LatentPress: compress long chats/docs into continuous memory tokens for frozen LLMs

LatentPress stores conversational histories and long documents as continuous memory tokens a frozen decoder reads through its input-embedding interface—no text reconstruction at inference. A small reader-matched writer (4.2M–26.2M adapter params, ~0.1% of the decoder) compresses 4–16×. On LongMemEval it reaches 0.504 accuracy at 7.70× compression vs 0.490 uncompressed, beating text summaries and OCR; writing is ~43ms per conversation and reading is 5–9× faster than raw context—useful for long-context agent memory.