11 citations · 12 across the 13 of their papers we have counts for
14 papers
Decompose the Distillation: Interpretable Single-Pass Guidance for Diffusion Models
Wuyang Zhang, Xiaolin Xu
Classifier-Free Guidance is a widely used inference-time guidance rule for conditional image generation with diffusion models, which exposes a guidance scale that adjusts condition…
-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?
Leilei Ding, Shumin Wang, Yuting Huang +10
Large language models (LLMs) now show strong reasoning and coding abilities, raising the prospect that they could assist in developing and optimizing the very infrastructure that p…
More Rejective, Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight
Yuchen Han, Cheng Yan, Wuyang Zhang
Pre-execution oversight is core to trusted monitoring in AI control: a fallible LLM monitor vets planned actions before irreversible execution. Over-blocking forfeits usefulness an…
UPAIR: Diagnosing Reasoning States via Uncertainty-Progress Alignment for Selective Intervention
Cheng Yan, Zhijun Fan, Guangyang Ye +4
While test-time scaling improves the problem-solving ability of large reasoning models (LRMs) through additional inference-time computation, it can also exacerbate overthinking and…
Beyond Global Routing Aggregation: Phase-Aware Expert Merging for MoE Vision-Language Models
Hongyu Zhang, Cheng Yan, Xiang Xia +1
Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full expert pool. Training-free expe…
DAVET: Denoising-Aware Visual Evidence Trajectory Allocation for Diffusion Vision-Language Models
Yongkang Zhou, Xiang Xia, Cheng Yan +2
Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual conditioning a substantial re…
REFLEX: Rethinking MoE Inference as Refinement-Aware Compute Allocation in Diffusion Language Models
Xiang Xia, Cheng Yan, Yiming Zhang +3
Mixture-of-experts (MoE) models increase parameter capacity by activating only a small subset of experts for each token. This conditional-computation paradigm has enabled autoregre…
DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference
Xiang Xia, Wuyang Zhang, Jiazheng Liu +2
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive language generation due to their potential for parallel decoding and global refinement of…
FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
Yao Li, Peiyuan Tang, Wuyang Zhang +7
Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation, but most existing approaches fuse all modalities at a single opera…
Breaking Model Lock-in: Cost-Efficient Zero-Shot LLM Routing via a Universal Latent Space
Cheng Yan, Wuyang Zhang, Zhiyuan Ning +5
The rapid proliferation of Large Language Models (LLMs) has led to a fragmented and inefficient ecosystem, a state of ``model lock-in'' where seamlessly integrating novel models re…
UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang +5
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary natur…
\(X\)-evolve: Solution space evolution powered by large language models
Yi Zhai, Zhiqiang Wei, Ruohan Li +7
While combining large language models (LLMs) with evolutionary algorithms (EAs) shows promise for solving complex optimization problems, current approaches typically evolve individ…
A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
Yuting Huang, Leilei Ding, Zhipeng Tang +5
Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However,…
Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing
Xinran Zhang, Hanqi Zhu, Yifan Duan +5
Constructing precise 3D maps is crucial for the development of future map-based systems such as self-driving and navigation. However, generating these maps in complex environments,…