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IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
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arXiv AI
6d ago
IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
arXiv:2610.08781v1 Announce Type: cross Abstract: Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training...
arXiv AI
Sep 4
LDC: Learning to Generate Research Idea with Dynamic Control
The paper introduces LDC, a framework that learns to generate research ideas with dynamic control. It combines supervised fine‑tuning on paper‑idea pairs with controllable reinforcement learning that optimizes novelty, feasibility, and effectiveness. During inference, sentence‑level controllers steer the generation process to balance these dimensions.
arXiv AI
Sep 25
Learning to Ideate for Scientific Impact
The paper "Learning to Ideate for Scientific Impact" explores using delayed signals of scientific uptake—specifically citation-normalized impact—as feedback to steer large language models toward generating high‑impact research ideas. The authors build a dataset of over 100,000 computer science papers, train a reward model to predict citation impact from goal‑idea pairs, and align an idea generator via supervised fine‑tuning and reinforcement learning. Evaluation with a reference‑grounded protocol shows that the RL‑tuned model consistently produces ideas with higher estimated impact than baseline models.
arXiv Computation and Language
Aug 28
RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature
RATIO (Retrieval Across Typed Ideation Operations) is a large-scale benchmark designed to evaluate how well retrieval systems can support scientific inspiration. It defines relevance through three ideation moves—Address, Broaden, and Specify—each targeting different levels of abstraction in literature retrieval. The benchmark is built from millions of full-text CS papers using a novel discourse-marker distant supervision method, and includes extensive LLM and human vetting to ensure quality.
arXiv AI
Jun 9
Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
arXiv:2606. 09105v1 Announce Type: new Abstract: Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.
arXiv Machine Learning
Aug 27
Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training
arXiv:2608. 25826v1 Announce Type: cross Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leaves the structure of whole documents untouched.