LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation
Learns when to perform another internal reasoning step, recall stored evidence or intermediate states, or emit a token. Trains these choices by branching over alternative actions and measuring their effects on the next emitted token.
First public: · Added to this list: October 18, 2026
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arXiv:2609.39740 · Initial submission: 2026-09-30
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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.
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.
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