2 citations · 2 across the 9 of their papers we have counts for
11 papers
Learning Perturbation Robust Policies for LLM Agents with Stable Optimization
Pengxin Wang, Yuanzhe LI, Yuxin Ren +2
Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policies can be s…
Lightweight Pedestrian Head-Orientation Recognition Network for Safe Pedestrian-Vehicle Interaction
Yuanzhe Li, Yidi Huang, Xiaotong Chang +1
Pedestrian head orientation recognition plays an important role in autonomous driving by providing valuable cues for understanding pedestrian attention and anticipating potential c…
Detecting Phone-Induced Pedestrian Distraction via a Multimodal Fusion Transformer
Yuanzhe Li, Hounian Liu, Xiaotong Chang +1
The increasing reliance on mobile phones has made phone-induced pedestrian distraction increasingly prevalent. Activities such as texting, watching videos, and making phone calls h…
SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
Jianing Deng, Yuanzhe Li, Jialu Wang +4
Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic com…
Dynamic Mixed-Precision Routing for Efficient Multi-step LLM Interaction
Yuanzhe Li, Jianing Deng, Jingtong Hu +3
Large language models (LLMs) achieve strong performance in long-horizon decision-making tasks through multi-step interaction and reasoning at test time. While practitioners commonl…
AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving
Tianhao Xu, Yiming Liu, Xianglong Lu +18
Optimizing Large Language Model (LLM) inference in production systems is increasingly difficult due to dynamic workloads, stringent latency/throughput targets, and a rapidly expand…
ACIT: Attention-Guided Cross-Modal Interaction Transformer for Pedestrian Crossing Intention Prediction
Yuanzhe Li, Steffen Müller
Predicting pedestrian crossing intention is crucial for autonomous vehicles to prevent pedestrian-related collisions. However, effectively extracting and integrating complementary…
Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments
Yuanzhe Li, Hang Zhong, Steffen Müller
Pedestrian crossing intention prediction is essential for autonomous vehicles to improve pedestrian safety and reduce traffic accidents. However, accurate pedestrian intention pred…
Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network
Yuanzhe Li, Steffen Müller
Pedestrian crossing intention prediction is essential for the deployment of autonomous vehicles (AVs) in urban environments. Ideal prediction provides AVs with critical environment…
SimLOB: Learning Representations of Limited Order Book for Financial Market Simulation
Yuanzhe Li, Yue Wu, Muyao Zhong +2
Financial market simulation (FMS) serves as a promising tool for understanding market anomalies and the underlying trading behaviors. To ensure high-fidelity simulations, it is cru…
StraIT: Non-autoregressive Generation with Stratified Image Transformer
Shengju Qian, Huiwen Chang, Yuanzhen Li +3
We propose Stratified Image Transformer(StraIT), a pure non-autoregressive(NAR) generative model that demonstrates superiority in high-quality image synthesis over existing autoreg…