arXiv Machine Learning October 16, 2026 By David Reber, James Reber, Ari Holtzman, Victor Veitch

Can LLMs Anticipate Their Cruxes?

Read the original on arXiv Machine Learning →

The paper investigates how to extract the key decision points—termed "cruxes"—that influence a language model’s recommendation. Three methods are compared: asking the model to predict its cruxes beforehand, extracting them from a single generated response, and aggregating cruxes across many responses using a reranker. Experiments show that only the many‑rollout approach reliably identifies the full set of important cruxes, making it essential for high‑stakes decisions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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