GIANTS: Generative Insight Anticipation from Scientific Literature

2026-04-10

AI Summary

This work proposes the "Insight Anticipation" task, which challenges language models to predict and generate core scientific insights from seminal papers, thereby enabling literature-driven synthesis of scientific knowledge. To facilitate this, the authors introduce GiantsBench, a multidisciplinary benchmark comprising 17k samples, and present GIANTS-4B—the first open-architecture language model trained with reinforcement learning and systematically evaluated for its capacity to generate scientifically meaningful insights. Experimental results demonstrate that GIANTS-4B achieves a 34% improvement over Gemini-3-Pro in similarity-based scores, with human evaluators rating its outputs as clearer and more coherent. Furthermore, SciJudge-30B, an accompanying impact prediction model, assesses GIANTS-4B’s generated insights as having higher citation potential in 68% of cases.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• insight anticipation
• scientific discovery
• literature synthesis
• core insight generation
• scientific literature

Innovation

• insight anticipation
• scientific discovery
• reinforcement learning
• language models
• benchmarking