DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
Feeds both token embeddings and hidden states back through repeated transformer layers to connect successive steps of multi-hop reasoning.
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arXiv:2607.00341 · Initial submission: 2026-07-01
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.
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