11 papers
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
Wendi Yu, Lianhao Zhou, Xiangjue Dong +6
LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, their advantages over single-agent systems (SAS) remain unclear, with perform…
Perron--Frobenius Operator Matching for Generative Modeling
Shiqi Zhang, Wuwei Wu, Jaemin Oh +2
We introduce Perron--Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump mod…
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon
Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter unce…
C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang +4
Low-Rank Adaptation (LoRA) offers a cost-effective solution for fine-tuning large language models (LLMs), but it often produces overconfident predictions in data-scarce few-shot se…
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
Sanket Jantre, Deepak Akhare, Zhiyuan Wang +2
Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs rather than using tra…
Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Sanket Jantre, Tianle Wang, Gilchan Park +5
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative d…