Search Results for author: Chaochao Lu

Found 36 papers, 15 papers with code

The Singapore Consensus on Global AI Safety Research Priorities

no code implementations25 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.

RePO: Replay-Enhanced Policy Optimization

1 code implementation11 Jun 2025 Siheng Li, Zhanhui Zhou, Wai Lam, Chao Yang, Chaochao Lu

Reinforcement learning (RL) is vital for optimizing large language models (LLMs).

Math Mathematical Reasoning +1

SafeCoT: Improving VLM Safety with Minimal Reasoning

no code implementations10 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.

VLMs Can Aggregate Scattered Training Patches

1 code implementation4 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.

Data Poisoning

IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data

no code implementations3 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.

Attribute Synthetic Data Generation

Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

no code implementations3 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.

Reinforcement Learning (RL)

Adversarial Preference Learning for Robust LLM Alignment

no code implementations30 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.

Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks

1 code implementation26 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.

ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search

no code implementations15 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.

RAG Retrieval-augmented Generation +1

A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond

1 code implementation27 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.

Survey

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?

no code implementations7 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.

Denoising

Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

no code implementations19 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.

Contrastive Learning Image Reconstruction +2

Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI

no code implementations8 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.

Decision Making

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability

1 code implementation29 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.

Prediction with Action: Visual Policy Learning via Joint Denoising Process

no code implementations27 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.

Denoising Image Generation +2

OASIS: Open Agent Social Interaction Simulations with One Million Agents

1 code implementation18 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.

Large Language Model Recommendation Systems

ADAM: An Embodied Causal Agent in Open-World Environments

no code implementations29 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.

Lifelong learning Minecraft +2

From Imitation to Introspection: Probing Self-Consciousness in Language Models

1 code implementation24 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.

Interpreting Low-level Vision Models with Causal Effect Maps

1 code implementation29 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.

Diagnostic Image Denoising

CELLO: Causal Evaluation of Large Vision-Language Models

1 code implementation27 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.

counterfactual Decision Making

Causal Evaluation of Language Models

2 code implementations1 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.

Causal Discovery Causal Inference +1

Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective

1 code implementation27 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.

Question Answering Visual Question Answering

Distribution-consistency Structural Causal Models

no code implementations29 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.

counterfactual Counterfactual Reasoning +1

ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation

1 code implementation11 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).

Text to Image Generation Text-to-Image Generation +2

Action-Sufficient State Representation Learning for Control with Structural Constraints

no code implementations12 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.

Computational Efficiency Decision Making +1

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

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.

Atari Games reinforcement-learning +2

Nonlinear Invariant Risk Minimization: A Causal Approach

no code implementations24 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.

BIG-bench Machine Learning Representation Learning

Invariant Causal Representation Learning

no code implementations1 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).

Out-of-Distribution Generalization Representation Learning

Interpreting Spatially Infinite Generative Models

no code implementations24 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.

Generative Adversarial Network Texture Synthesis

Deconfounding Reinforcement Learning in Observational Settings

1 code implementation26 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.

OpenAI Gym reinforcement-learning +2

Surpassing Human-Level Face Verification Performance on LFW with GaussianFace

no code implementations15 Apr 2014 Chaochao Lu, Xiaoou Tang

For the first time, the human-level performance in face verification (97. 53%) on LFW is surpassed.

Diversity Face Recognition +2

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