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#superposition

2 curated events
papersTODAY 04:00 UTC

arXiv Paper Examines Capacity Limits of Reasoning via Superposition

A new arXiv preprint studies how much intermediate computation a single vector can carry when language models reason through continuous or recurrent methods rather than token-by-token chain-of-thought. The work frames multi-step reasoning as superposition, where partial computations are packed into hidden states, and analyzes the resulting capacity limits. It offers a theoretical lens on the trade-offs between explicit token-based reasoning and continuous latent approaches.

papersSEP 10 04:00 UTC

Study derives high-probability guarantees for reading out superposed features in neural networks

A preprint cross-listed on arXiv's AI and machine-learning feeds investigates how networks store more concepts than they have dimensions via superposition, and how interference between stored features restricts how many can be recovered through linear read-out. By casting this recovery problem as a compressed sensing task, the authors establish conditions under which multiple simultaneously active features can be decoded with high probability.