arXiv (All)
open access
RACE: RoPE-Aligned Attention Compression Based on Query-Key Activation Energies
Head-dimension compression can reduce the memory required by the key-value (KV) cache, but existing methods often reconstruct full-dimensional representations during inference, adding significant decoding FLOPs overhead. We formalize the design considerations for converting pretrained attention into
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
- Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling#124007
- Value-Modulated Attentional Capture Arises from Direct Pavlovian Conditioning Unless Information Seeking is Established via Awareness#1179162
- Architecture and Operation of AI Systems and Future Expansion to Induce Artificial Consciousness in AI#689695
- If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data#120444
- Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation#485565
Record · ID 1412351
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