1 citations · 2 across the 26 of their papers we have counts for
27 papers
Masked Diffusion for Travel Diaries Synthesis
Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin +2
Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis pipelines split them across…
How Can Driving World Models Do Counterfactual Prediction?
Jiaru Zhang, Can Cui, Yi Xu +3
Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an…
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Yikun Bai, Binghang Lu, Yikai Liu +7
Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models p…
Sticky Jump Diffusions: A Unifying View of Masked, Continuous, and Hybrid Diffusion
Pascal Jutras-Dubé, Patrick Pynadath, Jeremy Lu +2
We introduce Sticky Jump Diffusions (SJDs), continuous-time Markov processes on whose discrete anchors are token embeddings. In forward time, anchors release their ma…
Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Bolian Li, Yifan Wang, Yi Ding +3
Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing cont…
Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity
Anamika Lochab, Bolian Li, Ruqi Zhang
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often suffers from reduced multi-sa…
Analytical Correction for Subsampling Bias in Drifting Models
Jiaru Zhang, Zeyun Deng, Juanwu Lu +2
Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…
Learning From Developers: Towards Reliable Patch Validation at Scale for Linux
Chih-En Lin, Attreyee Mukherjee, Ajay Rawat +2
Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies t…
Who Tests the Testers? Systematic Enumeration and Coverage Audit of LLM Agent Tool Call Safety
Xuan Chen, Lu Yan, Ruqi Zhang +1
Large Language Model (LLM) agents increasingly act through external tools, making their safety contingent on tool-call workflows rather than text generation alone. While recent ben…
Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
Jiaru Zhang, Manav Gagvani, Can Cui +3
Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged as promising candidates for end-to-end autonomous driving. However, these models typically face challeng…
Why Any-Order Autoregressive Models Need Two-Stream Attention: A Structural-Semantic Tradeoff
Patrick Pynadath, Ruqi Zhang
Any-order autoregressive models (AO-ARMs) offer a promising path toward efficient masked diffusion by enabling native key-value caching, but competitive performance has so far requ…
On Learning Closed-Loop Probabilistic Multi-Agent Simulator
Juanwu Lu, Rohit Gupta, Ahmadreza Moradipari +3
The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Re…
Entropy-Guided Sampling of Flat Modes in Discrete Spaces
Pinaki Mohanty, Riddhiman Bhattacharya, Ruqi Zhang
Sampling from flat modes in discrete spaces is a crucial yet underexplored problem. Flat modes represent robust solutions and have broad applications in combinatorial optimization…
Energy-Based Reward Models for Robust Language Model Alignment
Anamika Lochab, Ruqi Zhang
Reward models (RMs) are essential for aligning Large Language Models (LLMs) with human preferences. However, they often struggle with capturing complex human preferences and genera…
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
Muheng Li, Ruqi Zhang
Recently, gradient-based discrete sampling has emerged as a highly efficient, general-purpose solver for various combinatorial optimization (CO) problems, achieving performance com…
Optimal Stochastic Trace Estimation in Generative Modeling
Xinyang Liu, Hengrong Du, Wei Deng +1
Hutchinson estimators are widely employed in training divergence-based likelihoods for diffusion models to ensure optimal transport (OT) properties. However, this estimator often s…
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang
Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterat…
Controlled LLM Decoding via Discrete Auto-regressive Biasing
Patrick Pynadath, Ruqi Zhang
Controlled text generation allows for enforcing user-defined constraints on large language model outputs, an increasingly important field as LLMs become more prevalent in everyday…
CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
Boxuan Zhang, Ruqi Zhang
Large language models (LLMs) excel in many tasks but struggle to accurately quantify uncertainty in their generated responses. This limitation makes it challenging to detect misinf…
LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends
Can Cui, Yunsheng Ma, Sung-Yeon Park +14
With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving tech…
Dynamic Obstacle Avoidance through Uncertainty-Based Adaptive Planning with Diffusion
Vineet Punyamoorty, Pascal Jutras-Dubé, Ruqi Zhang +3
By framing reinforcement learning as a sequence modeling problem, recent work has enabled the use of generative models, such as diffusion models, for planning. While these models a…
Adaptive Planning with Generative Models under Uncertainty
Pascal Jutras-Dubé, Ruqi Zhang, Aniket Bera
Planning with generative models has emerged as an effective decision-making paradigm across a wide range of domains, including reinforcement learning and autonomous navigation. Whi…
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei, Dongkuan Xu, Ruqi Zhang +1
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications. However, the reliability of sparse models remains a…
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
Ziyi Wang, Yujie Chen, Qifan Song +1
Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian coun…