18 citations · 50 across the 20 of their papers we have counts for
21 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
Is Your Conditional Diffusion Model Actually Denoising?
Daniel Pfrommer, Zehao Dou, Christopher Scarvelis +2
We study the inductive biases of diffusion models with a conditioning-variable, which have seen widespread application as both text-conditioned generative image models and observat…
Monitoring Monitorability
Melody Y. Guan, Miles Wang, Micah Carroll +9
Observability into the decision making of modern AI systems may be required to safely deploy increasingly capable agents. Monitoring the chain-of-thought (CoT) of today's reasoning…
FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
Sifan Wang, Zehao Dou, Siming Shan +2
Recent advances in generative modeling -- particularly diffusion models and flow matching -- have achieved remarkable success in synthesizing discrete data such as images and video…
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
Bowen Baker, Joost Huizinga, Leo Gao +6
Mitigating reward hacking--where AI systems misbehave due to flaws or misspecifications in their learning objectives--remains a key challenge in constructing capable and aligned mo…
Approximating N-Player Nash Equilibrium through Gradient Descent
Dongge Wang, Xiang Yan, Zehao Dou +3
Decoding how rational agents should behave in shared systems remains a critical challenge within theoretical computer science, artificial intelligence and economics studies. Centra…
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
Yaxuan Zhu, Zehao Dou, Haoxin Zheng +3
Recent studies demonstrate that diffusion models can serve as a strong prior for solving inverse problems. A prominent example is Diffusion Posterior Sampling (DPS), which approxim…
From optimal score matching to optimal sampling
Zehao Dou, Subhodh Kotekal, Zhehao Xu +1
The recent, impressive advances in algorithmic generation of high-fidelity image, audio, and video are largely due to great successes in score-based diffusion models. A key impleme…
Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
Hengyu Fu, Zehao Dou, Jiawei Guo +2
Diffusion Transformer, the backbone of Sora for video generation, successfully scales the capacity of diffusion models, pioneering new avenues for high-fidelity sequential data gen…
Provable Statistical Rates for Consistency Diffusion Models
Zehao Dou, Minshuo Chen, Mengdi Wang +1
Diffusion models have revolutionized various application domains, including computer vision and audio generation. Despite the state-of-the-art performance, diffusion models are kno…
FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling
Yan Luo, Muhammad Osama Khan, Yu Tian +5
Equity in AI for healthcare is crucial due to its direct impact on human well-being. Despite advancements in 2D medical imaging fairness, the fairness of 3D models remains underexp…
Learning Narrow One-Hidden-Layer ReLU Networks
Sitan Chen, Zehao Dou, Surbhi Goel +2
We consider the well-studied problem of learning a linear combination of ReLU activations with respect to a Gaussian distribution on inputs in dimensions. We give the first…
Rates of estimation for high-dimensional multi-reference alignment
Zehao Dou, Zhou Fan, Harrison Zhou
We study the continuous multi-reference alignment model of estimating a periodic function on the circle from noisy and circularly-rotated observations. Motivated by analogous high-…
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Zehao Dou, Jakub Grudzien Kuba, Yaodong Yang
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games. For such a decomposition rule to hol…
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization
Zehao Dou, Yuanzhi Li
Adam-type methods, the extension of adaptive gradient methods, have shown great performance in the training of both supervised and unsupervised machine learning models. In particul…
Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang +1
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention. Recently, there have been more theoretical works on the reg…
Diffusion Mechanism in Residual Neural Network: Theory and Applications
Tangjun Wang, Zehao Dou, Chenglong Bao +1
Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training sam…
Making Method of Moments Great Again? -- How can GANs learn distributions
Yuanzhi Li, Zehao Dou
Generative Adversarial Networks (GANs) are widely used models to learn complex real-world distributions. In GANs, the training of the generator usually stops when the discriminator…
Finding Mixed Strategy Nash Equilibrium for Continuous Games through Deep Learning
Zehao Dou, Xiang Yan, Dongge Wang +1
Nash equilibrium has long been a desired solution concept in multi-player games, especially for those on continuous strategy spaces, which have attracted a rapidly growing amount o…
Mathematical Analysis of Adversarial Attacks
Zehao Dou, Stanley J. Osher, Bao Wang
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM c…
Hierarchical Attention: What Really Counts in Various NLP Tasks
Zehao Dou, Zhihua Zhang
Attention mechanisms in sequence to sequence models have shown great ability and wonderful performance in various natural language processing (NLP) tasks, such as sentence embeddin…