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...
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.
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.
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:2606. 09105v1 Announce Type: new Abstract: Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.
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.
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 leav...
arXiv:2607. 01233v1 Announce Type: cross Abstract: LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference.
Highlights provide a concise summary of the main contributions of an academic paper and help readers quickly understand its focus. However, many journals do not provide highlights, which limits their use in literature retrieval, text mining, and bibliometric analysis.
ScholarCatalyst is a new benchmark that evaluates how well AI systems can retrieve research papers that inspire new work. The dataset was created by having 184 lead authors of 207 recent computer science papers annotate which earlier papers helped their projects, providing detailed rationales. The benchmark tests retrieval from the literature available at the start of a project, revealing that current agentic search and even advanced models like Claude Fable 5.1 perform only modestly better than simple embedding retrieval.
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.
LitPivot is a tool that supports researchers in developing well-situated research ideas by dynamically linking literature exploration with idea refinement. It introduces literature-initiated pivots, where engaging with relevant papers prompts revisions to an idea, and vice versa, updating the set of pertinent literature. In a lab study with 17 participants, users produced higher-rated ideas and reported a stronger understanding of the literature space, while an open-ended study with five participants illustrated how LitPivot facilitates iterative idea evolution.