Aussie AI

Applications of Generative AI

  • Last Updated 30 August, 2025
  • by David Spuler, Ph.D.

Apps Built on AI

Building Applications for Generative AI

Research on building Gen AI apps:

Inference Frameworks

Research papers include:

Orchestration Frameworks

Research papers include:

LangChain

LangChain is an AI orchestration framework that allows "chaining" of multiple components in a sequence. Research papers on LangChain usage:

Wrap Architectures for Gen AI Applications

The simplest architectures for AI applications are those that simply "wrap" around LLMs, whether it is commercial LLMs like GPT, or open source LLMs like Mistral or Llama.

OpenAI API Applications

One particular type of "wrap" AI application is to use the OpenAI API (e.g. for ChatGPT).

Batch API for Inference

Application Layer

The "application layer" is the whole range of applications that can be built on top of generative AI and its LLMs as building blocks. Research includes:

Code Generation Applications of Generative AI

Code Checker Applications

User Interface (UI) Issues for AI Apps

Workflow

Research paper on workflow interfaces for AI applications:

Consoles

Declarative Programming

Declarative programming is the method of creating apps by defining what to do, rather than how to do it. The language to define a declarative app is more like a configuration file, rather than a procedural programming language like C++.

Research on declarative programming issues:

Script Languages

  • L. Zheng, L. Yin, Z. Xie, J. Huang, C. Sun, C. H. Yu, S. Cao, C. Kozyrakis, I. Stoica, J. E. Gonzalez et al., Dec 2023, Efficiently programming large language models using SGLang, arXiv preprint arXiv:2312.07104, 2023, https://arxiv.org/abs/2312.07104 (Uses a radix attention method, a trie or prefix tree, for KV caching.)
  • Hongzheng Chen, Niansong Zhang, Shaojie Xiang, Zhichen Zeng, Mengjia Dai, Zhiru Zhang, 7 Apr 2024, Allo: A Programming Model for Composable Accelerator Design, https://arxiv.org/abs/2404.04815
  • Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts, 5 Oct 2023, DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines, https://arxiv.org/abs/2310.03714 Code: https://github.com/stanfordnlp/dspy
  • Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si, 19 Jun 2024, APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts, https://arxiv.org/abs/2406.13161 Code: https://github.com/appl-team/appl (A Python-like script language for prompt engineering integration into applications and agents.)
  • Till Döhmen, 2024/10/17, Introducing the prompt() Function: Use the Power of LLMs with SQL! https://motherduck.com/blog/sql-llm-prompt-function-gpt-models/
  • Mandana Vaziri, Louis Mandel, Claudio Spiess, Martin Hirzel, 24 Oct 2024, PDL: A Declarative Prompt Programming Language, https://arxiv.org/abs/2410.19135
  • Saksham Goel, October 29, 2024, Build LLM/RAG pipelines with YAML templates by Pathway, https://pathway.com/blog/llm-yaml-templates
  • Yuka Ikarashi, Kevin Qian, Samir Droubi, Alex Reinking, Gilbert Bernstein, Jonathan Ragan-Kelley, 14 Nov 2024 (v2), Exo 2: Growing a Scheduling Language, https://arxiv.org/abs/2411.07211

API Architectures

Plugins

Custom AI Apps

No Code/Low Code for AI Apps

Miniapps

Tabular Data Applications

  • Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos, 1 Mar 2024 (v2), Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey, https://arxiv.org/abs/2402.17944
  • Weijia Wang, 2023, Efficient and Explainable Machine Learning Ph.D. thesis, University of California San Diego, https://escholarship.org/content/qt9q52g27p/qt9q52g27p_noSplash_70dba1eae3531240d1fec8e0cdaf1be2.pdf (Processing of tabular data is a weakness of GenAI models, and this thesis examines various issues of tabular data and rules-based processing.)
  • David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis, HyperFast: Instant Classification for Tabular Data, 2023, NeurIPS 2023, https://openreview.net/pdf?id=VRBhaU8IDz
  • Irwin Deng, Kushagra Dixit, Vivek Gupta, Dan Roth, 22 Jul 2024, Enhancing Temporal Understanding in LLMs for Semi-structured Tables, https://arxiv.org/abs/2407.16030
  • Liang, X., Hu, R., Liu, Y., Zhu, K. (2024). Open-Domain Question Answering over Tables with Large Language Models. In: Huang, DS., Pan, Y., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14873. Springer, Singapore. https://doi.org/10.1007/978-981-97-5615-5_28 https://link.springer.com/chapter/10.1007/978-981-97-5615-5_28
  • Xianjie Wu, Jian Yang, Linzheng Chai, Ge Zhang, Jiaheng Liu, Xinrun Du, Di Liang, Daixin Shu, Xianfu Cheng, Tianzhen Sun, Guanglin Niu, Tongliang Li, Zhoujun Li, 17 Aug 2024, TableBench: A Comprehensive and Complex Benchmark for Table Question Answering, https://www.arxiv.org/abs/2408.09174
  • Asim Biswal, Liana Patel, Siddarth Jha, Amog Kamsetty, Shu Liu, Joseph E. Gonzalez, Carlos Guestrin, Matei Zaharia, 27 Aug 2024, Text2SQL is Not Enough: Unifying AI and Databases with TAG, https://arxiv.org/abs/2408.14717 https://github.com/TAG-Research/TAG-Bench
  • Shubham Sharma, September 2, 2024, Table-augmented generation shows promise for complex dataset querying, outperforms text-to-SQL, https://venturebeat.com/data-infrastructure/table-augmented-generation-shows-promise-for-complex-dataset-querying-outperforms-text-to-sql/
  • S Madden, M Cafarella, M Franklin, T Kraska, 2024, Databases Unbound: Querying All of the World’s Bytes with AI, https://www.vldb.org/pvldb/vol17/p4546-madden.pdf
  • Shubham Sharma, September 12, 2024, Google’s DataGemma AI is a statistics wizard, https://venturebeat.com/ai/datagemma-googles-open-ai-models-mitigate-hallucination-on-statistical-queries/
  • David Gewirtz, Sept. 16, 2024, Why natural language AI scripting in Microsoft Excel could be a game changer. What if you could run advanced Excel analyses with no coding skills? Here's how Microsoft's Copilot in Excel could use Python to allow you to do just that, https://www.zdnet.com/article/why-natural-language-ai-scripting-in-microsoft-excel-could-be-a-game-changer/
  • Xinyuan Lu, Liangming Pan, Yubo Ma, Preslav Nakov, Min-Yen Kan, 18 Sep 2024, TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning, https://arxiv.org/abs/2409.11724 https://github.com/XinyuanLu00/TART
  • Yuzhang Tian, Jianbo Zhao, Haoyu Dong, Junyu Xiong, Shiyu Xia, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang, 12 Jul 2024, SpreadsheetLLM: Encoding Spreadsheets for Large Language Models, https://arxiv.org/abs/2407.09025
  • Mukul Singh, Gust Verbruggen, Vu Le, and Sumit Gulwani. 2024. Tabularis Revilio: Converting Text to Tables. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24). Association for Computing Machinery, New York, NY, USA, 4056–4060. https://doi.org/10.1145/3627673.3680000 https://dl.acm.org/doi/abs/10.1145/3627673.3680000
  • LangChain, Aug 10, 2024, UX for Agents, Part 3: Spreadsheet, Generative, and Collaborative UI/UX, https://blog.langchain.dev/ux-for-agents-part-3/
  • Deyi Ji, Lanyun Zhu, Siqi Gao, Peng Xu, Hongtao Lu, Jieping Ye, Feng Zhao, 13 Nov 2024, Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding, https://arxiv.org/abs/2411.08516
  • Qwen: An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zihan Qiu (additional authors not shown), 19 Dec 2024, Qwen2.5 Technical Report, https://arxiv.org/abs/2412.15115
  • Xiaoqiang Kang, Zimu Wang, Xiaobo Jin, Wei Wang, Kaizhu Huang, Qiufeng Wang, 20 Dec 2024, Template-Driven LLM-Paraphrased Framework for Tabular Math Word Problem Generation, https://arxiv.org/abs/2412.15594 https://github.com/Jason8Kang/TELL
  • Zipeng Qiu, You Peng, Guangxin He, Binhang Yuan, Chen Wang, 29 Nov 2024, TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension, https://arxiv.org/abs/2411.19504
  • Mayi Xu, Yunfeng Ning, Yongqi Li, Jianhao Chen, Jintao Wen, Yao Xiao, Shen Zhou, Birong Pan, Zepeng Bao, Xin Miao, Hankun Kang, Ke Sun, Tieyun Qian, 2 Jan 2025, Reasoning based on symbolic and parametric knowledge bases: a survey, https://arxiv.org/abs/2501.01030 (Extensive survey of reasoning from CoT to knowledge graphs to table-based reasoning.)
  • FZ Subah, Oct 2025, Mitigating and Assessing Bias and Fairness in Large Language Model-Generated Synthetic Tabular Data, Masters Thesis, Department of Engineering, University of Cambridge, https://www.mlmi.eng.cam.ac.uk/files/2023-2024/fzs21_mitigating_2024.pdf
  • G Wang, S Zhang, T Zhan, Z Shen, J Li, X Hu, X Sun, Jan 2025, Unlocking the Mysteries of OpenAI o1: A Survey of the Reasoning Abilities of Large Language Models, https://openreview.net/pdf?id=J0ADLa2rNp
  • Connor Shorten, Charles Pierse, Thomas Benjamin Smith, Karel D'Oosterlinck, Tuana Celik, Erika Cardenas, Leonie Monigatti, Mohd Shukri Hasan, Edward Schmuhl, Daniel Williams, Aravind Kesiraju, Bob van Luijt, 23 Jan 2025, Querying Databases with Function Calling, https://arxiv.org/abs/2502.00032
  • Minchae Song, 21 May 2025, Enhancing RAG Performance by Representing Hierarchical Nodes in Headers for Tabular Data, IEEE Access, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11003975
  • Xiaohan Yu, Pu Jian, Chong Chen 12 Jun 2025, TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning, https://arxiv.org/abs/2506.10380 https://github.com/yxh-y/TableRAG/tree/main
  • Paul Gross, June 17, 2025, Double-Entry Ledgers: The Missing Primitive in Modern Software, https://pgrs.net/2025/06/17/double-entry-ledgers-missing-primitive-in-modern-software/
  • Eunbin Lee, Younghan Lee, Ho Bae, July 2025, A Systematic Framework for Enhancing Retrieval-Augmented Generation for Tabular Data, https://koreascience.kr/article/JAKO202519957603573.pdf
  • Daniel Beaglehole, David Holzm\"uller, Adityanarayanan Radhakrishnan, Mikhail Belkin, 12 Aug 2025, xRFM: Accurate, scalable, and interpretable feature learning models for tabular data, https://arxiv.org/abs/2508.10053
  • Lalitesh Morishetti, Abhay Kumar, Jonathan Scott, Kaushiki Nag, Gunjan Sharma, Shanu Vashishtha, Rahul Sridhar, Rohit Chatter, and Kannan Achan, 13 Aug 2025, Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data, https://arxiv.org/abs/2508.09636
  • Jihye Lee, Minseo Kang, and Dongha Kim, 14 Aug 2025, MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts, https://arxiv.org/abs/2507.08280
  • Jessup Byun, Xiaofeng Lin, Joshua Ward, Guang Cheng, 22 Jul 2025, Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation, https://arxiv.org/abs/2507.17066
  • Vinura Galwaduge, Jagath Samarabandu, 23 Jul 2025, Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection, https://arxiv.org/abs/2507.17161
  • Rafael Ayll\'on-Gavil\'an, David Guijo-Rubio, Antonio Manuel G\'omez-Orellana, David Guijo-Rubio, Francisco B\'erchez-Moreno, V\'ictor Manuel Vargas-Yun and Pedro A. Guti\'errez, 23 Jul 2025, TOC-UCO: a comprehensive repository of tabular ordinal classification datasets, https://arxiv.org/abs/2507.17348
  • Calvin McCarter, 23 Jul 2025, Unmasking Trees for Tabular Data, https://arxiv.org/abs/2407.05593
  • Chaoyi Zhu, Jiayi Tang, Juan F. P\'erez, Marten van Dijk, Lydia Y. Chen, 21 Jul 2025, DP-TLDM: Differentially Private Tabular Latent Diffusion Model, https://arxiv.org/abs/2403.07842
  • Eduardo Aguilar-Bejarano, Daniel Lea, Karthikeyan Sivakumar, Jimiama M. Mase, Reza Omidvar, Ruizhe Li, Troy Kettle, James Mitchell-White, Morgan R Alexander, David A Winkler, Grazziela Figueredo, 23 Jul 2025, Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data, https://arxiv.org/abs/2507.17791
  • Shubham Mohole, Sainyam Galhotra, 23 Jul 2025, SIFOTL: A Principled, Statistically-Informed Fidelity-Optimization Method for Tabular Learning, https://arxiv.org/abs/2507.17979
  • Rana Alshaikh, Israa Alghanmi, Shelan Jeawak, 24 Jul 2025, AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data, https://arxiv.org/abs/2507.18442
  • Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci, 24 Jul 2025, Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models, https://arxiv.org/abs/2507.18504
  • Aleksey Lapin, Igor Hromov, Stanislav Chumakov, Mile Mitrovic, Dmitry Simakov, Nikolay O. Nikitin, Andrey V. Savchenko, 17 Jul 2025, LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data, https://arxiv.org/abs/2507.13413
  • Anh Nguyen, Sam Schafft, Nicholas Hale, John Alfaro, 21 Jul 2025, FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs, https://arxiv.org/abs/2507.15839
  • Andrey Sidorenko and Paul Tiwald, 8 Aug 2025, Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN, https://arxiv.org/abs/2508.06647
  • Zilong Zhao, Robert Birke, Aditya Kunar, Lydia Y. Chen, 11 Aug 2025, Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data, https://arxiv.org/abs/2108.07927
  • Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon, 9 Aug 2025, TFMPathy: Tabular Foundation Model for Privacy-Aware, Generalisable Empathy Detection from Videos, https://arxiv.org/abs/2504.10808
  • Yaobin Ling, Xiaoqian Jiang, Yejin Kim, 28 Jul 2025, MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data, https://arxiv.org/abs/2406.10521
  • Xuechen Li, Yupeng Li, Jian Liu, Xiaolin Jin and Xin Hu, 29 Jul 2025, Multi-branch of Attention Yields Accurate Results for Tabular Data, https://arxiv.org/abs/2502.12507
  • Sophie Kearney, Shu Yang, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Jason Moore, Marylyn Ritchie, Li Shen, 31 Jul 2025, Enabling Few-Shot Alzheimer's Disease Diagnosis on Tabular Biomarker Data with LLMs, https://arxiv.org/abs/2507.23227
  • Patricia A. Apell\'aniz and Ana Jim\'enez and Borja Arroyo Galende and Juan Parras and Santiago Zazo, 31 Jul 2025, Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios, https://arxiv.org/abs/2407.03080
  • Leonidas Akritidis, Panayiotis Bozanis, 1 Aug 2025, A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces, https://arxiv.org/abs/2508.00472
  • Ivona Krchova, Mariana Vargas Vieyra, Mario Scriminaci, Andrey Sidorenko, 1 Aug 2025, Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK, https://arxiv.org/abs/2508.00718
  • Timur Sattarov, Marco Schreyer, Damian Borth, 1 Aug 2025, Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data, https://arxiv.org/abs/2508.00758
  • Xiaofeng Wu, Alan Ritter, Wei Xu, 31 Jul 2025, Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges, https://arxiv.org/abs/2508.00217
  • Patrik Kenfack, Samira Ebrahimi Kahou, Ulrich A\"ivodji, 1 Aug 2025, Towards Fair In-Context Learning with Tabular Foundation Models, https://arxiv.org/abs/2505.09503
  • Mengshi Chen, Yuxiang Sun, Tengchao Li, Jianwei Wang, Kai Wang, Xuemin Lin, Ying Zhang, Wenjie Zhang, 3 Aug 2025, Empowering Tabular Data Preparation with Language Models: Why and How?, https://arxiv.org/abs/2508.01556
  • Siyi Liu, Yujia Zheng, Yongqi Zhang, 4 Aug 2025, StructSynth: Leveraging LLMs for Structure-Aware Tabular Data Synthesis in Low-Data Regimes, https://arxiv.org/abs/2508.02601
  • Riccardo Francia, Maurizio Leone, Giorgio Leonardi, Stefania Montani, Marzio Pennisi, Manuel Striani, Sandra D'Alfonso, 4 Aug 2025, AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data, https://arxiv.org/abs/2508.02625
  • Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen, Xiaoge Zhang, Kaiyu Tang, Xiao Li, Jing Li, 2 Aug 2025, A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective, https://arxiv.org/abs/2505.22322
  • Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal, 5 Aug 2025, MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation, https://arxiv.org/abs/2508.03083
  • Mengao Zhang, Jiayu Fu, Tanya Warrier, Yuwen Wang, Tianhui Tan, Ke-wei Huang, 7 Aug 2025, FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in finance, https://arxiv.org/abs/2508.05201
  • Yunbo Long, Liming Xu, Alexandra Brintrup, 7 Aug 2025, LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion, https://arxiv.org/abs/2503.02161
  • Ruiyu Zhang, Ce Zhao, Xin Zhao, Lin Nie, Wai-Fung Lam, 8 Aug 2025, Structural Equation-VAE: Disentangled Latent Representations for Tabular Data, https://arxiv.org/abs/2508.06347
  • Arshia Ilaty, Hossein Shirazi, Hajar Homayouni, 11 Aug 2025, SynLLM: A Comparative Analysis of Large Language Models for Medical Tabular Synthetic Data Generation via Prompt Engineering, https://arxiv.org/abs/2508.08529
  • Adri\'an Gude, Roi Santos-R\'ios, Francisco Prado-Vali\~no, Ana Ezquerro, Jes\'us Vilares, 12 Aug 2025, LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA, https://arxiv.org/abs/2508.09012
  • Peng Wang, Dongsheng Wang, He Zhao, Hangting Ye, Dandan Guo, Yi Chang, 12 Aug 2025, LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data, https://arxiv.org/abs/2508.09263
  • Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers, Martin Atzmueller, 13 Aug 2025, Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?, https://arxiv.org/abs/2508.09888
  • Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri, Ziyu Wang, Nima TaheriNejad, Axel Jantsch, Amir M. Rahmani, 15 Aug 2025, FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation, https://arxiv.org/abs/2508.11810
  • Andr\'es Guzm\'an-Cordero, Floor Eijkelboom, Jan-Willem van de Meent, 15 Aug 2025, Exponential Family Variational Flow Matching for Tabular Data Generation, https://arxiv.org/abs/2506.05940
  • Bastian Sch\"afer and Lennart Purucker and Maciej Janowski and Frank Hutter, 19 Aug 2025, How Usable is Automated Feature Engineering for Tabular Data?, https://arxiv.org/abs/2508.13932
  • Marco Spinaci, Marek Polewczyk, Maximilian Schambach, Sam Thelin, 19 Aug 2025, ConTextTab: A Semantics-Aware Tabular In-Context Learner, https://arxiv.org/abs/2506.10707
  • Anirudh Sundar, Christopher Richardson, Adar Avsian, Larry Heck, 19 Aug 2025, iTBLS: A Dataset of Interactive Conversations Over Tabular Information, https://arxiv.org/abs/2404.12580
  • Pablo G. Almeida, Guilherme A. L. Silva, Val\'eria Santos, Gladston Moreira, Pedro Silva and Eduardo Luz, 9 Aug 2025, Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students, https://arxiv.org/abs/2508.14057
  • Vishnou Vinayagame, Gregory Senay, and Luis Mart\'i, 20 Aug 2025, MATATA: Weakly Supervised End-to-End MAthematical Tool-Augmented Reasoning for Tabular Applications, https://arxiv.org/abs/2411.18915
  • Weijie Niu, Alberto Huertas Celdran, Karoline Siarsky, Burkhard Stiller, 22 Aug 2025, FEST: A Unified Framework for Evaluating Synthetic Tabular Data, https://arxiv.org/abs/2508.16254
  • Manar D. Samad, Kazi Fuad B. Akhter, Shourav B. Rabbani, Ibna Kowsar, 22 Aug 2025, Imputation Not Required in Incremental Learning of Tabular Data with Missing Values, https://arxiv.org/abs/2504.14610
  • Nikolaos Pavlidis, Vasilis Perifanis, Symeon Symeonidis, Pavlos S. Efraimidis, 24 Aug 2025, Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets, https://arxiv.org/abs/2508.17391
  • Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme, 23 Aug 2025, TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression, https://arxiv.org/abs/2508.17056
  • Yilang Ding, Jiawen Ren, Jiaying Lu, Gloria Hyunjung Kwak, Armin Iraji, Alex Fedorov, 25 Aug 2025, Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model, https://arxiv.org/abs/2508.17649
  • Jiyoon Myung, Jihyeon Park, Joohyung Han, 25 Aug 2025, HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data, https://arxiv.org/abs/2508.18048
  • Harshit Dhankhar and Kshitij Mishra and Tejas Bodas, 25 Aug 2025, Tabular and Deep Reinforcement Learning for Gittins Index, https://arxiv.org/abs/2405.01157

Microsoft Excel

Use of Microsoft Excel with AI:

Copilot Apps

Research on "copilot" types of AI applications:

AI Operating System

An AI operating system, or AI OS, is the idea of building an entire system on AI components. This is a generalization beyond just an AI framework or AI platform.

Research on an AI OS:

Security Credential Management

Security credential management is an important part of productionizing AI apps. This includes both user login passwords and the security keys of commercial APIs.

Papers on security credentials:

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