LatentPress: compressing context into continuous memory tokens a frozen LLM reads directly
A two-author paper introduces a third context representation beyond text and screenshots: continuous memory tokens written into a frozen decoder through its input-embedding interface, with no text reconstruction at inference. A small reader-matched writer (4.2M-26.2M adapter parameters, about 0.1% of the decoder) compresses 4-16x; on LongMemEval it scores 0.504 at 7.7x compression versus 0.490 for uncompressed evidence, far above text summaries (0.184) and OCR-based compression.
Why it matters
Over 100 upvotes on HF Daily Papers (110) — shows latent token memory can beat textual summarization for long-context recall at near-zero training cost.
Importance: 3/5
HF Daily Papers >=100 upvotes (110)