arXiv (All)
open access
Real Long-Term Memory for AI: A 50-Million-Token Window That Is Faster and Cheaper Than Recompute
A large language model can only use the text that fits in its context window, and it recomputes its internal key-value (KV) state for a prompt every time the prompt is sent. We test a memory layer, the public package galahad-kv, that saves the KV state of each block of about 16,000 tokens to encrypt
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
- Real Long-Term Memory for AI: A 50-Million-Token Window That Is Faster and Cheaper Than Recompute#1257939
- Retrieval Capacity of Self-Attention Under Competition#1142186
- Thinking in Tokens, Talking in Bits: A Practical Interface for Token Communication#1109295
- ✶ Token Pressure, Semantic Debt, and Machine-Facing Registers: Governing AI Language Economics#696043
- MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents#1061792
Record · ID 1247772
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