Think in Latent, Explain in Language: Self-Explainable Latent Reasoning
Jointly trains latent states for answer prediction and for decoding back into readable reasoning, using one model rather than a separate post-hoc explanation decoder.
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arXiv:2608.13570 · Initial submission: 2026-07-01
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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.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.
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