An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations. - Arvind Ramanathan; Pratul K. Agarwal; Maria G. Kurnikova; Christopher James Langmead (J. Comput. Biol., 2010)

QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. - Andrej J. Savol; Virginia M. Burger; Pratul K. Agarwal; Arvind Ramanathan; Chakra S. Chennubhotla (Bioinform., 2011)

Quasi-Anharmonic Analysis Reveals Intermediate States in the Nuclear Co-Activator Receptor Binding Domain Ensemble. - Virginia M. Burger; Arvind Ramanathan; Andrej J. Savol; Christopher B. Stanley; Pratul K. Agarwal; Chakra S. Chennubhotla (Pacific Symposium on Biocomputing, 2012)

Performance modeling of microsecond scale biological molecular dynamics simulations on heterogeneous architectures. - Pratul K. Agarwal; Scott S. Hampton; Jeffrey D. Poznanovic; Arvind Ramanathan; Sadaf R. Alam; Paul S. Crozier (Concurr. Comput. Pract. Exp., 2013)

EpiSpec: A formal specification language for parameterized agent-based models against epidemiological ground truth. - Faraz Hussain 001; Arvind Ramanathan; Laura L. Pullum; Sumit Kumar Jha 001 (ICCABS, 2014)

Oak Ridge Biosurveillance Toolkit: Scalable machine learning for public health surveillance. - Laura L. Pullum; Arvind Ramanathan (ICCABS, 2014)

Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians. - Kunal Malhotra; Tanner C. Hobson; Silvia Valkova; Laura L. Pullum; Arvind Ramanathan (IEEE BigData, 2015)

NoC Architectures as Enablers of Biological Discovery for Personalized and Precision Medicine. - Paul Bogdan; Turbo Majumder; Arvind Ramanathan; Yuankun Xue (NOCS, 2015)

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics. - Arvind Ramanathan; Laura L. Pullum; Tanner C. Hobson; Chad A. Steed; Shannon P. Quinn; Chakra S. Chennubhotla; Silvia Valkova (BMC Bioinform., 2015)

Constellation: A science graph network for scalable data and knowledge discovery in extreme-scale scientific collaborations. - Sudharshan S. Vazhkudai; John Harney; Raghul Gunasekaran; Dale Stansberry; Seung-Hwan Lim; Tom Barron; Andrew Nash; Arvind Ramanathan (IEEE BigData, 2016)

Integrating symbolic and statistical methods for testing intelligent systems: Applications to machine learning and computer vision. - Arvind Ramanathan; Laura L. Pullum; Faraz Hussain 001; Dwaipayan Chakrabarty; Sumit Kumar Jha 001 (DATE, 2016)

Multi-task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports. - Hong-Jun Yoon; Arvind Ramanathan; Georgia D. Tourassi (INNS Conference on Big Data, 2016)

Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models. - Özgür Özmen; James J. Nutaro; Laura L. Pullum; Arvind Ramanathan (Simul., 2016)

Testing autonomous cyber-physical systems using fuzzing features from convolutional neural networks: work-in-progress. - Sunny Raj; Sumit Kumar Jha 001; Arvind Ramanathan; Laura L. Pullum (EMSOFT Companion, 2017)

SATYA : Defending Against Adversarial Attacks Using Statistical Hypothesis Testing. - Sunny Raj; Laura Pullum; Arvind Ramanathan; Sumit Kumar Jha 001 (FPS, 2017)

Adversarial attacks on computer vision algorithms using natural perturbations. - Arvind Ramanathan; Laura L. Pullum; Zubir Husein; Sunny Raj; Neslisah Torosdagli; Sumanta N. Pattanaik; Sumit Kumar Jha 001 (IC3, 2017)

Data-Driven Immunization. - Yao Zhang 003; Arvind Ramanathan; Anil Vullikanti; Laura L. Pullum; B. Aditya Prakash (ICDM, 2017)

Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer. - Hong-Jun Yoon; Arvind Ramanathan; Folami Alamudun; Georgia D. Tourassi (IWBI, 2018)

Hierarchical Convolutional Attention Networks for Text Classification. - Shang Gao 008; Arvind Ramanathan; Georgia D. Tourassi (Rep4NLP@ACL, 2018)

HyperSpace: Distributed Bayesian Hyperparameter Optimization. - M. Todd Young; Jacob D. Hinkle; Arvind Ramanathan; Ramakrishnan Kannan (SBAC-PAD, 2018)

Deep clustering of protein folding simulations. - Debsindhu Bhowmik; Shang Gao 008; Michael T. Young; Arvind Ramanathan (BMC Bioinform., 2018)

Scalable deep text comprehension for Cancer surveillance on high-performance computing. - John X. Qiu; Hong-Jun Yoon; Kshitij Srivastava; Thomas P. Watson; James Blair Christian; Arvind Ramanathan; Xiao-Cheng Wu; Paul A. Fearn; Georgia D. Tourassi (BMC Bioinform., 2018)

Hierarchical attention networks for information extraction from cancer pathology reports. - Shang Gao 008; Michael T. Young; John X. Qiu; Hong-Jun Yoon; James Blair Christian; Paul A. Fearn; Georgia D. Tourassi; Arvind Ramanathan (J. Am. Medical Informatics Assoc., 2018)

Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC. - Hong-Jun Yoon; John Gounley; Shang Gao 008; Mohammed M. Alawad; Arvind Ramanathan; Georgia D. Tourassi (BHI, 2019)

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations. - Junghoon Chae; Debsindhu Bhowmik; Heng Ma; Arvind Ramanathan; Chad A. Steed (IEEE BigData, 2019)

Towards Native Execution of Deep Learning on a Leadership-Class HPC System. - Srikanth B. Yoginath; Md. Maksudul Alam; Arvind Ramanathan; Debsindhu Bhowmik; Nouamane Laanait; Kalyan S. Perumalla (IPDPS Workshops, 2019)

Deep Generative Model Driven Protein Folding Simulations. - Heng Ma; Debsindhu Bhowmik; Hyungro Lee; Matteo Turilli; Michael T. Young; Shantenu Jha; Arvind Ramanathan (PARCO, 2019)

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding. - Hyungro Lee; Matteo Turilli; Shantenu Jha; Debsindhu Bhowmik; Heng Ma; Arvind Ramanathan (DLS@SC, 2019)

Classifying cancer pathology reports with hierarchical self-attention networks. - Shang Gao 008; John X. Qiu; Mohammed M. Alawad; Jacob D. Hinkle; Noah Schaefferkoetter; Hong-Jun Yoon; James Blair Christian; Paul A. Fearn; Lynne Penberthy; Xiao-Cheng Wu; Linda Coyle; Georgia D. Tourassi; et al. (Artif. Intell. Medicine, 2019)

Data-driven efficient network and surveillance-based immunization. - Yao Zhang 003; Arvind Ramanathan; Anil Vullikanti; Laura Pullum; B. Aditya Prakash (Knowl. Inf. Syst., 2019)

Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions. - Akash Parvatikar; Om Choudhary; Arvind Ramanathan; Olga Navolotskaia; Gloria Carter; Akif Burak Tosun; Jeffrey L. Fine; S. Chakra Chennubhotla (MICCAI (5), 2020)

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release. - Yadu N. Babuji; Ben Blaiszik; Tom Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Ian T. Foster; Zhi Hong; Shantenu Jha; Zhuozhao Li; Xuefeng Liu; Arvind Ramanathan; et al. (CoRR, 2020)

Scalable HPC and AI Infrastructure for COVID-19 Therapeutics. - Hyungro Lee; André Merzky; Li Tan; Mikhail Titov; Matteo Turilli; Dario Alfè; Agastya Bhati; Alex Brace; Austin Clyde; Peter V. Coveney; Heng Ma; Arvind Ramanathan; et al. (CoRR, 2020)

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins. - Arvind Ramanathan; Heng Ma; Akash Parvatikar; S. Chakra Chennubhotla (CoRR, 2020)

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. - Atanu Acharya; Rupesh Agarwal; Matthew B. Baker; Jérôme Baudry; Debsindhu Bhowmik; Swen Böhm; Kendall G. Byler; Sam Yen-Chi Chen; Leighton Coates; Connor J. Cooper; Omar Demerdash; Isabella Daidone; et al. (J. Chem. Inf. Model., 2020)

Distributed Bayesian optimization of deep reinforcement learning algorithms. - M. Todd Young; Jacob D. Hinkle; Ramakrishnan Kannan; Arvind Ramanathan (J. Parallel Distributed Comput., 2020)

Attacking NIST biometric image software using nonlinear optimization. - Sunny Raj; Jodh S. Pannu; Steven Lawrence Fernandes; Arvind Ramanathan; Laura L. Pullum; Sumit Kumar Jha 001 (Pattern Recognit. Lett., 2020)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. - Aymen Al Saadi; Dario Alfè; Yadu N. Babuji; Agastya Bhati; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Peter V. Coveney; Ian T. Foster; et al. (ICPP, 2021)

The 4th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021). - Bijaya Adhikari; Ajitesh Srivastava; Sen Pei; Sarah Kefayati; Rose Yu; Amulya Yadav; Alexander Rodríguez; Arvind Ramanathan; Anil Vullikanti; B. Aditya Prakash (KDD, 2021)

Prototypical Models for Classifying High-Risk Atypical Breast Lesions. - Akash Parvatikar; Om Choudhary; Arvind Ramanathan; Rebekah Jenkins; Olga Navolotskaia; Gloria Carter; Akif Burak Tosun; Jeffrey L. Fine; S. Chakra Chennubhotla (MICCAI (8), 2021)

Stream-AI-MD: streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms. - Alexander Brace; Michael Salim; Vishal Subbiah; Heng Ma; Murali Emani; Anda Trifan; Austin R. Clyde; Corey Adams; Thomas D. Uram; Hyun Seung Yoo; Andew Hock; Jessica Liu; et al. (PASC, 2021)

Scalable HPC & AI infrastructure for COVID-19 therapeutics. - Hyungro Lee; André Merzky; Li Tan; Mikhail Titov; Matteo Turilli; Dario Alfè; Agastya Bhati; Alex Brace; Austin Clyde; Peter V. Coveney; Heng Ma; Arvind Ramanathan; et al. (PASC, 2021)

Adversarial Attacks against AI-driven Experimental Peptide Design Workflows. - Arvind Ramanathan; Sumit Kumar Jha 001 (XLOOP@SC, 2021)

Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers. - Agastya P. Bhati; Shunzhou Wan; Dario Alfè; Austin R. Clyde; Mathis Bode; Li Tan; Mikhail Titov; André Merzky; Matteo Turilli; Shantenu Jha; Roger R. Highfield; Walter Rocchia; et al. (CoRR, 2021)

Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds. - Austin Clyde; Arvind Ramanathan; Rick Stevens (CoRR, 2021)

Achieving 100X faster simulations of complex biological phenomena by coupling ML to HPC ensembles. - Alexander Brace; Hyungro Lee; Heng Ma; Anda Trifan; Matteo Turilli; Igor Yakushin; Todd S. Munson; Ian T. Foster; Shantenu Jha; Arvind Ramanathan (CoRR, 2021)

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening. - Austin Clyde; Thomas S. Brettin; Alexander Partin; Hyun Seung Yoo; Yadu N. Babuji; Ben Blaiszik; André Merzky; Matteo Turilli; Shantenu Jha; Arvind Ramanathan; Rick Stevens (CoRR, 2021)

CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models. - Max Zvyagin; Thomas S. Brettin; Arvind Ramanathan; Sumit Kumar Jha 001 (CoRR, 2021)

Protein Folding Neural Networks Are Not Robust. - Sumit Kumar Jha 001; Arvind Ramanathan; Rickard Ewetz; Alvaro Velasquez; Susmit Jha (CoRR, 2021)

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. - Lorenzo Casalino; Abigail C. Dommer; Zied Gaieb; Emília P. Barros; Terra Sztain; Surl-Hee Ahn; Anda Trifan; Alexander Brace; Anthony T. Bogetti; Austin Clyde; Heng Ma; Hyungro Lee; et al. (Int. J. High Perform. Comput. Appl., 2021)

Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites. - Eunice Cho; Margarida Rosa; Ruhi Anjum; Saman Mehmood; Mariya Soban; Moniza Mujtaba; Khair Bux; Syed Tarique Moin; Mohammad Tanweer; Sarath Dantu; Alessandro Pandini; Junqi Yin; et al. (J. Chem. Inf. Model., 2021)

Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity. - Jeremy Feinstein; Ganesh Sivaraman; Kurt Picel; Brian Peters; Álvaro Vázquez-Mayagoitia; Arvind Ramanathan; Margaret Macdonell; Ian T. Foster; Eugene Yan (J. Chem. Inf. Model., 2021)

Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations. - Sumit Kumar Jha 001; Rickard Ewetz; Alvaro Velasquez; Arvind Ramanathan; Susmit Jha (AAAI, 2022)

Coupling streaming AI and HPC ensembles to achieve 100-1000× faster biomolecular simulations. - Alexander Brace; Igor Yakushin; Heng Ma; Anda Trifan; Todd S. Munson; Ian T. Foster; Arvind Ramanathan; Hyungro Lee; Matteo Turilli; Shantenu Jha (IPDPS, 2022)

Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks. - Ryien Hosseini; Filippo Simini; Austin Clyde; Arvind Ramanathan (CoRR, 2022)

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action. - Anda Trifan; Defne Gorgun; Michael Salim; Zongyi Li; Alexander Brace; Maxim Zvyagin; Heng Ma; Austin Clyde; David Clark; David J. Hardy; Tom Burnley; Lei Huang 019; et al. (Int. J. High Perform. Comput. Appl., 2022)

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor. - Austin Clyde; Stephanie Galanie; Daniel W. Kneller; Heng Ma; Yadu N. Babuji; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Leighton Coates; Ian T. Foster; et al. (J. Chem. Inf. Model., 2022)

Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats. - Wei Chen 043; Yihui Ren 001; Ai Kagawa; Matthew R. Carbone; Samuel Yen-Chi Chen; Xiaohui Qu; Shinjae Yoo; Austin Clyde; Arvind Ramanathan; Rick L. Stevens; Hubertus Van Dam; Deyu Lu (ICMLA, 2023)

Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery. - Ashka Shah; Arvind Ramanathan; Valérie Hayot-Sasson; Rick Stevens (PASC, 2023)

Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers. - Alexander Brace; Rafael Vescovi; Ryan Chard; Nickolaus D. Saint; Arvind Ramanathan; Nestor J. Zaluzec; Ian T. Foster (SC Workshops, 2023)

Protein Generation via Genome-scale Language Models with Bio-physical Scoring. - Gautham Dharuman; Arvind Ramanathan (SC Workshops, 2023)

Scalable Lead Prediction with Transformers using HPC resources. - Archit Vasan; Thomas S. Brettin; Rick Stevens; Arvind Ramanathan; Venkatram Vishwanath (SC Workshops, 2023)

On the Robustness of AlphaFold: A COVID-19 Case Study. - Ismail Alkhouri; Sumit Kumar Jha 001; Andre Beckus; George K. Atia; Alvaro Velasquez; Rickard Ewetz; Arvind Ramanathan; Susmit Jha (CoRR, 2023)

Towards a Modular Architecture for Science Factories. - Rafael Vescovi; Tobias Ginsburg; Kyle Hippe; Doga Ozgulbas; Casey Stone; Abraham Stroka; Rory Butler; Ben Blaiszik; Tom Brettin; Kyle Chard; Mark Hereld; Arvind Ramanathan; et al. (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. - Abigail C. Dommer; Lorenzo Casalino; Fiona L. Kearns; Mia A. Rosenfeld; Nicholas Wauer; Surl-Hee Ahn; John Russo 003; A. Sofia F. Oliveira; Clare Morris; Anthony T. Bogetti; Anda Trifan; Alexander Brace; et al. (Int. J. High Perform. Comput. Appl., 2023)

Model certainty in cellular network-driven processes with missing data. - Michael W. Irvin; Arvind Ramanathan; Carlos F. Lopez (PLoS Comput. Biol., 2023)

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2. - Rajendra P. Joshi; Katherine J. Schultz; Jesse William Wilson; Agustin Kruel; Rohith Anand Varikoti; Chathuri J. Kombala; Daniel W. Kneller; Stephanie Galanie; Gwyndalyn Phillips; Qiu Zhang; Leighton Coates; Jyothi Parvathareddy; et al. (J. Chem. Inf. Model., 2023)

ChemoGraph: Interactive Visual Exploration of the Chemical Space. - Bharat Kale; Austin Clyde; Maoyuan Sun; Arvind Ramanathan; Rick Stevens; Michael E. Papka (Comput. Graph. Forum, 2023)

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. - Maxim Zvyagin; Alexander Brace; Kyle Hippe; Yuntian Deng; Bin Zhang; Cindy Orozco Bohorquez; Austin Clyde; Bharat Kale; Danilo Perez-Rivera; Heng Ma; Carla M. Mann; Michael W. Irvin; et al. (Int. J. High Perform. Comput. Appl., 2023)

Equivariant Graph Neural Operator for Modeling 3D Dynamics. - Minkai Xu; Jiaqi Han; Aaron Lou; Jean Kossaifi; Arvind Ramanathan; Kamyar Azizzadenesheli; Jure Leskovec; Stefano Ermon; Anima Anandkumar (ICML, 2024)

High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model. - Archit Vasan; Ozan Gökdemir; Alexander Brace; Arvind Ramanathan; Thomas S. Brettin; Rick Stevens; Venkatram Vishwanath (IPDPS (Workshops), 2024)

MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization. - Gautham Dharuman; Kyle Hippe; Alexander Brace; Sam Foreman; Väinö Hatanpää; Varuni Katti Sastry; Huihuo Zheng; Logan T. Ward; Servesh Muralidharan; Archit Vasan; Bharat Kale; Carla M. Mann; et al. (SC, 2024)

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. - Peter St. John; Dejun Lin; Polina Binder; Malcolm Greaves; Vega Shah; John St. John; Adrian Lange; Patrick D. Hsu; Rajesh Illango; Arvind Ramanathan; Anima Anandkumar; David H. Brookes; et al. (CoRR, 2024)

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights. - Ozan Gökdemir; Carlo Siebenschuh; Alexander Brace; Azton Wells; Brian Hsu; Kyle Hippe; Priyanka Setty; Aswathy Ajith; J. Gregory Pauloski; Varuni Sastry; Sam Foreman; Huihuo Zheng; et al. (PASC, 2025)

A text-guided protein design framework. - Shengchao Liu; Yanjing Li; Zhuoxinran Li; Anthony Gitter; Yutao Zhu 001; Jiarui Lu; Zhao Xu 005; Weili Nie; Arvind Ramanathan; Chaowei Xiao; Jian Tang 005; Hongyu Guo; et al. (Nat. Mac. Intell., 2025)

Connecting Large Language Model Agent to High Performance Computing Resource. - Heng Ma; Alexander Brace; Carlo Siebenschuh; J. Gregory Pauloski; Ian T. Foster; Arvind Ramanathan (CoRR, 2025)

MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories. - Natalie Tirabassi; Sathish A. P. Kumar; Sumit Jha 001; Arvind Ramanathan (CoRR, 2025)

Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms. - Sai Gana Sandeep Pula; Sathish A. P. Kumar; Sumit Jha 001; Arvind Ramanathan (CoRR, 2025)

DML-RAM: Deep Multimodal Learning Framework for Robotic Arm Manipulation using Pre-trained Models. - Sathish Kumar; Swaroop Damodaran; Naveen Kumar Kuruba; Sumit Jha 001; Arvind Ramanathan (CoRR, 2025)

ACE-RLHF: Automated Code Evaluation and Socratic Feedback Generation Tool using Large Language Models and Reinforcement Learning with Human Feedback. - Tasnia Rahman; Sathish A. P. Kumar; Sumit Kumar Jha 001; Arvind Ramanathan (CoRR, 2025)

AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine. - Carlo Siebenschuh; Kyle Hippe; Ozan Gökdemir; Alexander Brace; Arham Khan; Khalid Hossain; Yadu N. Babuji; Nicholas Chia; Venkatram Vishwanath; Rick Stevens; Arvind Ramanathan; Ian T. Foster; et al. (CoRR, 2025)

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights. - Ozan Gökdemir; Carlo Siebenschuh; Alexander Brace; Azton Wells; Brian Hsu; Kyle Hippe; Priyanka Vasanthakumari; Aswathy Ajith; J. Gregory Pauloski; Varuni Katti Sastry; Sam Foreman; Huihuo Zheng; et al. (CoRR, 2025)