·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Vision Hopfield Memory Networks13m◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations13m◆A Motivational Architecture for Conversational AGI13m◆Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers13m◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models13m◆Zero knowledge verification for frontier AI training is possible13m◆Brick-Composer: Using MLLMs for Assembly with Diverse Bricks13m◆Insurance of Agentic AI13m◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety13m◆PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage13m◆Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces13m◆Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation13m◆SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization13m◆When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty13m◆Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity13m◆GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection13m◆Multilingual Fine-Tuning via Localized Gradient Conflict Resolution13m◆Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack13m◆Evaluation of LLMs for Mathematical Formalization in Lean13m◆Answer Presence Drives RAG Rewriting Gains13m◆Vision Hopfield Memory Networks13m◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations13m◆A Motivational Architecture for Conversational AGI13m◆Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers13m◆Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models13m◆Zero knowledge verification for frontier AI training is possible13m◆Brick-Composer: Using MLLMs for Assembly with Diverse Bricks13m◆Insurance of Agentic AI13m◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety13m◆PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage13m◆Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces13m◆Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation13m◆SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization13m◆When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty13m◆Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity13m◆GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection13m◆Multilingual Fine-Tuning via Localized Gradient Conflict Resolution13m◆Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack13m◆Evaluation of LLMs for Mathematical Formalization in Lean13m◆Answer Presence Drives RAG Rewriting Gains13m◆
News/Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity
arxiv
PublishedJune 6, 2026 at 4:00 AM

Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.05532v1 Announce Type: new Abstract: Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations -- cognitive offloading and over-reliance -- identify symptoms but not mechanisms. We propose selective metacognitive ada

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivVision Hopfield Memory Networks13marxivStable Deep Reinforcement Learning via Isotropic Gaussian Representations13marxivA Motivational Architecture for Conversational AGI13marxivAssessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers13m
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews