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Training Reasoning Models to Generate Diverse Scientific Ideas

Paper recorded by Signals 4 on 2026-10-15 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.CL · 自然语言处理 · first seen 2026-10-16

Abstract

One of the first steps of a research pipeline is generating ideas. Large Language Models (LLMs) are increasingly used to automate this step, but most of this work optimizes the quality of individual ideas, while overlooking diversity. In our work, we study document-grounded ideation, focusing on increasing the diversity of LLM generated ideas and ensuring they are high quality. Given prior papers,

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#231 most recent of 660 cs.CL papers we have recorded · ↑ newer: OmniVerifier-C1: Decomposed Graded Verifier for Rule-Governed Generati · ↓ older: MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Lan
Cite this page: Training Reasoning Models to Generate Diverse Scientific Ideas: the #231 most recent of 660 cs.CL papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/training-reasoning-models-to-generate-diverse-scientific-ideas.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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