1 citations · 1 across the 6 of their papers we have counts for
11 papers
ValuSpec: Plug-and-Play Candidate Valuation before Target Verification for Tree-Based Speculative Decoding
Liang He, SiYuan Ma, Qishi Zhan +5
Tree-based speculative decoding accelerates large language model inference by verifying multiple candidate branches in parallel. However, in practical serving scenarios, similar re…
MedSTP: Preserving Subspace and Transition Evidence for Medical VQA Token Pruning
Mengchen Fan, Qizhen Lan, Xi Xiao +6
Medical vision-language models (VLMs) for visual question answering (VQA) often pass long visual-token sequences to the language model, making visual-token pruning attractive but r…
BrainTRACE: Tracing Longitudinal, Multimodal, and Volumetric Evidence in Brain MRI Clinical Reasoning
Qizhen Lan, Mengchen Fan, Hang Zhang +5
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within vol…
You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference
Liang He, Jingbo Wen, Yixiong Chen +4
Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored decision axis: after choosing…
EvoTreeNAD: Genealogy-Guided Evolution for LLM-Driven Neural Architecture Discovery
Lishan Yu, Derek Jiu, Qizhen Lan +1
AI-driven scientific discovery accelerates research by autonomously developing solutions and designs. Large language model (LLM) agents support this process through iterative gener…
Displacement Preserving Relational Distillation for Robust Medical Segmentation
Zhicheng Ding, Xinyu Chu, Jung Im Choi +5
Accurate 3D medical segmentation is limited by anatomical variability and high computational costs. While knowledge distillation (KD) offers a route for model compression, conventi…
BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding
Liang He, Jingbo Wen, Qishi Zhan +4
Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel. In resource-constrained deployments, the…
Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation
Mengchen Fan, Baocheng Geng, Xi Xiao +5
Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D…
Learnable Instance Attention Filtering for Adaptive Detector Distillation
Chen Liu, Qizhen Lan, Zhicheng Ding +2
As deep vision models grow increasingly complex to achieve higher performance, deployment efficiency has become a critical concern. Knowledge distillation (KD) mitigates this issue…
From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows
Qizhen Lan, Aaron Choi, Jun Ma +4
Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational resources are fixed and cloud-base…
ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation
Qizhen Lan, Yu-Chun Hsu, Nida Saddaf Khan +1
Accurate 3D medical image segmentation is vital for diagnosis and treatment planning, but state-of-the-art models are often too large for clinics with limited computing resources.…