·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery5h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?5h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants5h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning5h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing5h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting5h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science5h◆Scoped Semantic Invariance as a Foundation for LLM Reliability5h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning5h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation5h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems5h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings5h◆The Price of Scale in Proactive Memory Recall5h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents5h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents5h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI5h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs5h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding5h◆The Value of Memory in Language Agents5h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention5h◆Optimal Correction Sets for Provable Redress of Contestable Causal Discovery5h◆AOS-Bench: Can LLM Agents Manage Dynamic Workloads?5h◆InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants5h◆Propensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning5h◆Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing5h◆NewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting5h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science5h◆Scoped Semantic Invariance as a Foundation for LLM Reliability5h◆ThinkBit: Search Codewords, Recalibrate Scales, Select by Reasoning5h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation5h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems5h◆Multimodal Requirements Traceability Link Recovery in Industrial Settings5h◆The Price of Scale in Proactive Memory Recall5h◆AgentProofBench: Benchmarking Reusable Verifiers for Web Agents5h◆GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents5h◆The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI5h◆Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs5h◆XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding5h◆The Value of Memory in Language Agents5h◆HyScale: Scaling Long-Context Training for Learned Sparse Attention5h◆
News/SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders
arxiv
PublishedOctober 9, 2026 at 4:00 AM

SAGE: Sink-Aware Guided Emphasis for Visual Grounding in Vision-Language Decoders

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

arXiv:2610.11469v1 Announce Type: cross Abstract: Recent large vision-language models (VLMs) pair a visual encoder with a large language model (LLM) and perform well on diverse image-text tasks, yet their reliability is often limited by decoder attention pathologies that suppress visual evidence and

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
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivOptimal Correction Sets for Provable Redress of Contestable Causal Discovery5harxivAOS-Bench: Can LLM Agents Manage Dynamic Workloads?5harxivInferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants5harxivPropensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning5h
The Bubble Brief
MON · WED · FRI

Read AI 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