February 2026

arXiv:2602.18459v1 Announce Type: new Abstract: LLMs act in the social world by drawing upon shared cultural patterns to make social situations understandable and actionable. Because identity is often part of the inferential substrate of competent judgment, ethical alignment requires regulating when and how systems invoke identity. Yet the dominant governance regime for identity-related harm remains bias mitigation, which treats identity primarily as a source of measurable disparities or harmful associations to be detected and suppressed. This leaves underspecified […]

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arXiv:2602.18458v1 Announce Type: new Abstract: Reproducibility crises across sciences highlight the limitations of the paper-centric review system in assessing the rigor and reproducibility of research. AI agents that autonomously design and generate large volumes of research outputs exacerbate these challenges. In this work, we address the growing challenges of scalability and rigor by flipping the dynamic and developing AI agents as research evaluators. We propose the first execution-grounded evaluation framework that verifies research beyond narrative review by examining […]

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arXiv:2602.18457v1 Announce Type: new Abstract: In recent years, synthetic media from deepfake videos have emerged as a new interesting technology, whether that refers to cloned voices, multilingual translation models, or more recent applications of avatar tutors into higher education. As such, these technologies are rapidly becoming part of the multilingual distance learning model and, more recently, MOOCs worldwide. This article is a scoping review that focuses on recent international literature published between 2020 and 2025 to explore the […]

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arXiv:2602.18456v1 Announce Type: new Abstract: Contemporary benchmarks for agentic artificial intelligence (AI) frequently evaluate safety through isolated task-level accuracy thresholds, implicitly treating autonomous systems as single points of failure. This single-channel paradigm diverges from established principles in safety-critical engineering, where risk mitigation is achieved through redundancy, diversity of error modes, and joint system reliability. This paper argues that evaluating AI agents in isolation systematically mischaracterizes their operational safety when deployed within human-in-the-loop environments. Using a recent laboratory safety […]

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arXiv:2602.18455v1 Announce Type: new Abstract: Search engines increasingly display LLM-generated answers shown above organic links, shifting search from link lists to answer-first summaries. Publishers contend these summaries substitute for source pages and cannibalize traffic, while platforms argue they are complementary by directing users through included links. We estimate the causal impact of Google’s AI Overview (AIO) on Wikipedia traffic by leveraging the feature’s staggered geographic rollout and Wikipedia’s multilingual structure. Using a difference-in-differences design, we compare English Wikipedia […]

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arXiv:2602.18454v1 Announce Type: new Abstract: The rapid growth of AI-driven mental health mobile apps has raised concerns about their ethical considerations and user trust. This study proposed a natural language processing (NLP)-based framework to evaluate ethical aspects from user-generated reviews from the Google Play Store and Apple App Store. After gathering and cleaning the data, topic modeling was applied to identify latent themes in the context of ethics using topic words and then map them to well-recognized existing […]

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arXiv:2602.18453v1 Announce Type: new Abstract: The replication crisis, the failure of scientific claims to be validated by further research, is one of the most pressing issues for empirical research. This is partly an incentive problem: replication is costly and less well rewarded than original research. Large language models (LLMs) have accelerated scientific production by streamlining writing, coding, and reviewing, yet this acceleration risks outpacing verification. To address this, we present an LLM-based system that replicates statistical analyses from […]

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arXiv:2602.18452v1 Announce Type: new Abstract: Respiratory diseases are a leading cause of death globally, highlighting the urgent need for early and accessible screening methods. While some lung auscultation analysis has been automated and machine learning audio based models are able to predict respiratory pathologies, there remains a critical gap: the lack of intelligent systems that can interact in real-time consultations using natural language. Unlike other clinical domains, such as electronic health records, radiological images, and biosignals, where numerous […]

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arXiv:2602.18451v1 Announce Type: new Abstract: Contemporary science education reforms such as the Next Generation Science Standards (NGSS) demand assessments to understand students’ ability to use science knowledge to solve problems and design solutions. To elicit such higher-order ability, educators need performance-based assessments, which are challenging to develop. One solution that has been broadly adopted is Evidence-Centered Design (ECD), which emphasizes interconnected models of the learner, evidence, and tasks. Although ECD provides a framework to safeguard assessment validity, its […]

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arXiv:2602.18450v1 Announce Type: new Abstract: Multi-agent language systems can exhibit a failure mode where a shared dominant context progressively absorbs individual semantics, yielding near-uniform behavior across agents. We study this effect under the name Asymptotic Semantic Collapse in Hierarchical Optimization. In a closed linguistic setting with a Dominant Anchor Node whose semantic state has effectively infinite inertia, we show that repeated interactions with Peripheral Agent Nodes drive an asymptotic alignment that minimizes a global loss. We model semantic […]

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