arXiv CS
open access
LatComm: Reusable Context Compression for Latent Communication between Large Language Models
Systems that combine several large language models (LLMs) often use one context for many questions. When the context is passed between models as text, the receiving model processes all of its tokens again for every question. Avoiding this cost requires a context representation that is compact, reusa
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
- DP-MIND: Differentially private memory-guided Long context Inference#1297484
- Towards In-Parameter Memory Augmentation for Large Language Models#1218422
- $δ$-mem: Efficient Online Memory for Large Language Models#1129260
- Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models#1109649
- Continuous Latent Contexts Enable Efficient Online Learning in Transformers#1389272
Record · ID 1381271
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