GIANTS: Generative Insight Anticipation from Scientific Literature

TL;DR

This paper introduces insight anticipation, a task where language models predict the core insights of future scientific papers from their references, supported by a new benchmark and a specialized model that outperforms baselines.

Contribution

The authors present GiantsBench, a large benchmark for insight prediction, and GIANTS-4B, a reinforcement learning-trained model that improves insight quality and generalizes across domains.

Findings

GIANTS-4B outperforms proprietary baselines with a 34% relative improvement in similarity scores.

Insights generated by GIANTS-4B are more conceptually clear according to human evaluation.

Third-party model SciJudge-30B predicts insights from GIANTS-4B are more likely to lead to higher citations.

Abstract

Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific discovery, their ability to perform this targeted, literature-grounded synthesis remains underexplored. We introduce insight anticipation, a generation task in which a model predicts a downstream paper's core insight from its foundational parent papers. To evaluate this capability, we develop GiantsBench, a benchmark of 17k examples across eight scientific domains, where each example consists of a set of parent papers paired with the core insight of a downstream paper. We evaluate models using an LM judge that scores similarity between generated and ground-truth insights, and show that these similarity scores correlate with expert human ratings. Finally, we present GIANTS-4B, an LM trained via reinforcement learning (RL) to optimize insight anticipation using these similarity scores as a proxy reward. Despite its smaller open-source architecture, GIANTS-4B outperforms proprietary baselines and generalizes to unseen domains, achieving a 34% relative improvement in similarity score over gemini-3-pro. Human evaluations further show that GIANTS-4B produces insights that are more conceptually clear than those of the base model. In addition, SciJudge-30B, a third-party model trained to compare research abstracts by likely citation impact, predicts that insights generated by GIANTS-4B are more likely to lead to higher citations, preferring them over the base model in 68% of pairwise comparisons. We release our code, benchmark, and model to support future research in automated scientific discovery.