Learning Individual Scientific Taste with Privileged Hindsight Distillation

Key points

• 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.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Learning Individual Scientific Taste with Privileged Hindsight Distillation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:44 AM

  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.

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