SOURCE-LINKED INTELLIGENCE
How Many Thoughts Can a Vector Hold? The Capacity of Reasoning by Superposition
Large language models solve hard problems through intermediate computations across multi-step reasoning. Traditional chain-of-thought encodes these computations as tokens. Recent continuous and recurrent methods instead move partial computations into fixed-dimensional latent states, where a single thought can superpose multiple alternatives. This raises a fundamental design question:what should continuous thoughts preserve as reasoning proceeds? An intuitive approach discards past computations and keeps only the current reasoning frontier. Storing more items seems to dilute states and waste li
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T06:35:15.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.