When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap
Tests whether changing continuous reasoning states changes the model's later text output. Finds weaker steering effects than in explicit chain-of-thought, despite comparable changes to the hidden representations.
First public: · Added to this list: October 18, 2026
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arXiv:2609.21662 · Initial submission: 2026-09-18
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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.
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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