·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’22m◆Kimi: Threat or menace?2h◆The apps, gadgets, and tools every reader needs9h◆Neil Rimer thinks the AI money is coming back out16h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach17h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study17h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h◆The Steering Budget: Examples beat Knobs17h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs17h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination17h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization17h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models17h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation17h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System17h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets17h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility17h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation17h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks17h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios17h◆Dave Eggers told OpenAI staff that ChatGPT was ‘silencing an entire generation’22m◆Kimi: Threat or menace?2h◆The apps, gadgets, and tools every reader needs9h◆Neil Rimer thinks the AI money is coming back out16h◆Democratizing Agent Deployment Safety: A Structural Monitoring Approach17h◆Harnessing LLMs for Reliable Academic Supervision: A Comparative Study17h◆Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17h◆TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h◆The Steering Budget: Examples beat Knobs17h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs17h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination17h◆HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization17h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models17h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation17h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System17h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets17h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility17h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation17h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks17h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios17h◆
News/Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability
arxiv
PublishedJuly 3, 2026 at 4:00 AM

Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability

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

arXiv:2607.01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\in\mathbb{R}^{m\times n}$ with $m<n$, and inferring a sparse code $\mat

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
arxivDemocratizing Agent Deployment Safety: A Structural Monitoring Approach17harxivHarnessing LLMs for Reliable Academic Supervision: A Comparative Study17harxivAccelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap17harxivTIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation17h
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