arXiv (All)
open access
FoldAttention: Declared-Reference Softmax for Fast Decode and Deterministic Backward
Autoregressive decode repeatedly streams a growing KV cache, making attention a major cost at long context. Existing high-performance kernels use online softmax, which discovers a row's normalization reference as it scans keys. Earlier contributions therefore remain provisional and may require resca
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
- FoldAttention: Declared-Reference Softmax for Fast Decode and Deterministic Backward#1108728
- The Credit Realization Law: Exact Memory Limits and Minimal Trace Architectures for Online Recurrent Learning#478437
- Quasi Linear Kernel Attention with Infinite Capacity#1129941
- Official Review · NEURIPS 2024#787162
- RACORN-1: Adaptive Recall-Preserving Speedup for Low-Selectivity Filtered Vector Search#329175
Record · ID 1118122
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