Latent Thought Flows: Compress Text Before Generation
burny_tech · x · 2026-07-17
Introduces Latent Thought Flows:
- Compresses 256 text tokens into 8 continuous latents, generates them in the latent space using a one-step flow model, and then reads them back into text using an autoregressive decoder.
- The author's core thesis is that if "compression is intelligence," then discrete token sequences like text may not be the ultimate representation for superintelligence.
- In terms of results, they claim this method outperforms a fine-tuned autoregressive baseline in the compute vs. generation quality trade-off, demonstrating the advantages of "compression + one-step generation."
- The conclusion explicitly ties this work to the Bitter Lesson, suggesting that representation learning and compression may ultimately prevail.
Related event: Latent Thought Flows: Text Generation via Continuous Latent Space(2 posts)→
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