·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Deezer launches an AI music detector for other streaming services1h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing5h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5h◆The Impossibility of Eliciting Latent Knowledge5h◆Mapping Scientific Literature with Large Language Models and Topic Modeling5h◆Grounding Computer Use Agents on Human Demonstrations5h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models5h◆LSTM based IoT Device Identification5h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse5h◆Composing Linear Layers from Irreducibles5h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM5h◆BioMamba: Domain-Adaptive Biomedical Language Models5h◆Intermittent time series forecasting: local vs global models5h◆From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning5h◆Characterizing Software Aging in GPU-Based LLM Serving Systems5h◆Geometric Metrics and LLMs: What They Measure and When They Work5h◆Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions5h◆Augmenting Molecular Language Models with Local $n$-gram Memory5h◆Deezer launches an AI music detector for other streaming services1h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing5h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5h◆The Impossibility of Eliciting Latent Knowledge5h◆Mapping Scientific Literature with Large Language Models and Topic Modeling5h◆Grounding Computer Use Agents on Human Demonstrations5h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models5h◆LSTM based IoT Device Identification5h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse5h◆Composing Linear Layers from Irreducibles5h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM5h◆BioMamba: Domain-Adaptive Biomedical Language Models5h◆Intermittent time series forecasting: local vs global models5h◆From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning5h◆Characterizing Software Aging in GPU-Based LLM Serving Systems5h◆Geometric Metrics and LLMs: What They Measure and When They Work5h◆Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions5h◆Augmenting Molecular Language Models with Local $n$-gram Memory5h◆
News/From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning
arxiv
PublishedJune 11, 2026 at 4:00 AM

From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning

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

arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption while bypassing the slow, reflective processes that underpin sound judgment. This paper introduces Relat

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
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning5harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!5harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions5harxivThe Impossibility of Eliciting Latent Knowledge5h
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