·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI promises productivity, but for managers, it’s mostly a headache for now3m◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability1h◆OpenAI models are coming to Google Cloud2h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories3h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making10h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value10h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models10h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations10h◆When Synthetic Speech Is All You Have: Better Call GRPO10h◆Reducing belief in conspiracy theories as they unfold using large language models10h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors10h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization10h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning10h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding10h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment10h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions10h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents10h◆AI promises productivity, but for managers, it’s mostly a headache for now3m◆NVIDIA Kumo Forecast Combines Pretrained Time-Series Representations with Historical Pattern Retrieval and Interpretability1h◆OpenAI models are coming to Google Cloud2h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories3h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making10h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value10h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models10h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations10h◆When Synthetic Speech Is All You Have: Better Call GRPO10h◆Reducing belief in conspiracy theories as they unfold using large language models10h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors10h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization10h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning10h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding10h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment10h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions10h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents10h◆
News/The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering
arxiv
PublishedOctober 21, 2026 at 4:00 AM

The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering

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

arXiv:2610.26951v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has structural blind spots: it cannot anticipate which passages a question requires and d

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
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response10harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making10harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents10harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD10h
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