Real Long-Term Memory for AI: A 50-Million-Token Window That Is Faster and Cheaper Than Recompute

arXiv:2610.10845 · cs.CL, cs.AI, cs.DC, cs.LG · Submitted 2026-10-07 · Read on arXiv

cs.CL, cs.AI, cs.DC, cs.LG

Submitted: 2026-10-07

Updated: 2026-10-07

Terminology

• Transformer — 7× in this paper · explained in 2 episodes
• KV cache — 4× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Agent Memory — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes

Sources

• Attention Is All You Need
• Efficiently Scaling Transformer Inference
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• Prompt Cache: Modular Attention Reuse for Low-Latency Inference
• LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
• CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion
• CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving
• Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention
• InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management
• Do Large Language Models Need a Content Delivery Network?
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
• MemGPT: Towards LLMs as Operating Systems
• Gemma 4 Technical Report
• Gemma 3 Technical Report
• Working Around the Compute Ceiling: Byte-Exact Memory in Galahad Makes LLM Reading a One-Time Cost LLM Reading a One-Time Cost
• A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever
• Smarter and Cheaper at Once: Byte-Exact KV-Cache Grafting Turns a Frozen Small Model into a Verified-Knowledge Flywheel
• Lost in the Middle: How Language Models Use Long Contexts
• RULER: What's the Real Context Size of Your Long-Context Language Models?

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