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AI research papers explore crystal generation and EHR modeling

Two new research papers have been published on arXiv, detailing advancements in distinct AI applications. The first, titled "CrystalAR: Autoregressive Modeling for Symmetric Crystal Generation," introduces a novel framework for generating crystal structures by incorporating space-group symmetry. The second paper, "Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling," presents MEDIATOR, a new tokenizer designed to improve the modeling of electronic health records by enabling open-vocabulary tokenization. AI

IMPACT These papers showcase advancements in specialized AI applications, potentially improving scientific discovery and healthcare data analysis.

RANK_REASON Two distinct academic papers published on arXiv detailing novel AI methodologies.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI research papers explore crystal generation and EHR modeling

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ziqiao Meng, Rongsheng Chen, Zhi Zheng, Nikita Kazeev, Zhenkun Wang, Andrey Ustyuzhanin, Wee Sun Lee ·

    CrystalAR: Autoregressive Modeling for Symmetric Crystal Generation

    arXiv:2610.27762v1 Announce Type: new Abstract: Deep generative modeling has achieved remarkable progress in crystal generation. Given the rich symmetries inherent in crystalline structures, recent work has sought to explicitly incorporate space-group symmetry into generative mod…

  2. arXiv cs.AI TIER_1 English(EN) · Tong Ding, Caiwei Tian, Dongmin Bang, Ming Y. Lu, Sophia J. Wagner, Andrew Zhang, Rowland Pettit, Long Phi Le, Faisal Mahmood ·

    Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling

    arXiv:2610.27047v1 Announce Type: cross Abstract: EHR foundation models promise scalable clinical prediction across health systems, yet their deployment is constrained by a tokenization bottleneck. The same clinical concept can appear as different ontology codes, local identifier…