·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance7m◆Boosting Adversarial Robustness and Generalization with Dictionary Structure7m◆A Dominant Supplier Slows Recursive Drift More Than It Steers It7m◆COMiT: Learning Structured Visual Tokens through Sequential Communication7m◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation7m◆Data-Free Pruning of Self-Attention Layers in LLMs7m◆Screening Is Enough7m◆Data Unlearning via Inverse Distillation7m◆ReCIRC: Rectified Conformal Risk Control7m◆Replay-buffer engineering for noise-aware quantum circuit optimization7m◆SINO: Scale-Invariant Neural Operator7m◆Preferent Compression Bounds Are Tight7m◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions7m◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models7m◆Bregman Consensus7m◆In-Context Learning for Robots: Methods and Applications7m◆Modal Logic Neural Networks7m◆Volatility-Clustering Adaptation for Financial Time Series7m◆Reference-Guided Machine Unlearning7m◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training7m◆Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance7m◆Boosting Adversarial Robustness and Generalization with Dictionary Structure7m◆A Dominant Supplier Slows Recursive Drift More Than It Steers It7m◆COMiT: Learning Structured Visual Tokens through Sequential Communication7m◆Compress to Remember: Learning Compact Memory via On-Policy Distillation for Long Video Generation7m◆Data-Free Pruning of Self-Attention Layers in LLMs7m◆Screening Is Enough7m◆Data Unlearning via Inverse Distillation7m◆ReCIRC: Rectified Conformal Risk Control7m◆Replay-buffer engineering for noise-aware quantum circuit optimization7m◆SINO: Scale-Invariant Neural Operator7m◆Preferent Compression Bounds Are Tight7m◆Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions7m◆How to Tame a Multi-Headed Hydra? Adaptive Multi-Category Safety Steering for Large Language Models7m◆Bregman Consensus7m◆In-Context Learning for Robots: Methods and Applications7m◆Modal Logic Neural Networks7m◆Volatility-Clustering Adaptation for Financial Time Series7m◆Reference-Guided Machine Unlearning7m◆Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training7m◆
News/T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendation
arxiv
PublishedSeptember 29, 2026 at 4:00 AM
—neutral

T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendation

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

arXiv:2609.30576v1 Announce Type: new Abstract: Large-scale recommenders increasingly adopt the sequential generative recipe behind large language models, bringing the Transformer into recommendation along with design choices made for text, including Rotary Position Embedding (RoPE). In language mod

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 Nuisance7marxivBoosting Adversarial Robustness and Generalization with Dictionary Structure7marxivA Dominant Supplier Slows Recursive Drift More Than It Steers It7marxivCOMiT: Learning Structured Visual Tokens through Sequential Communication7m
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