Galahad turns LLM document reading into a one-time cost with byte-exact KV memory: 100 of 100 on a 97,000-token corpus at 0.59–0.64 s per question
Sietse Schelpe of Corbenic AI presents Galahad, a memory layer in which Taliesin saves and reloads byte-exact KV state and Blaise passes the model only the section a question needs; on a recall test with 100 facts hidden in a 97,000-token corpus (Gemma 4 31B), Taliesin alone answered 98 of 100 on llama.cpp at 3.0 s and 572 J per question against 10 of 100, 9.3 s and 2,754 J without Galahad, and with Blaise added the model answered 100 of 100 on all three runtimes at 0.59–0.64 s and 200–213 J, while a tuned RAGFlow pipeline answered 77.