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ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Paper recorded by Signals 4 on 2026-10-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.AI · 人工智能 · first seen 2026-10-02

Abstract

What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Us

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#4 most recent of 500 cs.AI papers we have recorded · ↑ newer: Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied A · ↓ older: SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resol
Cite this page: ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research: the #4 most recent of 500 cs.AI papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/scholarcatalyst-a-benchmark-for-retrieving-papers-that-inspire-new-research.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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