arXiv Computation and Language By Lorenzo Cardarelli, Roberto Ragno

(Whose defaults?) Is artificial intelligence reorienting archaeological methods?

Read the original on arXiv Computation and Language →

The paper investigates whether large language models (LLMs) are narrowing the methodological diversity of archaeology. By analysing 119,000 abstracts from 2010‑2025 and running controlled experiments, the authors find only a modest shift in method use after 2023, with overall diversity actually increasing. However, LLMs tend to recommend a narrower, less diverse set of methods, especially without guidance, suggesting a potential convergence in methodological choice.

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arXiv AI
Aug 19

The Problem Is the Problem: Towards Scalable Mathematical Discovery

The paper introduces a new human‑AI collaboration paradigm for mathematical discovery, shifting from selecting individual problems to exploring broad research directions. It presents the Find, Attempt, and Recommend (FAR) pipeline, which automatically searches a literature corpus, filters candidate conjectures, and surfaces promising resolutions for expert review. In a combinatorics pilot, FAR processed over 5,000 papers, identified thousands of open conjectures, and ultimately highlighted 77 items that led to new discoveries.

By Zeyu Zheng, Shengtong Zhang, Jeremy Avigad, Prasad Tetali, Sean Welleck