arXiv (All)
open access
SOLAR: SVD-Optimized Lifelong Attention for Recommendation
Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic cost in sequence length N makes long-context modeling expensive and often forces truncation or other heuristics. Linear attention reduces complexity to O(Nd^2) b
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond Pairwise Attention: Higher-Order Modular Attention for Efficient Sequence Learning#1076829
- Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies#1155917
- PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations#987996
- Proteus: Incremental Memory Activation for Long-Context Sequence Modeling#625393
- Proteus: Incremental Memory Activation for Long-Context Sequence Modeling#623120
Record · ID 1142634
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive