← Back to all news
arXiv Computation and Language By Peter A. Jansen

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

Read the original on arXiv Computation and Language →

The paper introduces the Scientific Contribution Graph, a large-scale resource that extracts 6 million scientific contributions from 655 k open-access papers across multiple disciplines and links them with 36 million prerequisite edges. It frames automated technological roadmapping as the task of identifying contributions and their prerequisites, and presents a new scientific prerequisite prediction task where models forecast which existing technologies enable future discoveries. The authors report that current models achieve a 0.48 MAP score on temporally-filtered backtesting, indicating rapid progress in this area.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

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.

By Shubham Kale, Aniketh Garikaparthi, Manasi Patwardhan
llmsreinforcement-learningfine-tuning
More like this →