1 citations · 2 across the 12 of their papers we have counts for
13 papers
Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
Pengcheng Jiang, Zhiyi Shi, Kelly Hong +5
Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which c…
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Jimeng Shi, Yixuan Xie +10
Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where so…
Learning to Predict Future-Aligned Research Proposals with Language Models
Heng Wang, Pengcheng Jiang, Jiashuo Sun +4
Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals remains difficult: novelty and sou…
CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support
Yichen Wu, Kailong Fan, Sangjoon Park +9
Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicab…
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31
Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…
Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model
Hyun-Jic Oh, Junsik Kim, Zhiyi Shi +5
Multiplex imaging is revolutionizing pathology by enabling the simultaneous visualization of multiple biomarkers within tissue samples, providing molecular-level insights that trad…
Spatial-Temporal Pre-Training for Embryo Viability Prediction Using Time-Lapse Videos
Zhiyi Shi, Junsik Kim, Helen Y. Yang +5
Automating embryo viability prediction for in vitro fertilization (IVF) is important but challenging due to the limited availability of labeled pregnancy outcome data, as only a sm…
DualEdit: Dual Editing for Knowledge Updating in Vision-Language Models
Zhiyi Shi, Binjie Wang, Chongjie Si +3
Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve prom…
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
Chongjie Si, Zhiyi Shi, Yadao Wang +3
The rapid development of large language models has revolutionized natural language processing, but their fine-tuning remains computationally expensive, hindering broad deployment.…
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations
Chongjie Si, Zhiyi Shi, Xuehui Wang +3
Adapting pre-trained foundation models for diverse downstream tasks is a core practice in artificial intelligence. However, the wide range of tasks and high computational costs mak…
Multimodal Learning for Embryo Viability Prediction in Clinical IVF
Junsik Kim, Zhiyi Shi, Davin Jeong +8
In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this p…
Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
Chongjie Si, Zhiyi Shi, Shifan Zhang +3
Large language models demonstrate impressive performance on downstream tasks, yet they require extensive resource consumption when fully fine-tuning all parameters. To mitigate thi…
MoRA: LoRA Guided Multi-Modal Disease Diagnosis with Missing Modality
Zhiyi Shi, Junsik Kim, Wanhua Li +2
Multi-modal pre-trained models efficiently extract and fuse features from different modalities with low memory requirements for fine-tuning. Despite this efficiency, their applicat…