Very interesting, but on my initial skim through, I don’t understand how this technique is lossless? Reproduced from the methods section: 1. Select a sequence

Very interesting, but on my initial skim through, I don’t understand how this technique is lossless? Reproduced from the methods section: 1. Select a sequence of tc tokens that the model has already generated or completed predicting as the reading area. 2. Insert t '<m>' tokens at once after the reading area to serve as the memory area. 3. The model performs a single inference on the memory area, but discards the model's output, retaining only the KV pairs from each layer. 4. Discard the reading area, and the model continues generating text from after the memory area. Isn’t the memory area a lossy compression of the reading area?