Learning in a World of Notes

Engram reports that reinforcement learning on top of fixed, high-quality notes raised agent accuracy by 7 to 20 points across three workspaces. On Harvey C&H, a trained Qwen 27B model matched GPT-6 Sol with notes at 78% for $0.06 per query versus $0.17, and training cost roughly 5-30% of building the notes. A model trained once still beat the base model after 23M tokens of note edits. These are the company's own results; on Hex it did not reach frontier accuracy, and the robustness test covers one workspace.