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834 papers, from 4 September 2026 to 5 November 2026: 834 arXiv preprints.
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Newest papers, page 27
Visibility on Terrains
This chapter surveys terrain visibility models, algorithms, and applications.Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals
Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed from the…Travel Mode- and Purpose-Specific Origin-Destination Matrices for England and Wales from Fused Travel Survey and Mobile Network Data
Origin-destination (OD) matrices sit behind much of quantitative transport planning, from model calibration and accessibility analysis to the appraisal of new services and development.Retrieval Sensitivity to Identity Signals in Queries
Dense retrievers decide which documents reach users and the language models that use them, yet they are typically evaluated with neutral queries.Selective Elicitation as a Commercial Influence Channel: A Reproducible Synthetic Shopping-Agent Stress Test
A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which preference question the assistant asks.Early Prediction of AI-Assisted Cheating Risk in Online Exams Through Learning Analytics
AI-assisted cheating has become an important threat to the security of online exams.Digital Labor Platforms as Infrastructure of Extractivism
This chapter examines how digital platforms, as infrastructures of extractivism in contemporary artificial intelligence, enable corporations to appropriate data and labour while concentrating value…Gender bias across LLMs is common and highly heterogeneous
Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with real consequences.Framing the Narrative: Ideological Mimicry in Large Language Models
Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable property.How People Use ChatGPT: Conversation-Level Evidence from India, Nigeria, Brazil, and Pakistan
Public understanding of how people use LLM-based conversational AI assistants comes primarily from aggregate platform reports by OpenAI and Anthropic, which apply fixed taxonomies and inferred…AI Agents are Vulnerable to Radicalization
Large language models (LLMs) can influence people's beliefs, yet little is known about whether and how they can manipulate each other.Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing
Major venues now publish detailed rules about how authors, reviewers, and area chairs may use AI, and those rules differ by role, by task, and by what must be disclosed.Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomenon known as anthropomorphism.Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems
Public institutions increasingly procure AI systems whose design they cannot inspect or change.Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions
Large language models (LLMs) are increasingly used in culturally sensitive settings, where alignment requires representing diverse preferences within populations.Navigating the Changing Landscape of Online Knowledge Consumption and Production in the Age of Generative AI: Evidence from Stack Overflow
Online knowledge communities rely on a division of epistemic labor between users who seek information and those who produce it.Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
AI labels are emerging as a primary safeguard for transparency about AI-generated content on social media, including under the EU Digital Services Act and AI Act.Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models
Names are personal identifiers, but they also carry social meaning and are widely used to evaluate how language models treat different people.Investigating Human--AI Discrepancies via Multiple-Solution Problems
Frontier artificial intelligence (AI) models are benchmarked on whether they reach a correct answer.Loyal Agents: Training LLM Agents to Protect Principal Interests Under Strategic Information Asymmetry
As LLMs increasingly act as delegated agents, they are expected to protect principals' interests when interacting with external parties.