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321 | Grace Lee et al. Biomni: A general-purpose biomedical ai agent for autonomous research. bioRxiv, 2025. | Recoverable | {
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323 | Jane Smith et al. Machine learning to predict de novo protein-protein interactions. Trends in Biotechnology, 43(5):321-335, 2025. | Recoverable | {
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324 | DeepMind. Gemma scope: Scaling mechanistic interpretability to chain of thought. DeepMind Safety Blog, 2025. URL https://deepmindsafetyresearch.medium.com/ evaluating-and-monitoring-for-ai-scheming-8a7f2ce087f9. Discusses scaling mechanistic interpretability techniques to chain-of-thought and applications such as hallu... | Unrecoverable | null | null |
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326 | Xinyu She et al. Reasoning models don't always say what they think. arXiv preprint arXiv:2505.05410, 2025. URL https://arxiv.org/abs/2505.05410. | Recoverable | {
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327 | Yilun Zhang, Mohit Bansal, Hao Cheng, et al. On the diagram of thought. arXiv preprint arXiv:2409.10038, 2024. URL https://arxiv.org/abs/2409.10038. | Recoverable | {
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328 | Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Graham Neubig, and Shixiang Shane Cao. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023. URL https://arxiv.org/abs/2305.10601. | Recoverable | {
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344 | Zhihang Jiang, Dingkang Wang, Yao Li, et al. Fp6-llm: Efficient llm serving through fp6-centric co-design. arXiv preprint arXiv:2401.14112, 2024. | Recoverable | {
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"title": "Clinicalbert: Modeling clinical notes and predicting hospital readmission",
"authors": [
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],
"venue": "arXiv preprint arXiv:1904.05342",
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368 | Alexandra C. Kiser, Kaitlyn Eilbeck, and Brandon T. Bucher. Developing an lstm model to identify surgical site infections using electronic healthcare records. AMIA Joint Summits on Translational Science Proceedings, 2023:330-339, 2023. | Recoverable | {
"title": "Developing an LSTM model to identify surgical site infections using electronic healthcare records",
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],
"venue": "AMIA Summits on Translational Science Proceedings",
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369 | Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Marzyeh Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. Mimic-iii, a freely accessible critical care database. Scientific Data, 3:160035, 2016. | Recoverable | {
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],
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370 | Anna Kowalski and David Thompson. Navigating ai regulations in healthcare: Ethical and legal perspectives. Health Policy and Technology, 14:101095, 2025. | Unrecoverable | null | null |
371 | Rohit Kumar, Arjun Singh, and Neha Gupta. Agent-based clinical simulations for multi-agent decision-making. Artificial Intelligence in Medicine, 112:102045, 2021. | Unrecoverable | null | null |
372 | Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, and Daniel et al. Khashabi. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Advances in Neural Information Processing Systems, volume 33, pp. 9459-9474, 2020. | Recoverable | {
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373 | Siddharth Nair, Anand Kumar, and Richa Gupta. Predictive modeling of patient outcomes through temporal reasoning. Journal of Biomedical Informatics, 109:103514, 2020. | Unrecoverable | null | null |
374 | Uzma Nawaz. A review of neuro-symbolic ai integrating reasoning and learning. Artificial Intelligence Review, 2025. | Recoverable | {
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} | Uzma Nawaz, Mufti Anees-ur-Rahaman, and Zubair Saeed. A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems. Intelligent Systems with Applications, 2025. |
375 | Stefano Montani and Riccardo Bellazzi. Artificial intelligence in clinical decision support: Challenges for evaluating ai and practical needs. Yearbook of Medical Informatics, 28:128-134, 2019. | Recoverable | {
"title": "Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications",
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376 | Thanh Nguyen and Xuan Ding. Cudoctor: A fuzzy logic-based medical chatbot for patient support. IEEE Journal of Biomedical and Health Informatics, 24(9):2498-2506, 2020. | Unrecoverable | null | null |
377 | Melissa Rotmensch, Yael Halpern, Ameet Tlimat, Steven Horng, and David Sontag. Learning a health knowledge graph from electronic medical records. Scientific Reports, 7(1):5994, 2017. | Recoverable | {
"title": "Learning a Health Knowledge Graph from Electronic Medical Records",
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"David Sontag"
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378 | Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Narges Hajaj, Moritz Hardt, and Peter et al. Liu. Scalable and accurate deep learning for electronic health records. npj Digital Medicine, 1: 18, 2018. | Recoverable | {
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379 | Jane Smith and Alan Lee. Managing clinical uncertainty: Practices and perspectives. Journal of Clinical Medicine, 9:2314, 2020. Placeholder; replace with a verified source if available. | Unrecoverable | null | null |
380 | Lei Wang, Min Zhao, and Wei Chen. Incorporating causal reasoning in medical temporal models. Artificial Intelligence in Medicine, 142:102803, 2023. | Unrecoverable | null | null |
381 | Sarah Williams, John Lee, and Hyun Park. Sam: Semantic annotation for medical temporal data. Journal of Biomedical Informatics, 130:103934, 2022. | Unrecoverable | null | null |
382 | Edward H. Shortliffe. Computer-based medical consultations: Mycin. American Scientist, 64(2): 173-181, 1976. | Recoverable | {
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383 | Guang Yang et al. Unbox the black-box for the medical explainable ai via multi-modal and multiinstance learning. Computers in Biology and Medicine, 143:105238, 2022. doi: 10.1016/j.comp biomed.2022.105238. | Recoverable | {
"title": "Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond",
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384 | Yifan Zhang, Ming Chen, and Hong Wang. Madip: Multi-agent decision infrastructure for healthcare. In Proceedings of AAAI, pp. 1220-1227, 2019. | Unrecoverable | null | null |
385 | Google. 3 new ways to navigate more sustainably with maps eco-friendly routing. https://blog.google/products/maps/ 3-new-ways-navigate-more-sustainably-maps/, 2021. Accessed: 2025-0921. | Recoverable | {
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"authors": [],
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386 | MIT Climate Group. A cautionary tale for deep learning in climate science. https://example. com, 2019. | Unrecoverable | null | null |
387 | C. Huntingford, J. A. Lowe, B. B. Booth, and C. D. Jones. Contributions of climate model uncertainties to future projections of lps (linear pattern scaling). Climatic Change, 2012. | Unrecoverable | null | null |
388 | Thomas Hauser, Corina Sandu, Brian Soden, et al. Artificial intelligence in climate science: Opportunities, challenges, and future prospects. Nature Climate Change, 12(5):371-381, 2022. | Unrecoverable | null | null |
389 | National Oceanic and Atmospheric Administration. Noaa global historical climatology network and climate data online. https://www.ncdc.noaa.gov/, 2023. Accessed: 2025-09-21. | Unrecoverable | null | null |
390 | T. Nguyen and Y. Zhao. Hybrid physics-informed deep learning for climate modeling. In Proceedings of I C L R, 2023. | Unrecoverable | null | null |
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392 | NOAA. Climate data online api documentation. https://www.ncdc.noaa.gov/cdo-web/ webservices/v2, 2017. | Recoverable | {
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393 | David Patterson, Rafael Gonzalez, Quoc Le, Cliff Liang, Julian Munguia, Daniel Rothchild, Jeffrey Dean, and Andrew Ng. Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350, 2021b. | Recoverable | {
"title": "Carbon Emissions and Large Neural Network Training",
"authors": [
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} | David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. Carbon Emissions and Large Neural Network Training. arXiv preprint arXiv:2104.10350, 2021. |
394 | J. Smith and T. Brown. Overfitting in climate prediction models: Risks and mitigation. In Proceedings of NeurIPS Workshop on Climate Informatics, 2021. | Unrecoverable | null | null |
395 | S. Selvaraj and H. Kaur. Ai in climate adaptation: Early warning systems for disasters. Environmental Modelling Software, 140:105005, 2021. | Unrecoverable | null | null |
396 | B. Ruddell and C. Monteleoni. Overfitting in climate-ai models: Challenges and risks. Climatic Informatics, 2020. | Unrecoverable | null | null |
397 | A. Williams and R. Gupta. Ai infrastructure and climate ethics. AI and Society, 36(3):421-432, 2021. | Unrecoverable | null | null |
398 | Roy Bar-Haim, Shachar Bhattacharya, Michal Jacovi, Yosi Mass, Matan Orbach, Eyal Sliwowicz, and Noam Slonim. Key point analysis via contrastive learning and extractive argument summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7953-7962, Online and Punta Can... | Recoverable | {
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399 | EPFL News. Ai's new power of persuasion: it can change your mind using only words, November 2023. URL https://actu.epfl.ch/news/ ai-s-new-power-of-persuasion-it-can-change-your-mi/. | Recoverable | {
"title": null,
"authors": [],
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"year": null,
"url": "https://actu.epfl.ch/news/ai-s-new-power-of-persuasion-it-can-change-your-mi/"
} | Tanya Petersen. “AI’s new power of persuasion: it can change your mind.” EPFL, April 15, 2024. URL https://actu.epfl.ch/news/ai-s-new-power-of-persuasion-it-can-change-your-mi/ |
400 | Yi Wang, Andrew Baker, Veronika Eyring, et al. Climatebench v2: A benchmark for data-driven climate projections. Geoscientific Model Development, 16(7):2105-2123, 2023b. | Unrecoverable | null | null |
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