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In the era of large language models (LLMs), high-quality, domain-rich, and continuously evolving datasets capturing expert-level knowledge, core human values, and reasoning are increasingly valuable.
Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 1908
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart Russell, et al · 2000
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Openreview: A venue for open peer review, open publishing, open access
Dagstuhl Publishing Soergel · 2013
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Igor Lopyrev, and Percy Liang · 2016
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Cooperative inverse reinforcement learning
Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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A dataset of peer reviews (peerread): Collection, insights and nlp applications
Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine Van Zuylen, Sebastian Kohlmeier, Eduard Hovy, and Roy Schwartz · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The peer-review crisis: a commentary
Serge P. Horbach · 2019
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Eli5: Long form question answering
Angela Fan, Yacine Jernite, Jason Weston, and David Grangier · 2019
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Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, and Paul F. Christiano · 2019
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Persuasion for good: Towards a personalized persuasive dialogue system for social good
Xinyao Wang, Han Zhang, Noah A. Smith, and Yejin Choi · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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DebateSum: A large-scale argument mining and summarization dataset
Allen Roush and Arvind Balaji · 2020
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Human-centered artificial intelligence: Reliable, safe & trustworthy
Ben Shneiderman · 2020
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Noise: A flaw in human judgment
Daniel Kahneman, Olivier Sibony, and Cass R Sunstein · 2021
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Deep learning–based text classification: a comprehensive review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao · 2021
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https://iclr.cc/media/Press/ICLR_2021_Fact_Sheet.pdf , 2021
Iclr 2021 fact sheet · 2021
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Asap: A chinese review dataset towards aspect category sentiment analysis and rating prediction
Jiahao Bu, Lei Ren, Shuang Zheng, Yang Yang, Jingang Wang, Fuzheng Zhang, and Wei Wu · 2021
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Disapere: A dataset for discourse structure in peer review discussions
Neha Kennard, Tim O’Gorman, Rajarshi Das, Akshay Sharma, Chhandak Bagchi, Matthew Clinton, Pranay Kumar Yelugam, Hamed Zamani, and Andrew McCallum · 2021
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Inverse decision modeling: Learning interpretable representations of behavior
Daniel Jarrett, Alihan Hüyük, and Mihaela Van Der Schaar · 2021
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Least-squares calibration for peer reviews
Chen Xu, Yao Li, Ivan Stelmakh, and Nihar B Shah · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, and et al · 2021
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The neurips 2021 consistency experiment
Alina Beygelzimer, Yann Dauphin, Percy Liang, and Jennifer Wortman Vaughan · 2021
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PaLM: Scaling language modeling with pathways, 2022
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Galactica: A large language model for science, 2022
Ross Taylor, Bingqing Wen Kardas, Sid McLean, Gabriel Brown, David Agarwal, Mo outlandish Bogin, Eric Michaels, Eric Hillestad, Hongyu Jiang, Danica Dang, et al · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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https://iclr.cc/media/Press/ICLR_2022_Fact_Sheet.pdf , 2022
Iclr 2022 fact sheet · 2022
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Argscichat: A dataset for argumentative dialogues on scientific papers
Federico Ruggeri, Mohsen Mesgar, and Iryna Gurevych · 2022
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Asap-review: Towards automating scientific paper review with argument-aware summary generation
Shuyang Yuan, Lu Wang, and Noah A. Smith · 2022
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Active example selection for in-context learning
Yiming Zhang, Shi Feng, and Chenhao Tan · 2022
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Navigating data-centric artificial intelligence with dc-check: Advances, challenges, and opportunities
Nabeel Seedat, Fergus Imrie, and Mihaela van der Schaar · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al · 2023
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Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al · 2023
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2023
Cited alongside, same era.
Openchat: Advancing open-source language models with mixed-quality data
Guan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li, Sen Song, and Yang Liu · 2023
Cited alongside, same era.
Embracing large language models for medical applications: opportunities and challenges
Mert Karabacak and Konstantinos Margetis · 2023
Cited alongside, same era.
Elsevier takes Scopus to the next level with generative AI
Elsevier · 2023
Cited alongside, same era.
Chatgpt: five priorities for research
Eva AM Van Dis, Johan Bollen, Willem Zuidema, Robert Van Rooij, and Claudi L Bockting · 2023
Cited alongside, same era.
By chatgpt fool scientists
Holly Else · 2023
Orsum: A dataset for paper meta-review generation via scientific opinion summarization
Yutong Zeng et al · 2024
Later among the works it cites.
Moprd: Multidisciplinary open peer review dataset
Yibo Lin, Wenhao Wang, et al · 2024
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Large language models revolutionized ai. llm agents are what’s next
Maya Murad · 2024
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Request for proposals: Benchmarking llm agents on consequential real-world tasks
Open Philanthropy · 2024
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Grok 3 beta – the age of reasoning agents
xAI · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee · 2023
Cited alongside, same era.
Reviewergpt? an exploratory study on using large language models for paper reviewing
Ryan Liu and Nihar B Shah · 2023
Cited alongside, same era.
Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
Cited alongside, same era.
Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2023
Cited alongside, same era.
https://media.iclr.cc/Conferences/ICLR2023/ICLR2023-Fact_Sheet.pdf , 2023
Iclr 2023 fact sheet · 2023
Cited alongside, same era.
Openassistant conversations-democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri Von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, et al · 2023
Cited alongside, same era.
Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian Moghaddam, and Neel Sundaresan · 2024
Later among the works it cites.
Chatdev: Communicative agents for software development
Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, et al · 2024
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Inverse-rlignment: Inverse reinforcement learning from demonstrations for llm alignment
Hao Sun and Mihaela van der Schaar · 2024
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Generalized preference optimization: A unified approach to offline alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, and Bilal Piot · 2024
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Dense reward for free in reinforcement learning from human feedback
Alex J Chan, Hao Sun, Samuel Holt, and Mihaela van der Schaar · 2024
Later among the works it cites.
Kto: Model alignment as prospect theoretic optimization
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Reasoning with large language models, a survey
Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, and Thomas Back · 2024
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Beyond accuracy: Evaluating the reasoning behavior of large language models – a survey
Philipp Mondorf and Barbara Plank · 2024
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OpenDebateEvidence: A massive-scale argument mining and summarization dataset
Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Mehdi Fatemi, and Ravid Shwartz-Ziv · 2024
Later among the works it cites.
Shuai Ma, Qiaoyi Chen, Xinru Wang, Chengbo Zheng, Zhenhui Peng, Ming Yin, and Xiaojuan Ma · 2024
Later among the works it cites.
MARG: Multi-agent review generation for scientific papers
Mike D’Arcy, Tom Hope, Larry Birnbaum, and Doug Downey · 2024
Later among the works it cites.
Human-in-the-loop AI reviewing: Feasibility, opportunities, and risks
Iddo Drori and Dov Te’eni · 2024
Later among the works it cites.
Data-centric artificial intelligence: A survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu · 2025
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Introducing deep research, 2025
OpenAI · 2025
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Welcome to the era of experience
David Silver and Richard S Sutton · 2025
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On the biology of a large language model
Jack Lindsey, Wes Gurnee, Emmanuel Ameisen, Brian Chen, Adam Pearce, Nicholas L. Turner, Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson, Sam Zimmerman, Kelley Rivoire, Thomas Conerly, Chris Olah, and Joshua Batson · 2025
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Friend or foe? exploring the implications of large language models on the science system
Benedikt Fecher, Marcel Hebing, Melissa Laufer, Jörg Pohle, and Fabian Sofsky · 2025
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DOGE: Reforming AI Conferences and Towards a Future Civilization of Fairness and Justice
Zeyuan Allen-Zhu and Xiaoli Xu · 2025
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Can llm feedback enhance review quality? a randomized study of 20k reviews at iclr 2025
Nitya Thakkar, Mert Yuksekgonul, Jake Silberg, Animesh Garg, Nanyun Peng, Fei Sha, Rose Yu, Carl Vondrick, and James Zou · 2025
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Gemini 2.5: Our most intelligent ai model
Google DeepMind · 2025
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Gpt-4.5 system card
OpenAI · 2025
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Grok 3 beta — the age of reasoning agents
xAI · 2025
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Author correction: Ai models collapse when trained on recursively generated data
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal · 2025
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Writing as a testbed for open ended agents
Sian Gooding, Lucia Lopez-Rivilla, and Edward Grefenstette · 2025
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1.4 million open-source distilled reasoning dataset to empower large language model training
Han Zhao, Haotian Wang, Yiping Peng, Sitong Zhao, Xiaoyu Tian, Shuaiting Chen, Yunjie Ji, and Xiangang Li · 2025
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Synthetic-1: Two million collaboratively generated reasoning traces from deepseek-r1, 2025
Justus Mattern, Sami Jaghouar, Manveer Basra, Jannik Straube, Matthew Di Ferrante, Felix Gabriel, Jack Min Ong, Vincent Weisser, and Johannes Hagemann · 2025
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https://media.iclr.cc/Conferences/ICLR2025/ICLR2025_Fact_Sheet.pdf , 2025
Iclr 2025 fact sheet · 2025
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Jing Yang · 2025
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Leveraging llm feedback to enhance review quality at iclr 2025
James Zou and Omkar Thakkar · 2025
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Aaai launches ai-powered peer review assessment system
AAAI · 2025
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Ai is transforming peer review — and many scientists are worried
Miryam Naddaf · 2025
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Detecting llm-written peer reviews
Sudarshan Rao, Lexing Xie, and Sameer Singh · 2025
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Agentic large language models: A survey
Yefei Liu, Prithviraj Ammanabrolu, David Demeter, et al · 2025
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Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam, Jessica C. Jones, Sungjoon Kim, Adnan Mohammad, Chi Thang Nguyen, and Bratin Sengupta · 2025
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An approach to technical agi safety and security
Rohin Shah, Alex Irpan, Alexander Matt Turner, Anna Wang, Arthur Conmy, David Lindner, Jonah Brown-Cohen, Lewis Ho, Neel Nanda, Raluca Ada Popa, et al · 2025
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Reviving the classics: Active reward modeling in large language model alignment
Yunyi Shen, Hao Sun, and Jean-François Ton · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Mr-gsm8k: A meta-reasoning benchmark for large language model evaluation
Zhongshen Zeng, Pengguang Chen, Shu Liu, Haiyun Jiang, and Jiaya Jia · 2025
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Towards large reasoning models: A survey of reinforced reasoning with large language models
Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, et al · 2025
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Reinforcement learning for reasoning in large language models with one training example
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Rethinking diverse human preference learning through principal component analysis
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Reasoning models don’t always say what they think
Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, et al · 2025
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