Fetching the paper…
Reading the bibliography…
Building a general-purpose agent is a long-standing vision in the field of artificial intelligence.
Role of the cerebellum in the visual guidance of movement
JF Stein · 1986
Earlier work this paper cites.
Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli · 2017
Earlier work this paper cites.
The amyloid hypothesis on trial
Simon Makin · 2018
Earlier work this paper cites.
Minerl: A large-scale dataset of minecraft demonstrations
William H Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov · 2019
Earlier work this paper cites.
Scientific progress is built on failure
Eileen Parkes · 2019
Earlier work this paper cites.
Multi-adversarial discriminative deep domain generalization for face presentation attack detection
Rui Shao, Xiangyuan Lan, Jiawei Li, and Pong C Yuen · 2019
Earlier work this paper cites.
Comprehensive integration of single-cell data
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, and Rahul Satija · 2019
Earlier work this paper cites.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov · 2021
Earlier work this paper cites.
Fast model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Earlier work this paper cites.
Varying demands for cognitive control reveals shared neural processes supporting semantic and episodic memory retrieval
Deniz Vatansever, Jonathan Smallwood, and Elizabeth Jefferies · 2021
Earlier work this paper cites.
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
Earlier work this paper cites.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
Earlier work this paper cites.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
Earlier work this paper cites.
Vima: General robot manipulation with multimodal prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan · 2022
Earlier work this paper cites.
Emocaps: Emotion capsule based model for conversational emotion recognition
Zaijing Li, Fengxiao Tang, Ming Zhao, and Yusen Zhu · 2022
Cited alongside, same era.
Causal mapping of human brain function
Shan H Siddiqi, Konrad P Kording, Josef Parvizi, and Michael D Fox · 2022
Cited alongside, same era.
Generating executable action plans with environmentally-aware language models
Maitrey Gramopadhye and Daniel Szafir · 2023
Cited alongside, same era.
A real-world webagent with planning, long context understanding, and program synthesis
Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust · 2023
Cited alongside, same era.
How long can context length of open-source llms truly promise?
Dacheng Li, Rulin Shao, Anze Xie, Ying Sheng, Lianmin Zheng, Joseph Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang · 2023
Cited alongside, same era.
Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, et al · 2024
Closest in time.
The development of human causal learning and reasoning
Mariel K Goddu and Alison Gopnik · 2024
Closest in time.
Ma-lmm: Memory-augmented large multimodal model for long-term video understanding
Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, and Ser-Nam Lim · 2024
Closest in time.
Deepseek-vl: towards real-world vision-language understanding
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Yaofeng Sun, et al · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Steve-1: A generative model for text-to-behavior in minecraft
Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, and Sheila McIlraith · 2023
Cited alongside, same era.
Giraffe: Adventures in expanding context lengths in llms
Arka Pal, Deep Karkhanis, Manley Roberts, Samuel Dooley, Arvind Sundararajan, and Siddartha Naidu · 2023
Cited alongside, same era.
Mp5: A multi-modal open-ended embodied system in minecraft via active perception
Yiran Qin, Enshen Zhou, Qichang Liu, Zhenfei Yin, Lu Sheng, Ruimao Zhang, Yu Qiao, and Jing Shao · 2023
Cited alongside, same era.
Detecting and grounding multi-modal media manipulation
Rui Shao, Tianxing Wu, and Ziwei Liu · 2023
Cited alongside, same era.
Generative multimodal models are in-context learners
Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Zhengxiong Luo, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, et al · 2023
Cited alongside, same era.
Appagent: Multimodal agents as smartphone users
Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu · 2023
Cited alongside, same era.
Skill reinforcement learning and planning for open-world long-horizon tasks
Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, and Zongqing Lu · 2023
Cited alongside, same era.
Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, et al · 2024
Closest in time.
Detecting and grounding multi-modal media manipulation and beyond
Rui Shao, Tianxing Wu, Jianlong Wu, Liqiang Nie, and Ziwei Liu · 2024
Closest in time.
Mome: Mixture of multimodal experts for generalist multimodal large language models
Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, and Liqiang Nie · 2024
Closest in time.
Towards general computer control: A multimodal agent for red dead redemption ii as a case study
Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, et al · 2024
Closest in time.
A survey on self-evolution of large language models
Zhengwei Tao, Ting-En Lin, Xiancai Chen, Hangyu Li, Yuchuan Wu, Yongbin Li, Zhi Jin, Fei Huang, Dacheng Tao, and Jingren Zhou · 2024
Closest in time.
Longvlm: Efficient long video understanding via large language models
Yuetian Weng, Mingfei Han, Haoyu He, Xiaojun Chang, and Bohan Zhuang · 2024
Closest in time.
Exploring diverse in-context configurations for image captioning
Xu Yang, Yongliang Wu, Mingzhuo Yang, Haokun Chen, and Xin Geng · 2024
Closest in time.
Cat: Enhancing multimodal large language model to answer questions in dynamic audio-visual scenarios
Qilang Ye, Zitong Yu, Rui Shao, Xinyu Xie, Philip Torr, and Xiaochun Cao · 2024
Closest in time.
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
Closest in time.
Minedreamer: Learning to follow instructions via chain-of-imagination for simulated-world control
Enshen Zhou, Yiran Qin, Zhenfei Yin, Yuzhou Huang, Ruimao Zhang, Lu Sheng, Yu Qiao, and Jing Shao · 2024
Closest in time.