arXiv — cs.AI preprintsInternational12 October 2026
UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2605.09040v4 Announce Type: replace Abstract: Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic compression, we propose UxSID, a framework exploring a third path: semantic-group shared interest memory. By utilizing Semantic IDs (SIDs) and a dual-level attention strategy, UxSID captures target-aware preferences without the heavy cost of item-specific models. This end-to-end architecture balances computational parsimony with semantic awareness, achieving state-
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance12 October 2026
- EliteTable: Self-Improving Program Search for Executable Control Policies12 October 2026
- Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study12 October 2026
- Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning12 October 2026
- SpireBench: Evaluating Long-Horizon Strategic Play of Agentic Models with Slay the Spire12 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.