arXiv (All)
open access
Evaluating Compressed Memory: Dependence, Utility, and Content Benefit
Does a language model answer better because its compressed memory contains useful document information? Replacing memory vectors with zeros can lower a trained model's score without showing that it outperforms a model trained without memory. We combine zeroing with separately trained models that can
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
- A RE-UNIFICATION OF TWO COMPETING MODELS FOR DOCUMENT RETRIEVAL#933478
- REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving#998757
- OVAL: Output-Aware Local Page Bases for KV Cache Retrieval#1215777
- OVAL: Output-Aware Local Page Bases for KV Cache Retrieval#1194443
- Invisible Retention Rate (IRR): Measuring the Preservation of Non-Legible Information#105590
Record · ID 1345196
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