2 citations · 5 across the 9 of their papers we have counts for
12 papers
DEdit: Iterative Draft Editing for Speculative Decoding
Longxuan Yu, Bingsen Chen, Peng Shi +9
Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusion-based drafters further red…
Look Less, Think Faster: Joint Token-Compute Adaptation for Multimodal LLMs
Pengcheng Wang, Zhiquan Wang, Jayoung Lee +5
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across vision-language tasks. However, their high inference cost, arising from both the large…
Efficient Table Retrieval and Understanding with Multimodal Large Language Models
Zhuoyan Xu, Haoyang Fang, Boran Han +4
Tabular data is frequently captured in image form across a wide range of real-world scenarios such as financial reports, handwritten records, and document scans. These visual repre…
Can Language Models Compose Skills In-Context?
Zidong Liu, Zhuoyan Xu, Zhenmei Shi +1
Composing basic skills from simple tasks to accomplish composite tasks is crucial for modern intelligent systems. We investigate the in-context composition ability of language mode…
Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering
Sai Prasanna Teja Reddy Bogireddy, Abrar Majeedi, Viswanatha Reddy Gajjala +3
Automated question answering (QA) over electronic health records (EHRs) can bridge critical information gaps for clinicians and patients, yet it demands both precise evidence retri…
Learning to Inference Adaptively for Multimodal Large Language Models
Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee +4
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in visual reasoning, yet come with substantial computational cost, limiting their deployment in resource…
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Zhuoyan Xu, Zhenmei Shi, Yingyu Liang
Large language models (LLMs) have emerged as powerful tools for many AI problems and exhibit remarkable in-context learning (ICL) capabilities. Compositional ability, solving unsee…
Out-of-distribution generalization via composition: a lens through induction heads in Transformers
Jiajun Song, Zhuoyan Xu, Yiqiao Zhong
Large language models (LLMs) such as GPT-4 sometimes appear to be creative, solving novel tasks often with a few demonstrations in the prompt. These tasks require the models to gen…
Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu +1
Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief…
Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
Yingyu Liang, Heshan Liu, Zhenmei Shi +3
The self-attention mechanism is the key to the success of transformers in recent Large Language Models (LLMs). However, the quadratic computational cost in the input seque…
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Zhuoyan Xu, Zhenmei Shi, Junyi Wei +3
Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those w…
Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases
Zhuoyan Xu, Kris Sankaran
Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation co…