arXiv AI
Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 15
Large Language Models Extend Known Research But Do Not Create New Research Directions
arXiv:2610.17439v1 Announce Type: new Abstract: The success of autonomous research systems relies on the quality of the scientific hypotheses they generate. Recent studies report that human reviewers...
arXiv Computation and Language
Sep 22
DeepInstructor: An Agentic AI Instructor for Experience-Driven Idea Evaluation
arXiv:2609.22104v1 Announce Type: new Abstract: As automated scientific discovery advances, Large Language Models (LLMs) can now generate research ideas at an unprecedented scale, shifting the bottle...
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 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 AI
Oct 7
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...