@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PMLR}
}

@article{johnson2019mimic,
  title={MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs},
  author={Johnson, Alistair EW and Pollard, Tom J and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng, Chih-ying and Peng, Yifan and Lu, Zhiyong and Mark, Roger G and Berkowitz, Seth J and Horng, Steven},
  journal={arXiv preprint arXiv:1901.07042},
  year={2019}
}


@inproceedings{irvin2019chexpert,
  title={Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
  author={Irvin, Jeremy and Rajpurkar, Pranav and Ko, Michael and Yu, Yifan and Ciurea-Ilcus, Silviana and Chute, Chris and Marklund, Henrik and Haghgoo, Behzad and Ball, Robyn and Shpanskaya, Katie and others},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={590--597},
  year={2019}
}

@article{yuan2023ramm,
  title={RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training},
  author={Yuan, Zheng and Jin, Qiao and Tan, Chuanqi and Zhao, Zhengyun and Yuan, Hongyi and Huang, Fei and Huang, Songfang},
  journal={arXiv preprint arXiv:2303.00534},
  year={2023}
}

@article{zhang2024biomedclip,
  title={A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs},
  author={Sheng Zhang and Yanbo Xu and Naoto Usuyama and Hanwen Xu and Jaspreet Bagga and Robert Tinn and Sam Preston and Rajesh Rao and Mu Wei and Naveen Valluri and Cliff Wong and Andrea Tupini and Yu Wang and Matt Mazzola and Swadheen Shukla and Lars Liden and Jianfeng Gao and Angela Crabtree and Brian Piening and Carlo Bifulco and Matthew P. Lungren and Tristan Naumann and Sheng Wang and Hoifung Poon},
  journal={NEJM AI},
  year={2024},
  volume={2},
  number={1},
  doi={10.1056/AIoa2400640},
  url={https://ai.nejm.org/doi/full/10.1056/AIoa2400640}
}


@article{wang2022medclip,
  title={Medclip: Contrastive learning from unpaired medical images and text},
  author={Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng},
  journal={arXiv preprint arXiv:2210.10163},
  year={2022}
}

@article{chen2023medblip,
  title={Medblip: Bootstrapping language-image pre-training from 3d medical images and texts},
  author={Chen, Qiuhui and Hu, Xinyue and Wang, Zirui and Hong, Yi},
  journal={arXiv preprint arXiv:2305.10799},
  year={2023}
}

@article{chen2023generative,
  title={Generative text-guided 3d vision-language pretraining for unified medical image segmentation},
  author={Chen, Yinda and Liu, Che and Huang, Wei and Cheng, Sibo and Arcucci, Rossella and Xiong, Zhiwei},
  journal={arXiv preprint arXiv:2306.04811},
  year={2023}
}

@article{wu2023towards,
  title={Towards generalist foundation model for radiology},
  author={Wu, Chaoyi and Zhang, Xiaoman and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  journal={arXiv preprint arXiv:2308.02463},
  year={2023}
}

@article{lei2023unibrain,
  title={Unibrain: Universal brain mri diagnosis with hierarchical knowledge-enhanced pre-training},
  author={Lei, Jiayu and Dai, Lisong and Jiang, Haoyun and Wu, Chaoyi and Zhang, Xiaoman and Zhang, Yao and Yao, Jiangchao and Xie, Weidi and Zhang, Yanyong and Li, Yuehua and others},
  journal={arXiv preprint arXiv:2309.06828},
  year={2023}
}

@article{liu2023t3d,
  title={T3D: Towards 3D Medical Image Understanding through Vision-Language Pre-training},
  author={Liu, Che and Ouyang, Cheng and Chen, Yinda and Quilodr{\'a}n-Casas, Cesar C{\'e}sar and Ma, Lei and Fu, Jie and Guo, Yike and Shah, Anand and Bai, Wenjia and Arcucci, Rossella},
  journal={arXiv preprint arXiv:2312.01529},
  year={2023}
}

@article{chen2024med3dinsight,
  title={Med3DInsight: Enhancing 3D Medical Image Understanding with 2D Multi-Modal Large Language Models},
  author={Chen, Qiuhui and Ye, Huping and Hong, Yi},
  journal={arXiv preprint arXiv:2403.05141},
  year={2024}
}

@inproceedings{lin2023pmc,
  title={Pmc-clip: Contrastive language-image pre-training using biomedical documents},
  author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  booktitle={MICCAI},
  year={2023}
}

@article{jiang2023mistral,
  title={Mistral 7B},
  author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},
  journal={arXiv preprint arXiv:2310.06825},
  year={2023}
}

@article{wasserthal2023totalsegmentator,
  title={Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images},
  author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={5},
  year={2023},
  publisher={Radiological Society of North America}
}


@inproceedings{thomas_etal_2024,
  author    = {Thomas, Sarina and Cao, Qing and Kulikova, Daria and Novikova, Ana and Ben-Yosef, Guy},
  title     = {Generating Natural Text Explanations for Ejection Fraction Predictions},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  year      = {2024}
}




@inproceedings{martella2023diagnostic,
  title={Diagnostic Technology: Trends of Use and Availability in a 10-Year Period (2011--2020) among Sixteen OECD Countries},
  author={Martella, Manuela and Lenzi, Jacopo and Gianino, Maria Michela},
  booktitle={Healthcare},
  volume={11},
  number={14},
  pages={2078},
  year={2023},
  organization={MDPI}
}

@misc{oecd_health_utilisation,
  title = {Healthcare Utilisation},
  howpublished = {\url{https://data-explorer.oecd.org/vis?fs[0]=Topic%2C1%7CHealth%23HEA%23%7CHealthcare%20utilisation%23HEA_PRC%23&pg=0&fc=Topic&bp=true&snb=10&vw=tb&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_HEALTH_PROC%40DF_DIAG_EXAM&df[ag]=OECD.ELS.HD&df[vs]=1.0&pd=2015%2C&dq=..EXM...............&ly[rw]=REF_AREA%2CPROVIDER&ly[cl]=TIME_PERIOD%2CHEALTH_FACILITY&to[TIME_PERIOD]=false}},
  note = {Accessed: 2024-06-03}
}

@article{shin2021neural,
  title={Neural bootstrapper},
  author={Shin, Minsuk and Cho, Hyungjoo and Min, Hyun-seok and Lim, Sungbin},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={16596--16609},
  year={2021}
}


@article{michelucci2021estimating,
  title={Estimating neural network’s performance with bootstrap: A tutorial},
  author={Michelucci, Umberto and Venturini, Francesca},
  journal={Machine Learning and Knowledge Extraction},
  volume={3},
  number={2},
  pages={357--373},
  year={2021},
  publisher={MDPI}
}

@article{bubeck2023sparks,
  title={Sparks of artificial general intelligence: Early experiments with gpt-4},
  author={Bubeck, S{\'e}bastien and Chandrasekaran, Varun and Eldan, Ronen and Gehrke, Johannes and Horvitz, Eric and Kamar, Ece and Lee, Peter and Lee, Yin Tat and Li, Yuanzhi and Lundberg, Scott and others},
  journal={arXiv preprint arXiv:2303.12712},
  year={2023}
}

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={Medical image computing and computer-assisted intervention--MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@inproceedings{wang2023self,
  title={Self-Instruct: Aligning Language Models with Self-Generated Instructions},
  author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A and Khashabi, Daniel and Hajishirzi, Hannaneh},
  booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={13484--13508},
  year={2023}
}

@article{liu2023visual,
  title={Visual instruction tuning},
  author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
  journal={Advances in neural information processing systems},
  volume={36},
  year={2023}
}

@article{liu2023improved,
  title={Improved baselines with visual instruction tuning},
  author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
  journal={arXiv preprint arXiv:2310.03744},
  year={2023}
}


@article{li2023llavamed,
  title={Llava-med: Training a large language-and-vision assistant for biomedicine in one day},
  author={Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2023}
}

@article{chambon2023automated,
  title={Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods},
  author={Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P},
  journal={Journal of the American Medical Informatics Association},
  volume={30},
  number={2},
  pages={318--328},
  year={2023},
  publisher={Oxford University Press}
}

@article{10.1093/jamia/ocac219,
    author = {Chambon, Pierre J and Wu, Christopher and Steinkamp, Jackson M and Adleberg, Jason and Cook, Tessa S and Langlotz, Curtis P},
    title = "{Automated deidentification of radiology reports combining transformer and “hide in plain sight” rule-based methods}",
    journal = {Journal of the American Medical Informatics Association},
    year = {2022},
    month = {11},
    abstract = "{To develop an automated deidentification pipeline for radiology reports that detect protected health information (PHI) entities and replaces them with realistic surrogates “hiding in plain sight.”In this retrospective study, 999 chest X-ray and CT reports collected between November 2019 and November 2020 were annotated for PHI at the token level and combined with 3001 X-rays and 2193 medical notes previously labeled, forming a large multi-institutional and cross-domain dataset of 6193 documents. Two radiology test sets, from a known and a new institution, as well as i2b2 2006 and 2014 test sets, served as an evaluation set to estimate model performance and to compare it with previously released deidentification tools. Several PHI detection models were developed based on different training datasets, fine-tuning approaches and data augmentation techniques, and a synthetic PHI generation algorithm. These models were compared using metrics such as precision, recall and F1 score, as well as paired samples Wilcoxon tests.Our best PHI detection model achieves 97.9 F1 score on radiology reports from a known institution, 99.6 from a new institution, 99.5 on i2b2 2006, and 98.9 on i2b2 2014. On reports from a known institution, it achieves 99.1 recall of detecting the core of each PHI span.Our model outperforms all deidentifiers it was compared to on all test sets as well as human labelers on i2b2 2014 data. It enables accurate and automatic deidentification of radiology reports.A transformer-based deidentification pipeline can achieve state-of-the-art performance for deidentifying radiology reports and other medical documents.}",
    issn = {1527-974X},
    doi = {10.1093/jamia/ocac219},
    url = {https://doi.org/10.1093/jamia/ocac219},
    note = {ocac219},
    eprint = {https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocac219/47220191/ocac219.pdf},
}


@misc{darpa_ccu,
  author = {{Defense Advanced Research Projects Agency (DARPA)}},
  title = {Computational Cultural Understanding (CCU)},
  howpublished = {\url{https://www.darpa.mil/program/computational-cultural-understanding}},
  year = {2022},
  note = {Accessed: 2024-06-03}
}

@misc{darpa_ecole,
  author = {{Defense Advanced Research Projects Agency (DARPA)}},
  title = {Environment-Driven Conceptual Learning (ECOLE)},
  howpublished = {\url{https://www.darpa.mil/program/environment-driven-conceptual-learning}},
  year = {2023},
  note = {Accessed: 2024-06-03}
}

@inproceedings{yuksekgonul2022and,
  title={When and why vision-language models behave like bags-of-words, and what to do about it?},
  author={Yuksekgonul, Mert and Bianchi, Federico and Kalluri, Pratyusha and Jurafsky, Dan and Zou, James},
  booktitle={The Eleventh International Conference on Learning Representations},
  year={2022}
}


@article{blankemeier2024merlin,
  title={Merlin: A Vision Language Foundation Model for 3D Computed Tomography},
  author={Blankemeier, Louis and Cohen, Joseph Paul and Kumar, Ashwin and Van Veen, Dave and Gardezi, Syed Jamal Safdar and Paschali, Magdalini and Chen, Zhihong and Delbrouck, Jean-Benoit and Reis, Eduardo and Truyts, Cesar and others},
  journal={arXiv preprint arXiv:2406.06512},
  year={2024}
}



##### 

@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@software{Chase_LangChain_2022,
author = {Chase, Harrison},
month = oct,
title = {{LangChain}},
url = {https://github.com/langchain-ai/langchain},
year = {2022}
}

@article{bilic2019liver,
  title={The liver tumor segmentation benchmark (LiTS)},
  author={Bilic, Patrick and Christ, Patrick F and Vorontsov, Eugene and Chlebus, Grzegorz and Chen, Hao and Dou, Qi and Fu, Chi-Wing and Han, Xiaoshuai and Heng, Pheng-Ann and Hesser, J{\"u}rgen and others},
  journal={Medical Image Analysis},
  volume={62},
  pages={101561},
  year={2020},
  publisher={Elsevier}
}

@article{kits23,
  title={The 2023 Kidney Tumor Segmentation Challenge},
  author={Heller, Nicholas and Isensee, Fabian and Maier-Hein, Klaus H and Hou, Xiaoshuai and Xie, Yuyin and Li, Fengyi and Nan, Yang and Mu, Gang and Han, Miaomiao and Wang, Ziyue and others},
  journal={arXiv preprint arXiv:2304.12345},
  year={2023}
}

@inproceedings{suris2023vipergpt,
  title={Vipergpt: Visual inference via python execution for reasoning},
  author={Sur{\'\i}s, D{\'\i}dac and Menon, Sachit and Vondrick, Carl},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={11888--11898},
  year={2023}
}

@inproceedings{gupta2023visual,
  title={Visual programming: Compositional visual reasoning without training},
  author={Gupta, Tanmay and Kembhavi, Aniruddha},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14953--14962},
  year={2023}
}

@inproceedings{khan2024self,
  title={Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement},
  author={Khan, Zaid and BG, Vijay Kumar and Schulter, Samuel and Fu, Yun and Chandraker, Manmohan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14344--14353},
  year={2024}
}

@inproceedings{ke2024hydra,
  title={HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning},
  author={Ke, Fucai and Cai, Zhixi and Jahangard, Simindokht and Wang, Weiqing and Haghighi, Pari Delir and Rezatofighi, Hamid},
  booktitle={European Conference on Computer Vision},
  pages={132--149},
  year={2024},
  organization={Springer}
}

@inproceedings{min2024morevqa,
  title={MoReVQA: Exploring Modular Reasoning Models for Video Question Answering},
  author={Min, Juhong and Buch, Shyamal and Nagrani, Arsha and Cho, Minsu and Schmid, Cordelia},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={13235--13245},
  year={2024}
}

@article{hamamci2024developing,
  title={Developing Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography},
  author={Hamamci, Ibrahim Ethem and Er, Sezgin and Almas, Furkan and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and Dogan, Irem and Dasdelen, Muhammed Furkan and Durugol, Omer Faruk and Wittmann, Bastian and Amiranashvili, Tamaz and others},
  journal={arXiv preprint arXiv:2403.17834},
  year={2024}
}

@inproceedings{chen2024bimcv,
  title={Bimcv-r: A landmark dataset for 3d ct text-image retrieval},
  author={Chen, Yinda and Liu, Che and Liu, Xiaoyu and Arcucci, Rossella and Xiong, Zhiwei},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={124--134},
  year={2024},
  organization={Springer}
}

@inproceedings{luo2024rethinking,
  title={Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases},
  author={Luo, Xiangde and Li, Zihan and Zhang, Shaoting and Liao, Wenjun and Wang, Guotai},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={531--541},
  year={2024},
  organization={Springer}
}

@inproceedings{blankemeier2022opportunistic,
  title={Opportunistic incidence prediction of multiple chronic diseases from abdominal CT imaging using multi-task learning},
  author={Blankemeier, Louis and Gallegos, Isabel and Zambrano Chaves, Juan Manuel and Maron, David and Sandhu, Alexander and Rodriguez, Fatima and Rubin, Daniel and Patel, Bhavik and Willis, Marc and Boutin, Robert and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={309--318},
  year={2022},
  organization={Springer}
}


# IF anomaly detection:
@inproceedings{almeida2023coopd,
  title={cOOpD: reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations},
  author={Almeida, Silvia D and L{\"u}th, Carsten T and Norajitra, Tobias and Wald, Tassilo and Nolden, Marco and J{\"a}ger, Paul F and Heussel, Claus P and Biederer, J{\"u}rgen and Weinheimer, Oliver and Maier-Hein, Klaus H},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={33--43},
  year={2023},
  organization={Springer}
}

@inproceedings{zhang2023model,
  title={A Model-Agnostic Framework for Universal Anomaly Detection of Multi-organ and Multi-modal Images},
  author={Zhang, Yinghao and Lu, Donghuan and Ning, Munan and Wang, Liansheng and Wei, Dong and Zheng, Yefeng},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={232--241},
  year={2023},
  organization={Springer}
}

@inproceedings{akcay2019ganomaly,
  title={Ganomaly: Semi-supervised anomaly detection via adversarial training},
  author={Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P},
  booktitle={Computer Vision--ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2--6, 2018, Revised Selected Papers, Part III 14},
  pages={622--637},
  year={2019},
  organization={Springer}
}

@article{schlegl2019f,
  title={f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks},
  author={Schlegl, Thomas and Seeb{\"o}ck, Philipp and Waldstein, Sebastian M and Langs, Georg and Schmidt-Erfurth, Ursula},
  journal={Medical image analysis},
  volume={54},
  pages={30--44},
  year={2019},
  publisher={Elsevier}
}

@article{shvetsova2021anomaly,
  title={Anomaly detection in medical imaging with deep perceptual autoencoders},
  author={Shvetsova, Nina and Bakker, Bart and Fedulova, Irina and Schulz, Heinrich and Dylov, Dmitry V},
  journal={IEEE Access},
  volume={9},
  pages={118571--118583},
  year={2021},
  publisher={IEEE}
}

@article{berland2010managing,
  title={Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee},
  author={Berland, Lincoln L and Silverman, Stuart G and Gore, Richard M and Mayo-Smith, William W and Megibow, Alec J and Yee, Judy and Brink, James A and Baker, Mark E and Federle, Michael P and Foley, W Dennis and others},
  journal={Journal of the American College of Radiology},
  volume={7},
  number={10},
  pages={754--773},
  year={2010},
  publisher={Elsevier}
}

@inproceedings{lyu2024superpixel,
  title={Superpixel-Guided Segment Anything Model for Liver Tumor Segmentation with Couinaud Segment Prompt},
  author={Lyu, Fei and Xu, Jingwen and Zhu, Ye and Wong, Grace Lai-Hung and Yuen, Pong C},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={678--688},
  year={2024},
  organization={Springer}
}


@article{bilic2023liver,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick and Li, Hongwei Bran and Vorontsov, Eugene and Ben-Cohen, Avi and Kaissis, Georgios and Szeskin, Adi and Jacobs, Colin and Mamani, Gabriel Efrain Humpire and Chartrand, Gabriel and others},
  journal={Medical Image Analysis},
  volume={84},
  pages={102680},
  year={2023},
  publisher={Elsevier}
}
@article{heller2023kits21,
  title={The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct},
  author={Heller, Nicholas and Isensee, Fabian and Trofimova, Dasha and Tejpaul, Resha and Zhao, Zhongchen and Chen, Huai and Wang, Lisheng and Golts, Alex and Khapun, Daniel and Shats, Daniel and others},
  journal={arXiv preprint arXiv:2307.01984},
  year={2023}
}


%% visual programs in computer vision
@inproceedings{Subramanian2023ModularVQ,
  title={Modular Visual Question Answering via Code Generation},
  author={Sanjay Subramanian and Medhini Narasimhan and Kushal Khangaonkar and Kevin Yang and Arsha Nagrani and Cordelia Schmid and Andy Zeng and Trevor Darrell and Dan Klein},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:259108774}
}




@misc{Yao2022ReActSR,
      title={ReAct: Synergizing Reasoning and Acting in Language Models}, 
      author={Shunyu Yao and Jeffrey Zhao and Dian Yu and Nan Du and Izhak Shafran and Karthik Narasimhan and Yuan Cao},
      year={2023},
      eprint={2210.03629},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2210.03629}, 
}


@article{yang2023mm,
  title={Mm-react: Prompting chatgpt for multimodal reasoning and action},
  author={Yang, Zhengyuan and Li, Linjie and Wang, Jianfeng and Lin, Kevin and Azarnasab, Ehsan and Ahmed, Faisal and Liu, Zicheng and Liu, Ce and Zeng, Michael and Wang, Lijuan},
  journal={arXiv preprint arXiv:2303.11381},
  year={2023}
}

@misc{anthropic2024claude,
  author       = {Anthropic},
  title        = {Claude 3.5 Sonnet},
  year         = {2024},
  url          = {https://www.anthropic.com/claude/sonnet},
}

@misc{openai2023gpt4o,
  author       = {OpenAI},
  title        = {GPT-4o},
  year         = {2023},
  url          = {https://www.openai.com/gpt-4o},
}khan2024self