Machine Learning · 2026-11-06

Depth or Description? When a One-Step Latent Score Replaces Lookahead in Proactive Information Acquisition

arXiv:2611.07155v1 Announce Type: new Abstract: A proactive agent must decide what to ask before it acts. Exact lookahead values combinations of questions but is expensive and leans on a fitted model; a one-step score is cheap but can miss them. Our main finding: the need for depth often lies in how the hidden cause is described. A one-step score on the label can be blind to an interaction among answers.

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arXiv:2611.07155v1 Announce Type: new Abstract: A proactive agent must decide what to ask before it acts. Exact lookahead values combinations of questions but is expensive and leans on a fitted model; a one-step score is cheap but can miss them. Our main finding: the need for depth often lies in how the hidden cause is described. A one-step score on the label can be blind to an interaction among answers. A score on a suitable latent cause is not, and where that latent encodes the decision it replaces most of depth at a fraction of the search. Described differently, the same problem can defeat the score while exact values do not move, so keep the latent small and check it against exact values.

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Original source: arXiv ML ↗

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