CaLR: Causal Latent Revision for Robust Diffusion Reasoning
Uses an expert-derived causal dependency matrix to guide gradient-based revision of diffusion reasoning states. The aim is to preserve logical dependencies while revising several intermediate positions during parallel generation.
First public: · Added to this list: October 18, 2026
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ArXiv preprint
arXiv:2609.20981 · Initial submission: 2026-09-17
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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.
Alternative generation methods — Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.
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