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Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g.
A mechanical proof of the Turing completeness of pure LISP
Robert S Boyer and J Strother Moore · 1983
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Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
Jurgen Schmidhuber · 1987
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Existential Risks: analyzing human extinction scenarios and related hazards
N Bostrom · 2002
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan · 2002
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Gödel machines: self-referential universal problem solvers making provably optimal self-improvements
Jürgen Schmidhuber · 2003
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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Artificial Intelligence as a positive and negative factor in global risk
Eliezer Yudkowsky et al · 2008
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Abandoning objectives: Evolution through the search for novelty alone
Joel Lehman and Kenneth O Stanley · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Illuminating search spaces by mapping elites
Jean-Baptiste Mouret and Jeff Clune · 2015
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Why greatness cannot be planned: The myth of the objective
Kenneth O Stanley and Joel Lehman · 2015
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Quality and diversity optimization: A unifying modular framework
Antoine Cully and Yiannis Demiris · 2017
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RL^2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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On the measure of intelligence
François Chollet · 2019
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Jeff Clune · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Automated machine learning: methods, systems, challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren · 2019
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Nsga-net: neural architecture search using multi-objective genetic algorithm
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf · 2019
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Designing neural networks through neuroevolution
Kenneth O Stanley, Jeff Clune, Joel Lehman, and Risto Miikkulainen · 2019
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The bitter lesson, 2019
Richard S. Sutton · 2019
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Poet: open-ended coevolution of environments and their optimized solutions
Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley · 2019
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Open questions in creating safe open-ended AI: Tensions between control and creativity
Adrien Ecoffet, Jeff Clune, and Joel Lehman · 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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A diverse corpus for evaluating and developing english math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2020
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SourceFinder: Finding malware Source-Code from publicly available repositories in GitHub
Md Omar Faruk Rokon, Risul Islam, Ahmad Darki, Evangelos E Papalexakis, and Michalis Faloutsos · 2020
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Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeffrey Clune, and Kenneth Stanley · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 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 massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
Cited alongside, same era.
Accelerating multi-objective neural architecture search by random-weight evaluation
Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu, Jing Wang, and Miao Zhang · 2021
Cited alongside, same era.
Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
Cited alongside, same era.
Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
Machines of loving grace, October 2024
Dario Amodei · 2024
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Managing extreme ai risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, et al · 2024
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What is an agent?
Harrison Chase · 2024
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Trace is the next autodiff: Generative optimization with rich feedback, execution traces, and llms
Ching-An Cheng, Allen Nie, and Adith Swaminathan · 2024
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Chatbot arena: An open platform for evaluating llms by human preference, 2024
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Transfer dynamics in emergent evolutionary curricula
Aaron Dharna, Amy K Hoover, Julian Togelius, and Lisa B Soros · 2022
Cited alongside, same era.
Langchain: Build context-aware reasoning applications
LangChainAI · 2022
Cited alongside, same era.
Introducing chatgpt
OpenAI · 2022
Cited alongside, same era.
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
Cited alongside, same era.
STar: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 2022
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.
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Cited alongside, same era.
Maxence Faldor, Jenny Zhang, Antoine Cully, and Jeff Clune · 2024
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Promptbreeder: Self-referential self-improvement via prompt evolution, 2024
Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel · 2024
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Getting 50% sota on arc-agi with gpt-4
Ryan Greenblatt · 2024
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Thought Cloning: Learning to think while acting by imitating human thinking
Shengran Hu and Jeff Clune · 2024
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SWE-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan · 2024
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Dspy: Compiling declarative language model calls into state-of-the-art pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, Heather Miller, et al · 2024
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Lecture 11: Turing-completeness
Abrahim Ladha · 2024
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Autoflow: Automated workflow generation for large language model agents
Zelong Li, Shuyuan Xu, Kai Mei, Wenyue Hua, Balaji Rama, Om Raheja, Hao Wang, He Zhu, and Yongfeng Zhang · 2024
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Evolution of heuristics: Towards efficient automatic algorithm design using large language model
Fei Liu, Tong Xialiang, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, and Qingfu Zhang · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Issue 253
Andrew Ng · 2024
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Simple evals, 2023
OpenAI · 2024
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Gpt-4 technical report, 2024
OpenAI · 2024
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Scaling large-language-model-based multi-agent collaboration
Chen Qian, Zihao Xie, Yifei Wang, Wei Liu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2024
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Tool learning with large language models: A survey
Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-Rong Wen · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Artificial Intelligence: 10 Things You Should Know
Tim Rocktäschel · 2024
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Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
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The prompt report: A systematic survey of prompting techniques
Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, et al · 2024
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The rise of ai agent infrastructure
Jon Turow · 2024
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A survey on large language model based autonomous agents
Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al · 2024
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen · 2024
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Evoagent: Towards automatic multi-agent generation via evolutionary algorithms
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, and Deqing Yang · 2024
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Textgrad: Automatic” differentiation” via text
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou · 2024
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The shift from models to compound ai systems
Matei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, and Ali Ghodsi · 2024
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Self-taught optimizer (stop): Recursively self-improving code generation
Eric Zelikman, Eliana Lorch, Lester Mackey, and Adam Tauman Kalai · 2024
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Gptswarm: Language agents as optimizable graphs
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber · 2024
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