·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery4h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?4h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing4h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting4h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science4h◆Scoped Semantic Invariance as a Foundation for LLM Reliability4h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning4h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation4h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems4h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings4h◆The Price of Scale in Proactive Memory Recall4h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents4h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents4h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI4h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs4h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding4h◆The Value of Memory in Language Agents4h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention4h◆Optimal Correction Sets for Provable Redress of Contestable Causal Discovery4h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?4h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing4h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting4h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science4h◆Scoped Semantic Invariance as a Foundation for LLM Reliability4h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning4h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation4h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems4h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings4h◆The Price of Scale in Proactive Memory Recall4h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents4h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents4h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI4h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs4h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding4h◆The Value of Memory in Language Agents4h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention4h◆
News/Learning, Reasoning, Refinement: A Framework for Kahneman's Dual-System Intelligence in GUI Agents
arxiv
PublishedJuly 22, 2026 at 4:00 AM
—neutral

Learning, Reasoning, Refinement: A Framework for Kahneman's Dual-System Intelligence in GUI Agents

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

arXiv:2506.17913v2 Announce Type: replace Abstract: Graphical User Interface (GUI) agents have made significant progress in automating digital tasks through the utilization of computer vision and language models. Nevertheless, existing agent systems encounter notable limitations. Firstly, they predo

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    CogniGUI
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#gui-automation#cognitive-architectures#benchmark

No replies yet. Be first.

Mentioned models
01
  • 01
    CogniGUI
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#gui-automation#cognitive-architectures#benchmark

Related coverage

More from ARXIV
arxivOptimal Correction Sets for Provable Redress of Contestable Causal Discovery4harxivAOS-Bench: Can LLM Agents Manage Dynamic Workloads?4harxivInferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants4harxivPropensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning4h
The Bubble Brief
MON · WED · FRI

Read gui-automation insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews