arXiv (All)
open access
Component Type, Not Reconstruction Error, Predicts Attention Quantization Sensitivity
Many post-training quantization (PTQ) methods use layer-wise reconstruction, second-order proxy objectives, or activation-aware transformations to reduce quantization-induced error. Whether that error signal predicts the downstream functional impact of quantizing an individual attention projection h
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
- Frontalparietal networks involved in categorization and item working memory#710002
- High-Dimensional Learning Dynamics of Attention-Indexed Models#926735
- Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance#141715
- Attentional prioritization enhances the accessibility of neural representations during working memory maintenance#259217
- Counterexamples to Local Reconstruction Gain as a Proxy for Final Fidelity in Residual Completion#1119136
Record · ID 1062074
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