·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Google must let publishers opt out of AI Search features, rules UK30m◆FederatedSkill: Federated Learning for Agentic Skill Evolution5h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge5h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation5h◆Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate5h◆Evaluating the Reversal Curse in Model Editing5h◆Fast Unlearning at Scale via Margin Self-Correction5h◆Can Local Learning Match Self-Supervised Backpropagation?5h◆CAPER: Clause-Aligned Process Supervision for Text-to-SQL5h◆Core-based Hierarchies for Efficient GraphRAG5h◆Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation5h◆An Asymptotic Theory of Chain-of-Thought in In-Context Learning5h◆Set-Preserving Calibration from Conformal P-Values to E-Values5h◆Localized, High-resolution Geographic Representations with Slepian Functions5h◆DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration5h◆RADE: Random Add-Drop Edge as a Regularizer5h◆Learning DNF through Generalized Fourier Representations5h◆Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction5h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems5h◆Closed-Loop Molecular Design with Calibrated Deference5h◆Google must let publishers opt out of AI Search features, rules UK30m◆FederatedSkill: Federated Learning for Agentic Skill Evolution5h◆Toward a Modular Architecture for Embedded AI Agent Systems at the Edge5h◆A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation5h◆Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate5h◆Evaluating the Reversal Curse in Model Editing5h◆Fast Unlearning at Scale via Margin Self-Correction5h◆Can Local Learning Match Self-Supervised Backpropagation?5h◆CAPER: Clause-Aligned Process Supervision for Text-to-SQL5h◆Core-based Hierarchies for Efficient GraphRAG5h◆Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation5h◆An Asymptotic Theory of Chain-of-Thought in In-Context Learning5h◆Set-Preserving Calibration from Conformal P-Values to E-Values5h◆Localized, High-resolution Geographic Representations with Slepian Functions5h◆DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration5h◆RADE: Random Add-Drop Edge as a Regularizer5h◆Learning DNF through Generalized Fourier Representations5h◆Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction5h◆Don't Gamble, GAMBLe: An Analytical Framework for AI-Driven Research Systems5h◆Closed-Loop Molecular Design with Calibrated Deference5h◆
News/Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
arxiv
PublishedMay 16, 2026 at 4:00 AM
—neutral

Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict

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

arXiv:2605.14473v1 Announce Type: cross Abstract: The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates the final answer even when it conflicts with the model's parametric knowledge. Accuracy alone does not reveal how retrieved context causally

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
arxivFederatedSkill: Federated Learning for Agentic Skill Evolution5harxivToward a Modular Architecture for Embedded AI Agent Systems at the Edge5harxivA Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation5harxivAnomalies in Multivariate Time Series Benchmarks Are Mostly Univariate5h
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