no code implementations • 25 Jun 2025 • Yoshua Bengio, Tegan Maharaj, Luke Ong, Stuart Russell, Dawn Song, Max Tegmark, Lan Xue, Ya-Qin Zhang, Stephen Casper, Wan Sie Lee, Sören Mindermann, Vidhisha Balachandran, Fazl Barez, Michael Belinsky, Imane Bello, Malo Bourgon, Mark Brakel, Siméon Campos, Duncan Cass-Beggs, Jiahao Chen, Rumman Chowdhury, Kuan Chua Seah, Jeff Clune, Juntao Dai, Agnes Delaborde, Nouha Dziri, Francisco Eiras, Joshua Engels, Jinyu Fan, Adam Gleave, Noah Goodman, Fynn Heide, Dan Hendrycks, Cyrus Hodes, Bryan Low Kian Hsiang, Minlie Huang, Sami Jawhar, Wang Jingyu, Adam Tauman Kalai, Meindert Kamphuis, Mohan Kankanhalli, Subhash Kantamneni, Mathias Bonde Kirk, Thomas Kwa, Jeffrey Ladish, Kwok-Yan Lam, Wan Lee Sie, Taewhi Lee, Xiaojian Li, Jiajun Liu, Chaochao Lu, Yifan Mai, Richard Mallah, Julian Michael, Nick Moës, Simon Möller, Kihyuk Nam, Kwan Yee Ng, Mark Nitzberg, Besmira Nushi, Seán O hÉigeartaigh, Alejandro Ortega, Pierre Peigné, James Petrie, Benjamin Prud'homme, Reihaneh Rabbany, Nayat Sanchez-pi, Sarah Schwettmann, Buck Shlegeris, Saad Siddiqui, Aradhana Sinha, Martín Soto, Cheston Tan, Dong Ting, Robert Trager, Brian Tse, Anthony Tung K. H., Vanessa Wilfred, John Willes, Denise Wong, Wei Xu, Rongwu Xu, Yi Zeng, HongJiang Zhang, Djordje Žikelić
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i. e., trustworthy, reliable, and secure.
1 code implementation • 11 Jun 2025 • Siheng Li, Zhanhui Zhou, Wai Lam, Chao Yang, Chaochao Lu
Reinforcement learning (RL) is vital for optimizing large language models (LLMs).
no code implementations • 10 Jun 2025 • Jiachen Ma, Zhanhui Zhou, Chao Yang, Chaochao Lu
Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios.
1 code implementation • 4 Jun 2025 • Zhanhui Zhou, Lingjie Chen, Chao Yang, Chaochao Lu
Building on this, we simulate the adversarial data poisoning scenario mentioned above by using patches from dangerous images and replacing IDs with text descriptions like ``safe'' or ``unsafe'', demonstrating how harmful content can evade moderation in patches and later be reconstructed through visual stitching, posing serious VLM safety risks.
no code implementations • 3 Jun 2025 • Bo Peng, Zhiheng Wang, Heyang Gong, Chaochao Lu
In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial.
no code implementations • 3 Jun 2025 • Xiaoying Zhang, Hao Sun, YiPeng Zhang, Kaituo Feng, Chaochao Lu, Chao Yang, Helen Meng
We then demonstrate that RL-finetuned models, even after exhibiting performance plateaus, can generate correct refinements on persistently failed problems by leveraging natural language feedback in the form of critiques.
no code implementations • 30 May 2025 • Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, MingChuan Yang
However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversarial attacks, and (3) the risk of feedback bias and reward hacking.
1 code implementation • 26 May 2025 • Sirui Chen, Shuqin Ma, Shu Yu, Hanwang Zhang, Shengjie Zhao, Chaochao Lu
Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency.
no code implementations • 15 Apr 2025 • Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu
Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test--time compute.
1 code implementation • 27 Mar 2025 • Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, BoWen Zhou, Yu Cheng
Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference.
no code implementations • 7 Feb 2025 • Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou
Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit specific failure cases with unsatisfactory outputs.
no code implementations • 19 Dec 2024 • Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, Jianyu Chen
Based on this hypothesis, we propose the Video Prediction Policy (VPP), which learns implicit inverse dynamics model conditioned on predicted future representations inside VDMs.
no code implementations • 8 Dec 2024 • Chao Yang, Chaochao Lu, Yingchun Wang, BoWen Zhou
In this position paper, we propose the \textit{AI-\textbf{$45^{\circ}$} Law} as a guiding principle for a balanced roadmap toward trustworthy AGI, and introduce the \textit{Causal Ladder of Trustworthy AGI} as a practical framework.
1 code implementation • 29 Nov 2024 • Yujin Han, Lei Xu, Sirui Chen, Difan Zou, Chaochao Lu
We further apply the ADCE to evaluate a series of mainstream LLMs, showing that most of them exhibit deep structure comprehension ability, which grows along with the prediction accuracy.
no code implementations • 27 Nov 2024 • Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang, Xiaoyu Chen, Chaochao Lu, Jianyu Chen
On the other line, diffusion models have also shown promise in robotic control tasks by denoising actions, known as diffusion policy.
1 code implementation • 18 Nov 2024 • ZiYi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang, Ziyue Gan, Zhiyu Wang, Zijian Ling, Jinsong Chen, Martz Ma, Bowen Dong, Prateek Gupta, Shuyue Hu, Zhenfei Yin, Guohao Li, Xu Jia, Lijun Wang, Bernard Ghanem, Huchuan Lu, Chaochao Lu, Wanli Ouyang, Yu Qiao, Philip Torr, Jing Shao
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i. e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems.
no code implementations • 29 Oct 2024 • Shu Yu, Chaochao Lu
ADAM is empowered by four key components: 1) an interaction module, enabling the agent to execute actions while documenting the interaction processes; 2) a causal model module, tasked with constructing an ever-growing causal graph from scratch, which enhances interpretability and diminishes reliance on prior knowledge; 3) a controller module, comprising a planner, an actor, and a memory pool, which uses the learned causal graph to accomplish tasks; 4) a perception module, powered by multimodal large language models, which enables ADAM to perceive like a human player.
1 code implementation • 24 Oct 2024 • Sirui Chen, Shu Yu, Shengjie Zhao, Chaochao Lu
Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process.
1 code implementation • 29 Jul 2024 • JinFan Hu, Jinjin Gu, Shiyao Yu, Fanghua Yu, Zheyuan Li, Zhiyuan You, Chaochao Lu, Chao Dong
Based on the causal effect theory, the proposed diagnostic tool can refresh our common knowledge and bring a deeper understanding of low-level vision models.
1 code implementation • 27 Jun 2024 • Meiqi Chen, Bo Peng, Yan Zhang, Chaochao Lu
Previous work typically focuses on commonsense causality between events and/or actions, which is insufficient for applications like embodied agents and lacks the explicitly defined causal graphs required for formal causal reasoning.
1 code implementation • 24 Jun 2024 • Sirui Chen, Mengying Xu, Kun Wang, Xingyu Zeng, Rui Zhao, Shengjie Zhao, Chaochao Lu
Causal reasoning is a cornerstone of how humans interpret the world.
2 code implementations • 1 May 2024 • Sirui Chen, Bo Peng, Meiqi Chen, Ruiqi Wang, Mengying Xu, Xingyu Zeng, Rui Zhao, Shengjie Zhao, Yu Qiao, Chaochao Lu
Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal reasoning.
1 code implementation • 27 Mar 2024 • Meiqi Chen, Yixin Cao, Yan Zhang, Chaochao Lu
Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions.
no code implementations • 29 Jan 2024 • Heyang Gong, Chaochao Lu, Yu Zhang
In the field of causal modeling, potential outcomes (PO) and structural causal models (SCMs) stand as the predominant frameworks.
no code implementations • 26 Jan 2024 • Chaochao Lu, Chen Qian, Guodong Zheng, Hongxing Fan, Hongzhi Gao, Jie Zhang, Jing Shao, Jingyi Deng, Jinlan Fu, Kexin Huang, Kunchang Li, Lijun Li, LiMin Wang, Lu Sheng, Meiqi Chen, Ming Zhang, Qibing Ren, Sirui Chen, Tao Gui, Wanli Ouyang, Yali Wang, Yan Teng, Yaru Wang, Yi Wang, Yinan He, Yingchun Wang, Yixu Wang, Yongting Zhang, Yu Qiao, Yujiong Shen, Yurong Mou, Yuxi Chen, Zaibin Zhang, Zhelun Shi, Zhenfei Yin, Zhipin Wang
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
1 code implementation • 11 Oct 2023 • Bo Peng, Xinyuan Chen, Yaohui Wang, Chaochao Lu, Yu Qiao
In this work, we introduce ConditionVideo, a training-free approach to text-to-video generation based on the provided condition, video, and input text, by leveraging the power of off-the-shelf text-to-image generation methods (e. g., Stable Diffusion).
no code implementations • 12 Oct 2021 • Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
no code implementations • ICLR 2022 • Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
Extensive experiments on both synthetic and real-world datasets show that our approach outperforms a variety of baseline methods.
1 code implementation • ICLR 2022 • Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.
no code implementations • 24 Feb 2021 • Chaochao Lu, Yuhuai Wu, Jośe Miguel Hernández-Lobato, Bernhard Schölkopf
Finally, in the discussion, we further explore the aforementioned assumption and propose a more general hypothesis, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes.
no code implementations • 1 Jan 2021 • Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf
As an alternative, we propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution generalization in the nonlinear setting (i. e., nonlinear representations and nonlinear classifiers).
no code implementations • 16 Dec 2020 • Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf
We propose counterfactual RL algorithms to learn both population-level and individual-level policies.
no code implementations • 24 Jul 2020 • Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman
In this paper we provide a firm theoretical interpretation for infinite spatial generation, by drawing connections to spatial stochastic processes.
1 code implementation • 26 Dec 2018 • Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato
Using this benchmark, we demonstrate that the proposed algorithms are superior to traditional RL methods in confounded environments with observational data.
no code implementations • CVPR 2017 • Chaochao Lu, Michael Hirsch, Bernhard Scholkopf
We describe a modular framework for video frame prediction.
no code implementations • 15 Apr 2014 • Chaochao Lu, Xiaoou Tang
For the first time, the human-level performance in face verification (97. 53%) on LFW is surpassed.