Cross-cultural survey of 4,641 participants shows LLM emotional support adoption varies widely by country and demographics, with socioeconomic status as strongest predictor of trust and use, and English-speaking nations more accepting than others in Europe.
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14 Pith papers cite this work. Polarity classification is still indexing.
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The conceptual multiverse system with a verification framework for decision structures helps users in philosophy, AI alignment, and poetry build clearer working maps of open-ended problems by making implicit LLM choices explicit and changeable.
Crowdsourced metaphors show rising anthropomorphism and warmth toward AI that predict trust and adoption, with notable demographic differences.
LLMs produce interpretive closure in 87.5% of ambiguous social scenarios through narrative alignment, reversal, or normative advice, with first-person perspectives increasing alignment tendencies.
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
Introduces the concept of agentic inequality and develops a three-dimensional framework (availability, quality, quantity) to analyze how autonomous AI agents could deepen or mitigate existing divides through scalable goal delegation.
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
HBHC protocol binds hierarchical credentials to heartbeat proofs for deterministic bounded-time revocation in AI agent swarms without network round-trips.
TSAssistant is a modular, human-in-the-loop multi-agent system that generates citable, section-specific drafts for target safety assessment reports by coordinating specialized sub-agents with biomedical data sources and interactive user refinement.
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
AGI may arrive by 2030-2040 and reshape global power balances, requiring Europe to close gaps in compute, talent retention, industrial adoption, and unified policy responses through a coordinated preparedness agenda.
citing papers explorer
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From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support
Cross-cultural survey of 4,641 participants shows LLM emotional support adoption varies widely by country and demographics, with socioeconomic status as strongest predictor of trust and use, and English-speaking nations more accepting than others in Europe.
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Navigating the Conceptual Multiverse
The conceptual multiverse system with a verification framework for decision structures helps users in philosophy, AI alignment, and poetry build clearer working maps of open-ended problems by making implicit LLM choices explicit and changeable.
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From tools to thieves: Measuring and understanding public perceptions of AI through crowdsourced metaphors
Crowdsourced metaphors show rising anthropomorphism and warmth toward AI that predict trust and adoption, with notable demographic differences.
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What Did They Mean? How LLMs Resolve Ambiguous Social Situations across Perspectives and Roles
LLMs produce interpretive closure in 87.5% of ambiguous social scenarios through narrative alignment, reversal, or normative advice, with first-person perspectives increasing alignment tendencies.
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Behavioral Transfer in AI Agents: Evidence and Privacy Implications
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
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The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
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Agentic Inequality
Introduces the concept of agentic inequality and develops a three-dimensional framework (availability, quality, quantity) to analyze how autonomous AI agents could deepen or mitigate existing divides through scalable goal delegation.
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Towards an AI co-scientist
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
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The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
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Heartbeat-Bound Hierarchical Credentials: Cryptographic Revocation for AI Agent Swarms
HBHC protocol binds hierarchical credentials to heartbeat proofs for deterministic bounded-time revocation in AI agent swarms without network round-trips.
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TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment
TSAssistant is a modular, human-in-the-loop multi-agent system that generates citable, section-specific drafts for target safety assessment reports by coordinating specialized sub-agents with biomedical data sources and interactive user refinement.
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Responsible Agentic AI Requires Explicit Provenance
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
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Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
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Europe and the Geopolitics of AGI: The Need for a Preparedness Plan
AGI may arrive by 2030-2040 and reshape global power balances, requiring Europe to close gaps in compute, talent retention, industrial adoption, and unified policy responses through a coordinated preparedness agenda.