arXiv cs.AI· Biswa Sengupta·· 12 小时前AI 评分27
Depth or Description？一步潜在评分何时能替代前瞻搜索
Depth or Description? When a One-Step Latent Score Replaces Lookahead in Proactive Information Acquisition
AI 导读
研究发现，主动信息获取中对隐藏原因的描述方式决定了一步评分能否替代精确前瞻：基于标签的一步评分可能对答案间的交互视而不见，而基于合适潜在原因的评分在潜在变量编码决策时可替代大部分搜索深度，仅需一小部分搜索成本。从一个语言模型答案中学到的概率无法迁移到另一个模型，除非规划同时考虑两者；从 token 读取的概率仅值几个标签。
正文 · 原文
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. Probabilities learned from one language model's answers do not carry to another unless planning allows for both; those read from tokens are worth only a few labels.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07155 [cs.LG] |
| (or arXiv:2611.07155v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07155 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Biswa Sengupta [view email]
[v1]
Wed, 4 Nov 2026 21:27:35 UTC (327 KB)
来源：arXiv cs.AI · arxiv.org