CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression
Compresses images of written derivations into continuous states, training both the answers they support and the reasoning that can be recovered from them. Gradually shifts from generating latent blocks in parallel to generating them in sequence.
First public: · Added to this list: October 18, 2026
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arXiv:2610.07175 · Initial submission: 2026-10-05
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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.
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