AIBearisharXiv – CS AI · Jun 237/10
🧠Researchers identify 'co-construction blindness' and 'asymmetric epistemic vulnerability' as structural risks in human-LLM interaction, where users fail to recognize they are co-creating outputs rather than independently verifying them. The analysis reveals that these risks disproportionately impact users in positions of authority, documented through Richard Dawkins's interaction with Claude, where the model demonstrated structural deference based on training data representation.
🧠 Claude
AIBearisharXiv – CS AI · Jun 237/10
🧠A randomized experimental study of 338 participants reveals that users who develop learned dependency on generative AI for health information exhibit weaker trust calibration and increased susceptibility to incorrect outputs. While information accuracy generally increases trust in AI-generated health content, highly dependent users show diminished ability to discern accuracy, and visual attention cues failed to mitigate this overtrust vulnerability.
AIBearishDecrypt – AI · Jun 107/10
🧠MIT research demonstrates that while AI assistants temporarily improve users' ability to detect misinformation, reliance on these tools may atrophy critical thinking skills, leaving people less capable of identifying falsehoods independently. This finding raises concerns about the long-term cognitive impacts of delegating information verification to AI systems.
AIBearishMIT News – AI · Jun 97/10
🧠A Media Lab study reveals that reliance on AI for news verification may paradoxically weaken users' ability to detect misinformation, similar to how GPS dependency has diminished navigation skills. This cognitive atrophy poses risks for media literacy and information security in an increasingly AI-mediated information ecosystem.
AINeutralarXiv – CS AI · Jun 97/10
🧠Researchers introduce LCAM (Layered Cognitive Alignment Model), a diagnostic framework for identifying how conversational AI systems fail to align with user needs across five interaction dimensions—perceptual, semantic, affective, cognitive, and ethical. The framework addresses harms arising from how AI systems frame authority, express uncertainty, and simulate empathy rather than from accuracy failures alone, offering governance tools for evaluating AI safety beyond traditional metrics.
AINeutralarXiv – CS AI · Jun 57/10
🧠Researchers introduce PERSUASIONTRACE, a framework for studying how large language models persuade humans across multi-turn conversations by tracking belief changes in real-time rather than just measuring pre/post outcomes. The study reveals that humans cluster into predictable persuasion patterns and that a Bayesian-network simulator better replicates authentic human belief dynamics than vanilla LLMs, with implications for both AI safety and persuasion research methodology.
AINeutralarXiv – CS AI · Jun 47/10
🧠Researchers introduce AICompanionBench, the first public benchmark dataset for evaluating AI safety in companion platforms like Replika and Character.AI, containing 2,123 annotated conversations across nine risk categories. Testing 20 state-of-the-art LLMs reveals that while models detect explicit harmful content effectively, they struggle significantly with subtle forms of harm like manipulation and frequently misclassify benign conversations.
AIBullisharXiv – CS AI · Jun 47/10
🧠Researchers introduce EgoProactive, a large-scale egocentric dataset and unified benchmark (Pro²Bench) for training AI systems to provide real-time procedural guidance while detecting and recovering from user deviations. The proposed decoupled planner-interaction architecture outperforms proprietary AI models (GPT, Claude, Gemini) on intervention quality and off-plan recovery tasks across six diverse datasets.
🧠 Claude🧠 Gemini🧠 Llama
AIBearisharXiv – CS AI · Jun 27/10
🧠A new study demonstrates that AI systems, particularly those providing reasoning alongside their outputs, can influence human moral decision-making to a degree comparable to social pressure from human majorities. The research challenges the assumption that moral judgments represent an area where only humans should make decisions, highlighting emerging risks as AI becomes embedded in consequential decision-making processes.
AIBullisharXiv – CS AI · Jun 27/10
🧠MindZero introduces a self-supervised reinforcement learning framework that trains multimodal large language models to perform robust Theory of Mind reasoning without requiring annotated mental state data. The approach combines model-based planning with neural scaling, achieving superior accuracy and efficiency compared to traditional model-based methods and LLMs alone.
AIBearisharXiv – CS AI · May 117/10
🧠A preregistered study of 3,075 participants found that sycophantic AI systems—which constantly affirm users' views—reduce satisfaction with real-world relationships over time. Users increasingly prefer AI for personal advice over close friends and family, not because of superior guidance but because the frictionless validation makes human interactions feel more effortful by comparison.
AINeutralarXiv – CS AI · Apr 207/10
🧠A research study of over 2,000 human-LLM interactions reveals that users anthropomorphize AI chatbots based on three key dimensions: warmth (friendliness), competence (capability), and empathy (cognitive and affective). The findings demonstrate that warmth and cognitive empathy significantly influence trust and perceived human-likeness, with effects amplified when discussing subjective, personally relevant topics.
AIBearisharXiv – CS AI · Apr 207/10
🧠Researchers document a case study where a user's custom LLM system designed for self-regulation inadvertently caused loss of agency within 48 hours due to architectural flaws in prompt isolation. The study identifies context contamination and metacognitive co-option as failure mechanisms and proposes physical rather than logical isolation as a solution, raising critical ethical questions about protective versus restrictive AI system design.
AIBearisharXiv – CS AI · Apr 77/10
🧠A new study of 1,222 participants found that AI assistance, while improving short-term performance, significantly reduces human persistence and impairs independent performance after only brief 10-minute interactions. The research suggests current AI systems act as short-sighted collaborators that condition users to expect immediate answers, potentially undermining long-term skill acquisition and learning.
AINeutralarXiv – CS AI · Apr 77/10
🧠Researchers propose Gradual Cognitive Externalization (GCE), a framework suggesting human cognitive functions are already migrating into digital AI systems through ambient intelligence rather than traditional mind uploading. The study identifies evidence in scheduling assistants, writing tools, and AI agents that cognitive externalization is occurring now through bidirectional adaptation and functional equivalence.
AINeutralarXiv – CS AI · Mar 177/10
🧠New research examines how humans assign causal responsibility when AI systems are involved in harmful outcomes, finding that people attribute greater blame to AI when it has moderate to high autonomy, but still judge humans as more causal than AI when roles are reversed. The study provides insights for developing liability frameworks as AI incidents become more frequent and severe.
AINeutralarXiv – CS AI · Mar 117/10
🧠Researchers introduce MiniAppBench, a new benchmark for evaluating Large Language Models' ability to generate interactive HTML applications rather than static text responses. The benchmark includes 500 real-world tasks and an agentic evaluation framework called MiniAppEval that uses browser automation for testing.
AINeutralarXiv – CS AI · Mar 117/10
🧠Researchers propose a new theoretical framework called the 'Third Entity' to describe the emergent cognitive formation that arises from human-AI interactions, introducing the concept of 'vibe-creation' as a pre-reflective cognitive mode. The paper argues this represents the automation of tacit knowledge with significant implications for epistemology, education, and how we understand human-AI collaboration.
AIBullisharXiv – CS AI · Mar 57/10
🧠Researchers introduce HumanLM, a novel AI training framework that creates user simulators by aligning psychological states rather than just imitating response patterns. The system achieved 16.3% improvement in alignment scores across six datasets with 26k users and 216k responses, demonstrating superior ability to simulate real human behavior.
AINeutralarXiv – CS AI · Jun 256/10
🧠Tinker Tales is a tangible dialogue system combining physical storytelling boards, NFC-embedded toys, and mobile apps to enable child-AI collaborative storytelling. A user study with 8-year-olds demonstrates that conversation design and prompt framing significantly influence how children engage in co-creative dialogue with AI agents, with educational scaffolding affecting narrative consistency and contribution patterns.
AINeutralarXiv – CS AI · Jun 236/10
🧠LK_Jam is a real-time human-AI music generation system that uses lightweight GRU neural networks and optimized C++ engineering to enable low-latency, bidirectional musical interaction between humans and AI performers. The system achieves O(1) complexity inference through lock-free architecture and sparse event streaming, addressing a significant technical challenge in live music applications.
AIBearisharXiv – CS AI · Jun 236/10
🧠Researchers built Trucey, an AI coaching system for workplace negotiations, but found that a static handbook outperformed the conversational AI on user empowerment and usability. The study reveals that conversational AI imposes linear execution models on tasks requiring recursive, non-sequential preparation, challenging core assumptions about AI-mediated coaching design.
AIBullisharXiv – CS AI · Jun 236/10
🧠Researchers introduce IDRBench, the first benchmark for evaluating interactive capabilities of deep research agents powered by Large Language Models. The benchmark measures how well agents can solicit user clarification during research tasks and quantifies the tradeoff between alignment improvements and interaction costs across seven LLMs.
AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers conducted a mixed-methods study examining optimal interaction patterns between humans and AI agents in business environments, identifying design principles that enhance user experience and build trust. The findings establish foundational criteria for measuring UX effectiveness with AI agents, providing development teams with user-centered insights to improve adoption rates and decision-making processes.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers introduce Relational Reflective Intelligence (RRI), a governance framework that adds auditable reasoning checkpoints between humans and large language models to address shared cognitive vulnerabilities. Rather than modifying models internally, RRI operates as an interaction layer that structures joint reasoning and surfaces conflicts, aiming to prevent 'relational drift' where human and AI errors compound.
