SciPaths: Forecasting Pathways to Scientific Discovery

2026-05-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Expert, extended

Summary

SciPaths introduces a new benchmark for "discovery pathway forecasting" in scientific progress, moving beyond traditional citation prediction to identify the specific enabling contributions required for a target scientific achievement. The benchmark comprises 262 expert-annotated "gold pathways" and 2,444 machine-generated "silver pathways" from machine learning and natural language processing papers. Each pathway details enabling contributions, their functional roles, rationales, and prior-work groundings or unmapped decisions. Evaluations with frontier and open-weight language models show that the best model achieves only 0.189 F1 under strict semantic matching, with core methodological dependencies proving particularly challenging to recover. The study highlights that decomposition quality is a significant bottleneck for end-to-end pathway recovery, as prior-work grounding improves substantially when gold enabling contributions are provided. SciPaths aims to shift AI4Science evaluation towards reasoning backward from a target contribution to its foundational scientific building blocks.

Key takeaway

For AI Scientists and Research Scientists developing AI4Science agents, this work indicates that current models struggle with the nuanced task of identifying prerequisite scientific contributions. You should prioritize developing systems that can accurately decompose target contributions into their necessary functional components and effectively ground these in prior work, rather than solely focusing on citation prediction or idea generation. Improving decomposition quality is critical for enhancing end-to-end pathway recovery and making AI agents more effective at scientific forecasting.

Key insights

Forecasting scientific discovery requires identifying specific enabling contributions and their prior-work groundings, a task current LLMs struggle with.

Principles

• Scientific progress builds on sequences of enabling contributions.
• Contribution-level analysis is superior to paper-level citation proxies.
• Necessity is the core criterion for identifying enabling contributions.

Method

SciPaths formalizes "discovery pathway forecasting" by identifying necessary enabling contributions for a target, then grounding them in prior work or marking as unmapped. This involves expert annotation of functional components and their roles.

In practice

• Focus on functional dependencies, not just citations.
• Decompose complex contributions into atomic components.
• Evaluate models on contribution-level correctness.

Topics

• Discovery Pathway Forecasting
• SciPaths Benchmark
• Enabling Contributions
• Prior-work Grounding
• Language Model Evaluation

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.