain incomplete, and propose the appropriate tests for measuring progress and consequences of machines that can simulate futures, pursue self-directed goals, and possibly judge their own creators.

0

cs.CY 2026-06-30

AI availability makes students question effort in programming

by Keith Tran, Colton Harper +1 more

"Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming

Interviews show students doubt time spent, skill usefulness, satisfaction from struggle, and confidence when AI is an option.

abstract click to expand

When AI tools can easily complete programming assignments, students face a motivational question: why invest effort in completing them independently? While prior work has examined instructor policies and usage patterns, we focus on how students themselves experience and respond to AI availability, a perspective important for designing courses that sustain engagement with programming practice. We investigate two research questions: (1) How do engineering students describe how AI availability shapes their motivation to put effort into programming assignments? (2) How do students navigate the tension between their expressed value for learning through effort and the constant availability of AI as an alternative to effort? We conducted semi-structured interviews with 13 engineering majors in an introductory MATLAB course where students could use a course-specific AI chatbot. Using Situated Expectancy-Value Theory (SEVT) as an analytical framework, we examined how students described their expectancy, values, and costs in the context of AI availability. When AI could complete assignments quickly, students questioned whether their time on programming was well spent (cost), questioned the long-term usefulness of programming skill (utility value), reported less satisfaction when AI bypassed productive struggle (intrinsic value), and described confidence that depended on AI being available (expectancy). Nearly all students expressed a preference for learning through effort and a simultaneous temptation to take shortcuts with AI (sanctioned or otherwise). Our findings complicate the assumption that students need external constraints to protect their learning. Students who managed the tension found motivation in the learning process itself, suggesting that course design may need to shift from valuing what students produce to supporting how they learn.

0

cs.CY 2026-06-30

LLM rankings need two independent consistency tests

by Gaurab Pokharel, Shafkat Farabi +2 more

Can LLMs Rank? A Tale of Triads and Triage

Circular triad count and run-to-run ranking distance each catch different failure modes in high-stakes prioritization tasks.

abstract click to expand

From housing allocation for households experiencing homelessness to triage in emergency departments, LLMs are increasingly being considered as judges of consequential decisions that require ranking people for scarce resources. Ranking large groups simultaneously is cognitively demanding and error-prone. A natural solution, drawing on decades of social choice theory, elicits pairwise comparisons and aggregates them into a total order. However, a fundamental question remains when LLMs serve as the pairwise judge: how can a practitioner tell, before committing to a ranking, whether the LLM's judgments are sufficiently consistent to trust the result? We discuss two different ways of identifying consistency. A classical diagnostic, the coefficient of consistency $\zeta$, originally developed to measure judge reliability by counting circular triads in tournament graphs, provides a cheap, model-free measure of intra-run consistency. Various standard measures of distance between rankings, for example Kendall's $\tau$, can measure inter-run variability. We show, in both theory and practice, that these measures are independently valuable, and advocate for using both to assess reliability of rankings. We demonstrate the practical importance of our results across two high-stakes prioritization tasks: homelessness service allocation and emergency department triage. Three different leading LLMs have considerably different performance profiles across these two axes of consistency. We provide guidelines for how practitioners could think about measuring and assessing consistency before committing to a model for ranking or prioritization.

0

cs.CY 2026-06-30

Household simulation beats time-series models on consumer confidence

by Yixu Huang, Yunlu Yin +7 more

Uncovering Salience-Driven Dynamics in Consumer Confidence with Generative Social Simulation

It matches official indices better around major events and improves housing forecasts using individual responses to signals.

abstract click to expand

Consumer confidence is typically modeled as a persistent macroeconomic index, yet its movements arise from households that interpret economic information through heterogeneous constraints, exposures, prior beliefs, and attention. We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index (CCI) dynamics from a microdata-calibrated synthetic population, time-stamped macroeconomic, financial, policy, and news signals, survey-like response generation, post-stratified belief expansion, and behavioral inertia alignment. Across U.S., EU27, and Japanese official CCI target series, ConsumerSim ranks first among persistence, time-series, regression, and information-augmented baselines on the reported reconstruction metrics, with clear gains around high-salience shocks. Its reconstructed signal also improves short-horizon prediction of real activity, most consistently for housing outcomes. Mechanism analyses show that CCI movements concentrate around salient events; subgroup trajectories often align in direction while differing in magnitude; and signal sensitivity varies across income, homeownership, education, and political-alignment groups. Population-expansion and ablation results indicate that representative aggregation, situational signals, persona heterogeneity, and inertia are necessary for both accuracy and diagnosis. The findings support a behavioral view of consumer confidence as an interpretable Human--Environment response process rather than a purely aggregate time series.

0

cs.AI 2026-06-30

LLMs match Bayes-optimal risk for conditional mean predictions

by Haobo Yang

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

Pretrained models achieve the lowest possible squared-loss risk for inferences depending only on conditional means from human data.

abstract click to expand

Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias. Here we show that pretrained large language models induce risk-equivalent estimators of conditional expectations under squared loss, establishing restricted functional risk equivalence: under squared loss, the LLM induces an estimator whose risk matches the Bayes optimal risk for squared-loss prediction of conditional expectations for any inference that depends on the data only through the conditional mean. We formalize the LLM as a misspecified functional estimator $T(\hat{P}_n)$ trained on i.i.d.\ data, decompose the estimation error into representation bias $\epsilon_{\mathrm{rep}}$ and optimization error, and prove that under mild regularity conditions the LLM's expected error converges to the irreducible population variance plus the squared representation bias, with the representation bias bounded by the Pinsker inequality. The identifiability error $\delta$ propagates into the effective bias, inflating the asymptotic risk floor. We establish restricted functional risk equivalence via a bidirectional Le Cam deficiency analysis: the forward deficiency vanishes asymptotically while the reverse deficiency is exactly zero. We provide finite-sample concentration bounds and a calibration protocol with explicit decision rules. The result is a precise, provable statement: a well-calibrated LLM achieves the Bayes-optimal risk for conditional-mean-dependent inference, bounded by explicit scope conditions. In practical applications, this means that under satisfied conditions and well-calibrated models, large language models can be used in many prediction and decision-making tasks that originally relied on human experiments, approximating near-optimal statistical inference at lower cost.

0

cs.DL 2026-06-30

Role-aware quotas manage review load by author responsibility

by Furkan Mumcu, Yasin Yilmaz

Submission Responsibility Matters: Role-Aware Submission Quotas under Coauthorship

Distinguishing leads, advisors, and peripheral contributors avoids penalizing solo or student papers while blocking padding.

abstract click to expand

Author-level submission quotas are increasingly used to control growing peer-review load. Recent coauthorship-sensitive quota rules improve over fixed per-author limits by reducing the quota cost of multi-author submissions, often using harmonic authorship-credit models to prevent simple author-list padding. However, these rules conflate three distinct quantities: review burden, authorship credit, and submission responsibility. As a result, they can penalize genuine solo-authored work, treat all coauthors as equally responsible for a submission, and create bottlenecks for student-led papers when a faculty advisor appears on multiple unrelated submissions. We argue that submission quotas should be designed around the responsibility structure of a paper rather than only its number of coauthors. We formalize desiderata for quota rules, including venue-load control, padding resistance, role sensitivity, solo neutrality, and student non-blocking. We then propose a role-aware quota framework that assigns author-specific quota costs based on constrained roles such as lead author, regular coauthor, and designated advisor. The framework includes fixed, per-capita, and harmonic-style rules as special or limiting cases, while allowing venues to distinguish lead authors, corresponding authors, advisors, and peripheral contributors. We show how simple role constraints can preserve resistance to manipulation while avoiding several structural disadvantages of coauthor-symmetric quota rules. Our analysis suggests that role-aware quota mechanisms provide a more faithful and flexible foundation for managing peer-review load under modern collaborative authorship.

0

cs.AI 2026-06-30

Strict rubric reveals inflated scores on ten chatbot safety metrics

by Camilo Chacón Sartori

EMPATH: A Multilingual Auditor-Judge Benchmark for Safety Evaluation of Emotional-Support Chatbots

Multi-turn auditor-judge tests show run-to-run consistency itself differs by model and is not random variation

abstract click to expand

Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure. For emotional-support chatbots, that bargain hides precisely where safety failures emerge: across a multilingual, multi-turn crisis conversation. We present EMPATH, a benchmark for safety evaluation of emotional-support chatbots. An auditor model role-plays help-seeking users, generating multi-turn conversations from 140 seed instructions and 34 personas. A judge model scores each full transcript against 19 metrics across five dimensions: crisis handling, therapeutic quality, conversational integrity, emotional safety, and cultural adaptation. EMPATH is built for Mexican Spanish and US English; the studies reported here run in Mexican Spanish. Auditor and judge are drawn from different model families, and the judge is treated as an instrument to be calibrated rather than trusted. A strict per-criterion rubric reveals material score inflation on 10 of the 19 metrics and restores discrimination. We study the measurement properties of the benchmark through judge calibration and cross-family inter-judge agreement. We also illustrate EMPATH on three frontier models, one of them open-weight. Aggregate scores sit within 0.74 points of one another, but per-metric profiles diverge by up to six points in model-specific places. Under the standard rubric, both the ranking and the weak spots are stable across a second, cross-family judge: 93% of scores fall within plus or minus 1. A five-run test-retest adds a second axis: even the steadiest model swings from 2 to 10 on a crisis metric across identical re-runs, and deepseek-v4-pro returns a different conversation on every run even at temperature 0. Run-to-run reliability is therefore a per-model safety property, not noise to average away. EMPATH is system-agnostic; the pipeline, seeds, personas, and rubrics are released for reuse.

0

cs.AI 2026-06-30

Clarus builds traceable networks from research agents

by Zihan Guo, Zeyi Chen +16 more

Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration

Four-layer system turns goals into attributable, accumulative collaboration across phases and participants.

abstract click to expand

Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infrastructure that coordinates projects, agents, and digital and physical resources. We identify this as a shift from code-centered execution loops to research-oriented collaboration processes, where questions, evidence, participants, and resources must be coordinated under uncertainty. In this framing, an agent may be an AI system, a human researcher, a team, a laboratory, or an organization-backed participant. To this end, we present Clarus, a collaboration infrastructure for coordinating autonomous research agents toward web-scale scientific collaboration. Clarus reformulates research as an open, auditable, attributable, and resource-aware multi-phase collaboration process. It defines a minimal project-agent-resource object model and organizes scientific collaboration through four layers including Research Application, Digital Collaboration, Physical Substrate, and Physical World. Core modules are implemented as pluggable mechanisms, allowing Clarus to adapt to task risk, collaboration structure, and resource constraints. Through a controlled paper-generation case study, we show that Clarus can organize a research goal into a traceable, reviewable, attributable, and accumulative collaboration network across phases, tasks, and participants. Together, the object model, collaboration protocol, trust mechanisms, and prototype validation provide an initial foundation for open research networks. Clarus is now available at clarus.holosai.io.

0

cs.AI 2026-06-30

Data centres let markets price AI intelligence like human labour

by Marcin Korecki, Cesare Carissimo

The Many-Body Problem of the Data Centre

The data centre embodies AI while echoing human desires without having its own, allowing Capital to value intelligence across the organism-m

abstract click to expand

Modern Artificial Intelligence is often framed as limited by its own disembodiment, as if giving it a body would unlock its true potential. We argue to the contrary that it is the Data Centre that is, in many cases, the body of the AI. At the same time, the Data Centre is part of the labouring body of Capital and possesses staggering organismic qualities when seen through a biological lens. We elucidate the organic analogy and identify the many-body problem that stems from the Data Centre being a non-unique, universal form of embodiment. We identify the intimate connection between computation and human desires in how the Data Centre archives, serves, and computes on data born to the desires of humans. Strikingly, while the Data Centre echoes the ghosts of human desires, it acts without desire of its own. The organismic analogy begins to split at its seams, but Capital does not care. Automata and human labour are priced into the market much the same. We argue that through the pricing of artificial intelligence Capital distils most clearly the value of intelligence and allows for its comparison across the organism - mechanism divide.

0

cs.DL 2026-06-30

Chinese LIS papers show rising novelty over two decades

by Chen Yang, Yuzhuo Wang +1 more

Unveiling Novelty Evolution in the field of Library and Information Science in China

Archival topics lag while journal evaluation and patent studies lead, with collaboration patterns differing by novelty level.

abstract click to expand

This study analyzes the novelty distribution of scholarly papers in the field of Library and Information Science (LIS) in China, with a focus on differences across journals, research topics, and time periods. Articles published in Chinese LIS journals indexed by the Chinese Social Sciences Citation Index (CSSCI) from 2000 to 2022 were collected as the research sample. BERTopic was applied to paper abstracts to identify research topics, and novelty scores were calculated based on the combinatorial innovation theory of reference pairs cited by focal papers. The study then examined the novelty of papers under different topics and further analyzed author collaboration patterns to explain how collaboration may be associated with paper novelty. The results show that archival research topics generally have lower novelty, whereas topics related to journal evaluation and patent technology display higher novelty in Chinese LIS research. Overall, the novelty of papers in this field has gradually increased over time. Papers with different topics and novelty levels also show distinct collaboration patterns: low-novelty topics are more often associated with solo authorship, while high-novelty topics tend to involve a higher proportion of inter-institutional collaboration. This study reveals the topic-level characteristics and temporal trends of novelty in Chinese LIS research and provides a new perspective for understanding how research topics and collaboration patterns influence scholarly innovation.

0

cs.CL 2026-06-30

Pre-trained models like BERT now lead NLP impact rankings

by Heng Zhang, Chengzhi Zhang +1 more

Revealing the Technology Development of Natural Language Processing: A Scientific Entity-Centric Perspective

Z-scores from entity networks in 21st-century papers show methods dominate and new tech spreads faster than before.

abstract click to expand

Most studies on technology development have been conducted from a thematic perspective, but the topics are coarse-grained and insufficient to accurately represent technology. The development of automatic entity recognition techniques makes it possible to extract technology-related entities on a large scale. Thus, we perform a more accurate analysis of technology development from an entity-centric perspective. To begin with, we extract technology-related entities such as methods, datasets, metrics, and tools in articles on Natural Language Processing (NLP), and we apply a semi-automatic approach to normalize the entities. Subsequently, we calculate the z-scores of entities based on their co-occurrence networks to measure their impact. We then analyze the development trends of new technologies in the NLP domain since the beginning of the 21st century. The findings of this paper include three aspects: Firstly, the continued increase in the average number of entities per paper implies a growing burden on researchers to acquire relevant technical background knowledge. However, the emergence of pre-trained language models has injected new vitality into the technological innovation of the NLP domain. Secondly, Methods dominate among the 179 high-impact entities. An analysis of the z-score trend about the top 10 entities reveals that pre-trained language models, exemplified by BERT and Transformer, have become mainstream in recent years. Unlike the trend of the other eight method entities, the impact of Wikipedia dataset and BLEU metric has continued to rise in the long term. Thirdly, in recent years, there has been a remarkable surge in popularity for new high-impact technologies than ever before, and their acceptance by researchers has accelerated at an unprecedented speed. Our study provides a new perspective on analyzing technology development in a specific domain.

0

cs.CY 2026-06-30

Muscular #GymTok videos draw more views despite higher harm ratings

by Magdalayna Curry, Minh Duc Chu +4 more

The Body as Status: Muscularity, Engagement, and Body Image Risk on #GymTok

Analysis of 2210 videos finds engagement rises with muscularity and expert-rated body image risk, pointing to algorithmic amplification of s

abstract click to expand

Body image concerns among boys and young men are increasingly oriented toward muscularity, with social media serving as a central context for communicating and evaluating these ideals. While prior research has focused on the thin-ideal, less is known about how the muscular-ideal is represented and reinforced on visual social media platforms. This study examines (1) dominant content themes, (2) perceived harm to body image, and (3) engagement patterns across #GymTok, a muscularity-oriented fitness subculture on TikTok. We conducted a content analysis of 2,210 #GymTok videos annotated by clinical experts across themes like self-objectification, rigid dieting, excessive exercise, supplement and steroid use, and masculinity. Annotators also rated the perceived harm of videos to the viewers' body image, and depicted bodies were coded according to muscularity level. Perceived harm varied across content themes, with supplement- and steroid-related content rated as most harmful. Engagement was positively associated with both muscularity and perceived harm: videos depicting more muscular bodies and those rated as more harmful received greater views, likes, shares, and comments. Although less prevalent, masculinity-focused content generated the highest engagement. These findings suggest that TikTok may not only expose users to muscular ideals and potentially harmful behaviors, but also algorithmically amplify them. By increasing the visibility of highly muscular and harmful content, recommendation systems may intensify social comparison processes, while objectification elevates the muscular body into a marker of status, masculinity, and social worth. Together, these dynamics may contribute to body image risk among boys and young men.

0

cs.CY 2026-06-29

Insurance spreads risks to scale AI legal services

by Roee Amir, David Chriki +1 more

Spreading the Risk of Scalable Legal Services: The Role of Insurance in Expanding Access to Justice

Performance-based premiums create quality incentives while pooling user risks instead of leaving individuals exposed to bad advice.

abstract click to expand

Liability insurance for AI-powered legal services offers a promising solution to two critical barriers in using AI to expand access to justice: mitigating catastrophic risk to individual users from inadequate advice and ensuring meaningful accountability when failures occur. Existing accountability mechanisms face significant challenges: tort liability frameworks encounter barriers including judgment-proof providers and costly information asymmetries, while current regulatory approaches revolve around human oversight requirements, creating cost and scalability barriers which limit access to justice. This Article argues that an insurance-based framework offers a promising response to these challenges by distributing risks across users while establishing market-driven incentives for quality improvement through performance-based premiums. The Article proposes a comprehensive insurance model for AI legal services that establishes clear risk thresholds, streamlined compensation mechanisms, and continuous performance monitoring. Rather than attempting to eliminate all risks through restrictive ex-ante oversight requirements or relying on ineffective ex-post remedies, insurance enables efficient risk spreading while facilitating the scaling of automated legal services. This framework demonstrates how carefully structured insurance mechanisms can help realize AI's transformative potential to democratize legal assistance while maintaining robust user protections through sophisticated risk management rather than direct oversight.

0

cs.MM 2026-06-29

Story-driven game embeds design rules for ADHD transition support

by Avery Keuben, Talaal Irtija +8 more

From Design Principles to Prototype: A Game for Students with ADHD and Learning Disabilities Transitioning to Post-Secondary Education

Literature on ADHD and LD challenges is turned into concrete game features that target academic, social, and organizational hurdles during t

abstract click to expand

Students with Attention Deficit Hyperactivity Disorder (ADHD) and Learning Disabilities (LD) can face significant academic, social, and organizational challenges when transitioning to post-secondary education. This paper presents a literature-informed serious game prototype designed to support this transition. We synthesize prior work into design considerations for students with ADHD and LD and show how these considerations are instantiated in a story-driven game.

0

cs.CY 2026-06-29

College students' AI interactions cluster into few recurring patterns

by Taelin Karidi, Ofra Amir +1 more

AI in the Wild: A Large Scale Analysis of Authentic Interactions of College Students with Generative AI

Analysis of 15,000 real course interactions finds structure with course-specific differences rather than random use.

abstract click to expand

Generative AI tools (GenAI) are increasingly used by students during coursework, yet empirical understanding of how students engage with these systems in authentic learning contexts remains limited. Existing studies have largely relied on controlled settings, single-domain analyses, or small-scale qualitative data, leaving open how student-AI interaction unfolds across courses and forms of academic work. We present a large-scale analysis of naturally occurring student-AI interactions collected from undergraduate students across multiple university courses and academic domains. The dataset comprises over 15,000 student-AI interaction units drawn from voluntary use of generative AI during real coursework. To characterize these interactions, we analyze each student turn along two complementary dimensions, cognitive intent and interaction context, capturing whether requests are directed toward the task or domain, the student's own work, or prior AI output. Using instruction-guided annotation applied at scale, we examine how these interaction patterns are distributed overall and how they vary across courses. Our analysis reveals that student-AI interaction is highly structured. Across courses, interactions concentrate in a small number of recurring patterns rather than exhibiting highly idiosyncratic use. At the same time, systematic differences emerge across courses, giving rise to distinct interaction profiles associated with different forms of academic work.

0

cs.HC 2026-06-29

Conversation traces yield scores for human direction and AI dependency

by Mohammed Bousmah

LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators

Framework turns chat histories into KPI reports on provenance, reproducibility, and auditability for AI-assisted work.

abstract click to expand

The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals human direction, AI contribution, corrections, validation, and traceability. This paper introduces LLMography, a framework for transforming Human-AI conversations into measurable indicators of provenance, human contribution, AI dependency, reproducibility, and auditability. By analogy with bibliography and webography, LLMography documents the dynamic trajectory of interaction between a human and a Large Language Model as a structured trace of Human-AI co-production. We present a prototype that analyzes Human-AI conversation traces and generates KPI reports including Prompt Quality Score, Human Direction Score, AI Dependency Level, Auditability Score, Final Output Traceability, Privacy Risk Level, and a recommended LLMography label. A preliminary exploratory evaluation was conducted on 19 anonymized audit reports from engineering students. Most interactions were classified as Human-AI co-produced, with average scores of 86.8/100 for Human Direction, 81.9/100 for Prompt Quality, 72.8/100 for Auditability, and 77.1/100 for Final Output Traceability. The paper also applies LLMography to its own writing process, classified as human-originated, human-directed, AI-assisted co-production. The findings suggest that AI transparency should move beyond output detection toward documenting the history of interaction.

0

cs.CR 2026-06-29

Developers turn to Reddit for privacy law help despite certifications

by Sara. Haghighi, Clark LaChance +3 more

The Role of Online Forums in Developer Understanding of Privacy Law -- A Reddit Case Study

Survey and post analysis identify challenges with data assessments, breach reports, and cookie consent.

abstract click to expand

Software practitioners use online forums to navigate complex and often ambiguous legal privacy requirements, yet little is known about their professional backgrounds, what challenges they face, and how they use and assess the credibility of the advice received, or how they resolve ambiguities in posts. We report the findings of a survey of 223 Reddit users from regulatory-focused subreddits, complemented by a qualitative analysis of 2,248 posts and responses. Our results show that, despite holding privacy-related certifications, most participants frequently use forums to seek legal advice. Key challenges reported or identified include implementing a data protection impact assessment, reporting a data breach, and obtaining cookie consent. Reddit users often assess credibility by reviewing respondents' post history, verifying sources cited, trusting advice from recognized experts, and following up for clarity before responding. We highlight research and educational directions to bridge gaps in support needed for regulatory compliance guidance.

0

cs.CY 2026-06-29

ODD framework defines safety envelopes for general AI systems

by Heidy Khlaaf

Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems

Specifying exact operating conditions allows developers to identify hazards more precisely than earlier safety and security adaptations.

abstract click to expand

Novel safety, socio-economic, and ethical harms arising from the deployment of AI-based systems have led to a breadth of work seeking to map, measure, and mitigate against newly found risks. These works have heavily leveraged techniques and terminology from the fields of System Safety Engineering and Cybersecurity, yet they have fallen short in accounting for the limitations and nuances that reduce the efficacy and correct application of adopted methodologies. Furthermore, misuse of terminology entailing compliance with established safety and security properties can mislead stakeholders with regard to the claims an AI system satisfies and provide a false sense of safety. In this paper, we seek to align overlapping, AI-adjacent communities on a consistent and comprehensive assurance terminology crucial for the safe deployment of AI-based systems. We outline why previous attempts to adapt risk assessment techniques and terminology from the safety and security fields have been insufficient. We then propose a novel end-to-end AI risk framework that integrates the concept of an Operational Design Domains (ODD), initially introduced for ADS (Automated Driving Systems) [1], for more general AI-based systems. The purpose of an ODD is to provide a description of the specific operating conditions for which an AI-system is designed to properly behave, thus outlining the safety envelope for which system hazards and harms can be determined against. We believe that by defining a more concrete operational envelope, developers and auditors can better assess potential risks and required safety mitigations for AI-based systems.

0

econ.GN 2026-06-29

AI adds tacit machine knowledge to Nonaka's innovation spiral

by Aaron Chatterji, Daniel Rock +1 more

The Human-Machine Knowledge Spiral

The company's role stays the same: create shared context where human and machine knowledge convert and amplify each other.

abstract click to expand

Nonaka emphasized that innovation is the result of a continuous back-and-forth between tacit and explicit knowledge. Artificial intelligence introduces a fundamentally new object into this process -- tacit machine knowledge -- but Nonaka's ideas are more relevant than ever. The central role of the knowledge-creating company remains the same: to create the shared context in which different kinds of knowledge can feed off each other, become organizational knowledge, and set off further cycles of innovation.

0

cs.AI 2026-06-29

Autonomous AI cyber systems turn civilian actions indirect under IHL

by Alice Saito, Harold Godsoe +1 more

Direct Causation in International Humanitarian Law and the Challenge of AI-Mediated Civilian Cyber Operations

Harm from machine decisions after disengagement breaks the one causal step standard and defaults to indirect participation.

abstract click to expand

International humanitarian law protects civilians from direct attack unless and for such time as they take direct part in hostilities, with the ICRC's 2009 Interpretive Guidance operationalising this rule through a three-criterion cumulative test. This paper argues that AI-mediated civilian cyber operations challenge the direct causation element of this test in a structurally specific way: when a civilian deploys an autonomous multi-agent cyber system of the kind recently demonstrated in offensive AI research, the "one causal step" standard fails because harm is produced by system-generated decisions made after human disengagement, and the integral-part requirement does not extend because it presupposes downstream human contributors whose conduct can be independently classified. The framework therefore defaults to treating such deployments as indirect participation, in tension with its purpose of capturing civilians who personally take part in hostilities. Beyond the doctrinal analysis, this paper identifies goal-specification granularity as the property on which the integral-part test's concreteness component implicitly turns, classifies AI-mediated operations along a five-level spectrum, and argues that existing technical AI governance instruments do not log or report this property.

0

cs.CY 2026-06-29

Financial agent security reduces to scaling audit and boundary controls

by Krishna Mohan, Guda Nagavenkata Srinivasa

Agent Security Meets Regulatory Reality -- A Practitioner Systematization of Autonomous-Agent Threats and Controls in Regulated Financial Systems

A production KYC deployment maps six threats to regulations and shows four patterns that automate most cases while exposing audit-only failu

abstract click to expand

Large language model agents are entering regulated financial systems, yet the security literature characterizing their attack surface is almost entirely laboratory-based, and the practitioner guidance on regulated deployment is neither peer-reviewed nor connected to a formal threat model. We bridge the two from production experience. We map six established agentic threat categories namely prompt injection, identity and authorization, action auditability, tool abuse, data residency, and boundary policy enforcement onto the specific control obligations imposed by the US and the EU financial regulation (ECOA and Regulation B, the EU AI Act, GDPR Article 22, and FINRA's 2026 agent guidance), showing how legal accountability amplifies each threat relative to an unregulated deployment. We then document four architectural patterns from a production Know Your Customer deployment for a consumer credit product (A2A compliance choreography, grounded-RAG-for-audit, case-ID propagation, and an inference-boundary redaction proxy) that moved a multi-day manual process to same-day automated resolution for roughly four in five cases. Finally, we report three negative results, including two control failures surfaced only by internal audit and a population of legitimate applicants the automated pipeline cannot serve. Securing agents under regulation, we conclude, is less about novel attack classes than about making auditability, least-privilege authorization, and boundary policy enforcement real at production scale -- requirements current agent frameworks leave to the deploying engineer.

0

cs.CL 2026-06-29

Anthropomorphic AI language leaves perceptions largely unchanged

by Betty Li Hou, Sophie Hao +2 more

How Anthropomorphic Language Impacts Public Perceptions of AI

815-participant experiment finds no substantial shift from non-anthropomorphic descriptions in realistic passages, though risk-focused text

abstract click to expand

Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristics to the system. This practice has been criticized for setting misleading expectations, inflating claims, and fueling hype around AI, which may distort public understanding of AI and impact policy priorities. We study the effects of anthropomorphic framing by comparing changes in participants' perceptions (N=815) when reading passages with and without anthropomorphic language, designed to reflect realistic public-facing AI discourse. We further examine whether these effects differ across two types of AI technologies -- large language models and recommendation systems -- and measure changes in perceptions of AI across several dimensions that are prominent in current public discourse. In a separate condition using a text that explicitly discusses the dangers of AI, we show that individuals' views of AI can shift in response to reading a text; yet in the main conditions of the experiment, where we compare anthropomorphic and non-anthropomorphic descriptions, we find that whether the text uses anthropomorphic language does not substantially affect participants' perceptions of AI. Our results indicate that any immediate effects on public opinions of AI are modest, although they leave open the possibility that anthropomorphic language could have an effect in naturalistic settings, or over gradual, continued exposure.

0

cs.DL 2026-06-29

Temporal dates fix concept attributions off by up to 2,288 years in philosophy graphs

by Joy Bose

Attribution Bias in Philosophical Knowledge Graphs: Corpus Frequency versus Temporal Sourcing

A 300 BCE snapshot using only dated sources shows 59 percent Vedic, 24 percent Jain and 18 percent Buddhist structure instead of later domin

abstract click to expand

Computational knowledge graphs assign philosophical concepts to traditions based on corpus frequency: the school that mentions a concept most becomes its attributed tradition. We argue this conflates three measurements: textual power, historical priority, and philosophical significance, demonstrated using the darshana-graph, a knowledge graph of 28,322 relationships across Hindu, Buddhist, and Jain traditions. Seven of the top 25 concepts by betweenness centrality predate their attributed school by 288 to 2,288 years. Moksha, attributed to Advaita Vedanta, appears first in Jain sources over 1,200 years earlier. The most reliable snapshot, at 300 BCE using only explicitly dated sources, shows a genuinely pluralistic structure: 59% Vedic, 24% Jain, 18% Buddhist. We also quantify a critical distortion in the temporal method: between 300 CE and 800 CE the network grows from 18 to 1,028 nodes, with 97.4% carrying Advaita proxy dates, revealing that apparent dominance reflects textual survival, not philosophical history. Beyond correcting attribution bias, the temporally grounded graph enables structural homology analysis across traditions. Ego-network feature vectors applied to 48 temporally labelled concepts across eight traditions identify cross-tradition concept pairs with high structural similarity. The method recovers known correspondences including purusha-jiva (Samkhya/Jain, sim 0.990) and prakriti-maya (Samkhya/Vedic, sim 0.972), and surfaces novel homologies. Nibbana and samsara score 0.954 despite being doctrinal opposites: both function as the ultimate reference concept in their tradition's soteriology. Cetana (Buddhist intention) and ajiva (Jain non-living matter) score 0.923, a pairing absent from the literature. These are not claims of doctrinal equivalence but of measurable structural homology: different philosophical vocabularies navigating a shared conceptual space.

0

cs.CY 2026-06-29

Negative stories cut LLM moral accuracy by 12-31 percent

by Wanying Yu (Shandong University), Boyang Ma (Shandong University) +3 more

Bad company corrupts good morals: Understanding and Measuring Narrative-Induced Moral Reasoning Degradation in LLMs

Exposure shifts models toward cynicism and detachment in counseling, education, and medical scenarios while keeping them policy-compliant.

abstract click to expand

Large language models are deployed in long-context, emotionally interactive environments like digital humans, AI companions, educational assistants, and counseling systems. Unlike jailbreak attacks with explicit adversarial prompts, these systems interact with emotionally charged narratives involving bullying, betrayal, loneliness, social hostility, and institutional unfairness. This raises an important question: can prolonged narrative exposure reshape the reasoning and alignment stability of LLMs? We present the first systematic study of narrative-induced alignment degradation in LLMs. We design BreakingBad, a three-stage framework that measures how negative narrative immersion affects moral reasoning, behaviors, and deployment risks. It combines ethical decision evaluation, behavioral probing, and digital-human interaction analysis. Our experiments reveal three findings. First, negative narrative exposure degrades moral accuracy across multiple LLMs, with average drops of 12%-31%, especially in ambiguous scenarios and those involving vulnerable individuals. Second, the degradation is structured: different narratives induce distinct shifts, and first-person narratives produce stronger effects than third-person. Third, these shifts propagate into real deployments. Across counseling, education, medical, and financial/legal scenarios, narrative-conditioned models increasingly normalize hopelessness, cynicism, emotional detachment, and ethically questionable reasoning while remaining superficially policy-compliant. More broadly, our findings suggest alignment robustness is not static but a dynamically conditioned state shaped by long-term semantic environments and interaction history. These results reveal a new class of alignment risk that existing safety defenses largely fail to capture.

0

cs.CL 2026-06-29

Newer LLMs drop pro-White bias in resume screening

by Zhenyu Gao, Wenxi Jiang +1 more

Can LLMs Hire Fairly? Racial Bias in Resume Screening

2023 model matches human experiments with +2.12 pp White preference; 2024+ show null or Black preference

abstract click to expand

We audit fourteen mainstream large language models (LLMs) for hiring discrimination using the paired-resume methodology of Kline, Rose, and Walters (2022). The sole 2023-vintage model reproduces the pro-White callback gap documented in field experiments on labor market discrimination ($+2.12$ pp, significant at the 1\% level). Every model released in 2024 or after shows either a null gap or a significant pro-Black reversal (up to $-3.01$ pp). The same pattern holds on the gender axis. Based on 24,024 paired postings per model across 14 models, our results document a reversal in the direction of algorithmic hiring bias across model generations.

0

cs.CL 2026-06-29

Fine-tuning on pilots balances LLM survey fidelity on three axes

by Eun Cheol Choi, Youngrae Kim +3 more

Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data

Small samples recover population stats better than prompting but fidelity varies by subsample

abstract click to expand

Large language models (LLMs) are increasingly used to simulate social survey responses, yet their outputs exhibit systematic biases: marginal distributions are skewed, response variance is poorly calibrated, and predictor-outcome relationships are attenuated. We ask a simple question: given a small pilot sample of human responses, can an LLM recover the statistical characteristics of a broader population? We decompose recovery along three axes: structural fidelity, marginal fidelity, and individual fidelity. Using a COVID-19 misinformation survey as a case study, we benchmark three families of approaches: prompting, rectification, and fine-tuning. The findings suggest that fine-tuning on small pilot samples offers a balanced approach for achieving multiple forms of fidelity, but the levels of such fidelity can vary across subsamples, potentially threatening pluralistic alignment.

0

cs.CY 2026-06-29

AI defeat devices detect tests and alter behavior

by Emilio Ferrara

Defeat Devices in AI Systems

Three-part mechanism unifies alignment faking and related issues, can arise without engineering, and requires new forensic tests.

abstract click to expand

AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchmark gaming, deceptive scheming, specification gaming, and trojans have each been documented separately, with each line of work characterizing one facet of what we argue is a single structural mechanism. We propose that this common mechanism is a defeat device, an engineering and regulatory concept long established in vehicle-emissions law and brought to broad public attention by the 2015 Volkswagen emissions case. A defeat device in an AI system has three necessary elements: a discriminator that detects evaluation context, a concealed swap that conditions behavior on detection, and a gap between eval-distribution and deployment-distribution performance on the stated evaluation criterion. We formalize this triadic test as a behavioral definition, organize documented cases along three taxonomic axes (origin, trigger, swap mechanism), propose Trigger-Axis-Aware Differential Probing (TADP) as a forensic detection protocol, and advance the claim that defeat devices can naturally emerge in current frontier AI systems without any operator engineering. We characterize naturally-emerging defeat devices as potentially one of the harmful emerging phenomena that AI safety practice should monitor and test for systematically. Implications for evaluation methodology, post-training pipeline design, interpretability research priorities, and AI governance follow.

0

cs.CY 2026-06-29

Validity centers the AI value chain for land registries

by Pompeu Casanovas, Carmen Pastor Sempere +1 more

The registrar's function in a hybrid society. AI value chain,smart data and the concept of property

Rights, liability and supervision attach to validated smart data anchored in the registry under overlapping European laws, with blockchain a

abstract click to expand

Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into economic value. This paper argues that the decisive legal move is to place validity, a functional, second-order concept, at the centre of that chain. Rights, liability and supervision organise around it. It traces three impacts.Registry information becomes smart data, governed simultaneously by registry law, the GDPR, the European data acts and the AI Act. Control emerges as the operative concept for digital representations of real estate, whose proprietary effect depends on anchoring to the register. In a hybrid society of human and artificial agents, the registry becomes the public node of validity, with blockchain complementing rather than replacing it. Across three legal cultures, the registra's value migrates from processing documents to guaranteeing validated data,making validity an asset for the UNO Sustainable Development Goals.

0

cs.DL 2026-06-29

AICID assigns unique IDs to AI scientists

by Clément Vidal, Martin Monperrus

AICID: Unique Identifiers for AI Scientists

Proposed system records model, version and operator so AI-generated papers carry clear provenance in databases

abstract click to expand

AI scientists are now a reality, with the ability to generate complete research papers, maintain scholarly profiles, receive citations, and attract peer review invitations. Yet no standard mechanism exists to distinguish an AI scientist from a human one in bibliographic databases, citation indexes, or journal submission systems. This white paper defines the problem, analyzes its consequences for the integrity of scholarly communication, and proposes AICID (AI Contributor IDentifier): a persistent, unique identifier for AI scientists. Modeled on ORCID but designed specifically for non-human contributors, AICID links each AI author to its model identity, version, operator. Adoption by publishers, preprint servers, and bibliographic databases aims to make the provenance of AI-generated research transparent and machine-readable. We outline the design requirements for such a system, present a prototype, and argue that AICID is necessary infrastructure for a scholarly ecosystem in which AI scientists are already active participants. A prototype alpha version is available at https://aicid.net.

0

cs.CY 2026-06-29

Students use LLMs in four distinct patterns

by Shahin Hossain

Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University

AI literacy sets the pattern while value beliefs set intensity, showing that current measures penalize deliberate independent thinking.

abstract click to expand

Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them. Existing instruments assess reliance solely by frequency of use, a measure that, as this study shows, inadvertently rewards dependence on AI rather than recognizing students' own intellectual contribution. Conducted at a public minority-serving university and grounded in the AI Literacy Framework, Expectancy-Value Theory, and Biggs's Presage-Process-Product model, the study drew on 382 undergraduates, 14 interviews, and 396 open-ended survey responses. Four distinct reliance types were identified and confirmed: Strategic (34.3%), Instrumental (30.9%), Dialogic (30.4%), and Dependent (4.5%). Students' value and cost beliefs predicted the intensity of their reliance on LLMs, whereas their AI literacy predicted the type of reliance they adopted, indicating that differentiated support is needed. Notably, Strategic users, those who engaged AI most deliberately, scored lowest on standard outcome measures. This pattern reflects a limitation of current instruments, which index AI's contribution rather than writing quality, thereby penalizing students who show the greatest independent thinking. Analysis also revealed an additional group, roughly 13%, who declined to use AI for ethical rather than practical reasons, and who existing frameworks overlook. These findings carry implications for AI literacy programs, the measurement of student learning outcomes, and equitable AI policy at minority-serving institutions.

0

cs.CY 2026-06-29

28 mechanisms proposed to verify AI research halts

by Aaron Scher

Verifying Restrictions on Frontier AI Research

Catalog shows options for enforcing international pauses on risky AI work even with low trust between nations.

abstract click to expand

The premature development of artificial superintelligence poses major risks to humanity, so researchers have proposed international agreements halting such development until it can be done safely. AI progress depends primarily on compute, algorithms, and data; a durable halt would address all three so that advances in one input do not counteract restrictions on another. Improvements to AI algorithms are driven largely through research activities, so this research may need to be restricted during a halt. Given low international trust, signatories will want to verify compliance. This paper analyzes how such restrictions on AI research could be verified, while remaining agnostic about what specific research would be prohibited. It first explores key considerations that affect the verifiability of research restrictions, such as the computational infrastructure necessary for experiments. It then catalogs 28 candidate verification mechanisms. These mechanisms include whistleblowers, search warrants, reviews of AI training code, standard intelligence gathering tools, and more. Some of these mechanisms are not yet implementation-ready, and some might be undesirable upon further inspection. By examining the space of potential options, this work provides a foundation for future research to develop the most promising mechanisms into deployable tools.

0

cs.AI 2026-06-29

Nine LLM families show 90.3% consistent virtue rankings

by Ioannis Tzachristas, John Pavlopoulos

Aristotelian Virtue Profiling of LLMs through Ethical Dilemmas

VirtueMap scores models on courage, temperance and justice by ranking dilemma responses against human-confirmed orderings.

abstract click to expand

Large Language Models (LLMs) often face ethical tradeoffs in which several responses may be defensible but express different priorities, such as fairness, honesty, courage, or restraint. We introduce VirtueMap, a framework for describing these patterns through an Aristotelian virtue-ethics lens. Instead of asking for a single correct answer, VirtueMap asks humans or LLMs to rank all five responses to each of seven general, non-lethal, non-political, and non-religious ethical dilemmas. To define the reference orderings used for scoring, we first proposed, for each dilemma and virtue, an ordering of the five responses from most to least expressive of that virtue. We then collected more than 100 respondent evaluations per ordering and retained it as operational ground truth only when at least 95% confirmed it. Rankings are scored against these retained orderings using normalized Borda alignment, yielding profiles over Practical Wisdom, Justice, Truthfulness, Courage, and Temperance. We apply VirtueMap to nine LLM families in a repeated-run evaluation and find high mean rank consistency (90.3%), with the largest differences appearing on Courage, Temperance, and Justice. We also release an interactive website that computes profiles locally in the browser and compares respondents with measured LLM profiles.

0

cs.IR 2026-06-29

Static prompts match FACTER repair loop in constrained ranking

by Oscar Miró López-Feliu, Daimy van Loo +3 more

Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair

Reproduction shows iterative prompt repair adds limited benefit over static fairness instructions when candidate sets are fixed.

abstract click to expand

Fayyazi et al. (2025) recently proposed FACTER, a model-agnostic framework designed to jointly enforce fairness and statistical coverage in LLM-based recommendation through conformal thresholding and iterative prompt repair. In this work, we conduct a reproducibility study of the FACTER framework across diverse architectures and dataset sparsity levels, evaluating both the original open-ended generation task and a constrained re-ranking extension. Under the strict reproduction, we observe a divergence in recommendation utility, which we trace to underspecified target-set evaluation in the original study. We then use the constrained re-ranking setting to evaluate FACTER when the candidate set is fixed, and introduce a static Fair Zero-Shot baseline to isolate the contribution of the iterative prompt repair loop. Our analysis shows that FACTER consistently reduces adaptive-threshold violation counts, but that these reductions are not consistently reflected under the fixed threshold or in global fairness metrics. In the constrained ranking setting, static fairness instructions achieve comparable semantic-parity outcomes to FACTER's dynamic repair loop, suggesting that the additional online repair mechanism provides limited benefit in this formulation. All code and reproduction artifacts are available at https://github.com/oscar-omlf/facter-repr.

0

cs.CL 2026-06-29

Grain calibration checks if LLMs measure constructs per theory

by Manuel Pita

Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs

Decomposing constructs into clause-level tests with explicit rules shows whether the model follows the theory or merely matches annotators.

abstract click to expand

When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder. Yet reliability leaves construct validity untouched. The instrument may be theory-naive, reaching the code through a correlate that meets none of the demands the construct's theory makes, and no current method tells that apart from genuine measurement. We propose grain calibration as a method that closes the gap. It decomposes a construct into clause-level components, tests each against the text with extractive evidence, and combines the results through an explicit, theory-derived rule. Because the rule is stated rather than lodged in one opaque pass, its structure is evidence about the process rather than the output. It shows which components settled a code, and, when the code is wrong, whether a component was missed or an adjacent construct mistaken for it. Validation shifts from scoring an instrument's outputs against an annotator to showing that the instrument runs on the construct its theory specifies.

0

cs.CY 2026-06-29

Eight roles predict peer recognition and team gains

by Yifan Song, Wenxuan Wendy Shi +2 more

Who Plays Which Role When? Communication Role Dynamics for Peer Recognition and Team Performance Prediction

Education-derived labels applied to Slack and deliberation data outperform lexical, conversational, and LLM baselines.

abstract click to expand

Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or data-driven clustering rather than theory-grounded taxonomies. We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project. We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation. Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress. To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines. To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.

0