3 citations · 10 across the 14 of their papers we have counts for
14 papers
M2G-LLM: Enhancing Clinical Prediction via Multimodal Graph Reasoning and LLM Context Injection
Inyoung Choi, Sukwon Yun, Jiayi Xin +3
Integrating diverse data modalities --- such as clinical notes, laboratory results, and medical imaging --- is essential for advancing clinical decision-making. While Large Languag…
The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability
Abigail Woodring, Adrian Chan, Rana Muhammad Shahroz Khan +3
Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such as low-rank adaptation (LoRA)…
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration
Sukwon Yun, Jie Peng, Pingzhi Li +5
With an ever-growing zoo of LLMs and benchmarks, the need to orchestrate multiple models for improved task performance has never been more pressing. While frameworks like Mixture-o…
Sparse Mixture-of-Experts for Multi-Channel Imaging: Are All Channel Interactions Required?
Sukwon Yun, Heming Yao, Burkhard Hoeckendorf +3
Vision Transformers () have become the backbone of vision foundation models, yet their optimization for multi-channel domains - such as cell painting or satellite imag…
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
Zhenglun Kong, Mufan Qiu, John Boesen +5
Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics tec…
Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis
Chi-Jane Chen, Yuhang Chen, Sukwon Yun +2
Image mass cytometry (IMC) enables high-dimensional spatial profiling by combining mass cytometry's analytical power with spatial distributions of cell phenotypes. Recent studies l…
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
Jiayi Xin, Sukwon Yun, Jie Peng +4
Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods are limited by (1) inability to…
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer
Huaizhi Qu, Inyoung Choi, Zhen Tan +6
LLM ensembles are widely used for LLM judges. However, how to estimate their accuracy, especially in an efficient way, is unknown. In this paper, we present a principled maximum a…
: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks
Rana Muhammad Shahroz Khan, Zhen Tan, Sukwon Yun +2
Most discussions about Large Language Model (LLM) safety have focused on single-agent settings but multi-agent LLM systems now create novel adversarial risks because their behavior…
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models
Mufan Qiu, Xinyu Hu, Fengwei Zhan +6
Foundation models for single-cell RNA sequencing (scRNA-seq) have shown promising capabilities in capturing gene expression patterns. However, current approaches face critical limi…
Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
Justin Chih-Yao Chen, Sukwon Yun, Elias Stengel-Eskin +2
Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coarse-grained, since different instances…
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
Sukwon Yun, Inyoung Choi, Jie Peng +6
Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized recor…
Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems
Guibin Zhang, Yanwei Yue, Zhixun Li +6
Recent advancements in large language model (LLM)-powered agents have shown that collective intelligence can significantly outperform individual capabilities, largely attributed to…
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
Rana Muhammad Shahroz Khan, Pingzhi Li, Sukwon Yun +4
As large language models (LLMs) increasingly shape the AI landscape, fine-tuning pretrained models has become more popular than in the pre-LLM era for achieving optimal performance…