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Language-based agentic systems have shown great promise in recent years, transitioning from solving small-scale research problems to being deployed in challenging real-world tasks.
The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
Seppo Linnainmaa · 1970
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Taylor expansion of the accumulated rounding error
Seppo Linnainmaa · 1976
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Applications of advances in nonlinear sensitivity analysis
Paul Werbos · 1982
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Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments
J. Schmidhuber · 1990
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Learning to control fast-weight memories: An alternative to recurrent nets
Jürgen Schmidhuber · 1992
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Cauchy and the gradient method
Claude Lemaréchal · 2012
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Semantic backpropagation for designing search operators in genetic programming
Tomasz P Pawlak, Bartosz Wieloch, and Krzysztof Krawiec · 2014
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Jürgen Schmidhuber · 2015
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First-order methods in optimization
Amir Beck · 2017
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Attention is all you need
A Vaswani · 2017
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“liar, liar pants on fire”: A new benchmark dataset for fake news detection
William Yang Wang · 2017
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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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Linear transformers are secretly fast weight programmers
Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber · 2021
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Gptscore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu · 2023
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Camel: Communicative agents for” mind” exploration of large language model society
Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem · 2023
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Automatic prompt optimization with “gradient descent” and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, et al · 2023
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From llms to llm-based agents for software engineering: A survey of current, challenges and future
Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, and Huaming Chen · 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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Prompt engineering
OpenAI · 2024
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Gpt-pilot: Open-source ai agent for software development
Pythagora-io · 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 are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
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Mindstorms in natural language-based societies of mind
Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, et al · 2023
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Grad-sum: Leveraging gradient summarization for optimal prompt engineering
Derek Austin and Elliott Chartock · 2024
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Mllm-as-a-judge: Assessing multimodal llm-as-a-judge with vision-language benchmark
Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Pan Zhou, Yao Wan, and Lichao Sun · 2024
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Trace is the new autodiff–unlocking efficient optimization of computational workflows
Ching-An Cheng, Allen Nie, and Adith Swaminathan · 2024
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Large language model based multi-agents: A survey of progress and challenges
T Guo, X Chen, Y Wang, R Chang, S Pei, NV Chawla, O Wiest, and X Zhang · 2024
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Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou · 2024
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A survey on the memory mechanism of large language model based agents
Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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Symbolic learning enables self-evolving agents
Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, et al · 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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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al · 2024
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