·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response8h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making8h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents8h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD8h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value8h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models8h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations8h◆When Synthetic Speech Is All You Have: Better Call GRPO8h◆Reducing belief in conspiracy theories as they unfold using large language models8h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors8h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization8h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning8h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding8h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment8h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions8h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents8h◆Secure Agentic Coding through Counterexample-Grounded Feedback8h◆Policy Search in Code Space for Multi-Agent Pathfinding8h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling8h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories1h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response8h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making8h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents8h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD8h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value8h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models8h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations8h◆When Synthetic Speech Is All You Have: Better Call GRPO8h◆Reducing belief in conspiracy theories as they unfold using large language models8h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors8h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization8h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning8h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding8h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment8h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions8h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents8h◆Secure Agentic Coding through Counterexample-Grounded Feedback8h◆Policy Search in Code Space for Multi-Agent Pathfinding8h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling8h◆
News/Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

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

arXiv:2606.12138v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training runs. We study this question through \emph{feature stability}: for each S

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 Response8harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making8harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents8harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD8h
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