arXiv：cs.LG· Hanwei Fan, Yanqiao Wang, Yiqi Zhu, Xuanyu Lei, Peng Li, Yang Liu·· 3 小时前
用特权事后蒸馏学习个体科学品味
Learning Individual Scientific Taste with Privileged Hindsight Distillation
AI 导读
研究者提出 Privileged Hindsight Distillation（PHD），将科学品味建模为从共享评估空间中的准则级评估到个人相对偏好的映射，训练时利用后续出现的科学证据改进评估，推理时仅使用提案可见信息。在量子研究中，每位研究者仅需 15 次成对比较即可捕捉到差异明显的品味，包括对同一研究方向对的相反偏好。交互研究显示，个性化排序还能浮现出后续用户参与度更高的提案。
正文
Abstract:Scientific taste shapes which research directions researchers pursue, and it varies across individuals. Existing work evaluates research directions along multiple evaluation criteria or models scientific taste using community feedback signals such as citations and reviews, but neither directly captures how individuals translate assessments under the same criteria into different preferences. We model scientific taste as a researcher-specific mapping from criterion-level assessments in a shared, evidence-grounded evaluation space with proposal-visible evidence, to relative preferences. A population-level mapping learned from retrospective supervision provides a cold start when individual feedback is scarce. We introduce Privileged Hindsight Distillation (PHD), which uses later-emerging scientific evidence during training to improve proposal-time criterion assessment and the population mapping, while exposing only proposal-visible information at inference. We evaluate our approach primarily in quantum research, where structured public reviews are largely unavailable, and use pairwise proposal comparison as a direct interface for observing individual taste. With only 15 comparisons per researcher, our method captures sharply different tastes, including opposing preferences over the same research-direction pairs when they trade off across evaluation criteria. In an interactive study, personalized rankings also surface proposals associated with greater subsequent user engagement, connecting learned taste to downstream research exploration.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19719 [cs.AI] |
| (or arXiv:2610.19719v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19719 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hanwei Fan [view email]
[v1]
Wed, 14 Oct 2026 07:44:35 UTC (3,750 KB)
来源：arXiv：cs.LG · arxiv.org