·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance9m◆Boosting Adversarial Robustness and Generalization with Dictionary Structure9m◆A Dominant Supplier Slows Recursive Drift More Than It Steers It9m◆COMiT: Learning Structured Visual Tokens through Sequential Communication9m◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation9m◆Data-Free Pruning of Self-Attention Layers in LLMs9m◆Screening Is Enough9m◆Data Unlearning via Inverse Distillation9m◆ReCIRC: Rectified Conformal Risk Control9m◆Replay-buffer engineering for noise-aware quantum circuit optimization9m◆SINO: Scale-Invariant Neural Operator9m◆Preferent Compression Bounds Are Tight9m◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions9m◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models9m◆Bregman Consensus9m◆In-Context Learning for Robots: Methods and Applications9m◆Modal Logic Neural Networks9m◆Volatility-Clustering Adaptation for Financial Time Series9m◆Reference-Guided Machine Unlearning9m◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training9m◆Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance9m◆Boosting Adversarial Robustness and Generalization with Dictionary Structure9m◆A Dominant Supplier Slows Recursive Drift More Than It Steers It9m◆COMiT: Learning Structured Visual Tokens through Sequential Communication9m◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation9m◆Data-Free Pruning of Self-Attention Layers in LLMs9m◆Screening Is Enough9m◆Data Unlearning via Inverse Distillation9m◆ReCIRC: Rectified Conformal Risk Control9m◆Replay-buffer engineering for noise-aware quantum circuit optimization9m◆SINO: Scale-Invariant Neural Operator9m◆Preferent Compression Bounds Are Tight9m◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions9m◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models9m◆Bregman Consensus9m◆In-Context Learning for Robots: Methods and Applications9m◆Modal Logic Neural Networks9m◆Volatility-Clustering Adaptation for Financial Time Series9m◆Reference-Guided Machine Unlearning9m◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training9m◆
News/Screening Is Enough
arxiv
PublishedOctober 1, 2026 at 4:00 AM
—neutral

Screening Is Enough

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

arXiv:2604.01178v4 Announce Type: replace Abstract: We call query--key relevance absolute when its values lie on a fixed bounded scale, depend on neither competing keys nor sequence length, require no sequence-length-dependent calibration, and can all be zero. To realize this notion, we introduce sc

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
arxivPredictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance9marxivBoosting Adversarial Robustness and Generalization with Dictionary Structure9marxivA Dominant Supplier Slows Recursive Drift More Than It Steers It9marxivCOMiT: Learning Structured Visual Tokens through Sequential Communication9m
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 ↗
Built by Marouane Gazouzi
HomeModelsNews