·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation55m◆Counterfactual Model Testing Under Distribution Shift55m◆Centralized Critics for N-Agent Ad Hoc Teamwork55m◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning55m◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction55m◆Numina Fuse: Interactive Autoformalization in Lean 455m◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy55m◆Video World Models Need Predictable Latents55m◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator55m◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models55m◆Latent Goal Prediction from Language for Model-Based Planning55m◆Rectified Language Flow55m◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment55m◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila55m◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights55m◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training55m◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks55m◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training55m◆Unbiased Alignment for Large Language Models with Noisy Preferences55m◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration55m◆MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation55m◆Counterfactual Model Testing Under Distribution Shift55m◆Centralized Critics for N-Agent Ad Hoc Teamwork55m◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning55m◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction55m◆Numina Fuse: Interactive Autoformalization in Lean 455m◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy55m◆Video World Models Need Predictable Latents55m◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator55m◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models55m◆Latent Goal Prediction from Language for Model-Based Planning55m◆Rectified Language Flow55m◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment55m◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila55m◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights55m◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training55m◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks55m◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training55m◆Unbiased Alignment for Large Language Models with Noisy Preferences55m◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration55m◆
News/A Mixture-Gated Theory of Anisotropic Representation Dynamics in Transformers
arxiv
PublishedOctober 27, 2026 at 4:00 AM

A Mixture-Gated Theory of Anisotropic Representation Dynamics in Transformers

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

arXiv:2610.23242v2 Announce Type: replace Abstract: Small differences between internal representations can shrink or grow as they pass through a Transformer layer, affecting how information is preserved and how sensitive the model is to perturbations. Yet it remains unclear when changes in attention

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
arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation55marxivCounterfactual Model Testing Under Distribution Shift55marxivCentralized Critics for N-Agent Ad Hoc Teamwork55marxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning55m
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