·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Figma announces new features to define how agents work on its platform3h◆CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing4h◆FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks4h◆Narrowing the Collaboration Gap, Probably4h◆Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine4h◆Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion4h◆LLMs program using Reusable and Composable Abstract Representations4h◆FinGREAT: Evaluating LLMs on Financial Graph-guided Retrieval And Typing4h◆Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents4h◆MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces4h◆New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents4h◆TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models4h◆What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization4h◆ProtModernBERT: Compute-Efficient Protein Language Modeling4h◆CAST: History-Adaptive Spectral Clipping for LLM Pretraining4h◆AVA-Encoder: Towards Agent-Native Video Representation Learning4h◆Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers4h◆Efficient contextual partial monitoring with general function approximation4h◆VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting4h◆When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models4h◆Figma announces new features to define how agents work on its platform3h◆CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing4h◆FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks4h◆Narrowing the Collaboration Gap, Probably4h◆Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine4h◆Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion4h◆LLMs program using Reusable and Composable Abstract Representations4h◆FinGREAT: Evaluating LLMs on Financial Graph-guided Retrieval And Typing4h◆Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents4h◆MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces4h◆New Model, Old Habits: Behavior Transfer under Model Swapping in Multi-Session LLM Agents4h◆TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models4h◆What Happens to the Goal? Distinct Failures of Policy Alignment in Goal Misgeneralization4h◆ProtModernBERT: Compute-Efficient Protein Language Modeling4h◆CAST: History-Adaptive Spectral Clipping for LLM Pretraining4h◆AVA-Encoder: Towards Agent-Native Video Representation Learning4h◆Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers4h◆Efficient contextual partial monitoring with general function approximation4h◆VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting4h◆When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models4h◆
News/Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS
arxiv
PublishedAugust 3, 2026 at 4:00 AM

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS

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

arXiv:2607.22657v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior. We introduce Level-based Evaluati

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
arxivNarrowing the Collaboration Gap, Probably4harxivAVA-Encoder: Towards Agent-Native Video Representation Learning4harxivNon-asymptotic Convergence of Average-reward Q-learning with Options4harxivDeep kernel hedging4h
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