# SciPaths: Forecasting Pathways to Scientific Discovery

arXiv: https://arxiv.org/abs/2605.14600 · HTML: https://mlsanity.com/paper/2605.14600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://mlsanity.com/paper/2305.14259/) (2023)
- [Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges](https://mlsanity.com/paper/2412.11427/) (2024)
- [GIANTS: Generative Insight Anticipation from Scientific Literature](https://mlsanity.com/paper/2604.09793/) (2026)
- [Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification](https://mlsanity.com/paper/2410.13332/) (2024)
- [Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations](https://mlsanity.com/paper/2503.18865/) (2025)

## Built on by (most cited first, 1 of 1)

- [Inspire: Benchmarking Scientific Literature Search for Open Research Problems](https://mlsanity.com/paper/2609.33233/) (2026)

## Abstract (arXiv)

Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generation rather than the dependencies that make progress possible. In this paper, we introduce discovery pathway forecasting: given a target scientific contribution and the prior literature available at a specified time, the task is to (1) identify the enabling contributions required to realize it and (2) ground each in prior work when such prior work exists. We present SciPaths, a benchmark of 262 expert-annotated gold pathways and 2,444 silver pathways constructed from machine learning and natural language processing papers, where each pathway records enabling contributions, roles, rationales, and prior-work groundings or unmapped decisions. Evaluating frontier and open-weight language models, we find that the best model reaches only 0.189 F1 under strict semantic matching, with core methodological dependencies hardest to recover. Prior-work grounding improves substantially when gold enabling contributions are provided, showing that decomposition quality is a major bottleneck for end-to-end pathway recovery. SciPaths therefore shifts evaluation toward a missing capability in scientific forecasting: reasoning backward from a target contribution to the enabling scientific building blocks and prior-work dependencies that make it feasible.