SciPaths: Forecasting Pathways to Scientific Discovery

2026-05-14 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Scientific Discovery & Forecasting · Depth: Expert, quick

Summary

SciPaths is a new benchmark dataset designed to evaluate AI models on their ability to forecast scientific discovery pathways. It addresses a gap in existing AI4Science benchmarks, which typically focus on citation prediction or idea generation, by instead focusing on identifying enabling contributions and grounding them in prior literature. The benchmark comprises 262 expert-annotated "gold" pathways and 2,444 "silver" pathways derived from machine learning and natural language processing papers. Each pathway details enabling contributions, their roles, rationales, and links to prior work or unmapped decisions. Initial evaluations with frontier and open-weight language models show that the best model achieves only 0.189 F1 for strict semantic matching, with methodological dependencies proving particularly challenging to recover. The study highlights that the quality of decomposition into enabling contributions is a significant bottleneck for end-to-end pathway recovery.

Key takeaway

For AI scientists and NLP engineers developing models for scientific forecasting, this research indicates that current language models struggle significantly with identifying and grounding enabling contributions in scientific discovery pathways. Your efforts should prioritize improving the decomposition of target contributions into their fundamental building blocks and enhancing the recovery of core methodological dependencies to advance scientific forecasting capabilities.

Key insights

Forecasting scientific discovery pathways requires identifying enabling contributions and grounding them in prior work.

Principles

• Scientific progress depends on sequential enabling contributions.
• Decomposition quality is critical for pathway recovery.

Method

The SciPaths benchmark involves identifying enabling contributions for a target scientific contribution and grounding each in prior literature available at a specified time.

In practice

• Focus on improving decomposition for pathway forecasting.
• Develop models better at recovering methodological dependencies.

Topics

• SciPaths
• Discovery Pathway Forecasting
• AI4Science Benchmarks
• Enabling Contributions
• Prior-Work Grounding

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.