2 citations · 5 across the 13 of their papers we have counts for
14 papers
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization
Jiacheng Lin, Geoffrey Low, Nishit Agarwal +4
Clinical trial protocols serve as the source of truth for study design, but are mostly written and used as unstructured PDF documents. Digitizing protocols turns static content int…
TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization
Jiacheng Lin, Zifeng Wang, Zheng Chen +5
Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on clinical development planni…
Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
Brian Wang, Bin Feng, Xiaoman Pan +26
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…
LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
Jiacheng Lin, Kun Qian, Arvind Srinivasan +15
Personalization today is fundamentally platform-centric: services build user representations from the behavioral fragments they observe. Yet no platform can construct a complete pi…
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…
Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning
Jiacheng Lin, Zhenbang Wu, Jimeng Sun
We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). Wh…
Developing Large Language Models for Clinical Research Using One Million Clinical Trials
Zifeng Wang, Jiacheng Lin, Qiao Jin +5
Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce Tria…
s3: You Don't Need That Much Data to Train a Search Agent via RL
Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin +4
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search…
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning
Pengcheng Jiang, Jiacheng Lin, Lang Cao +5
Information retrieval systems are crucial for enabling effective access to large document collections. Recent approaches have leveraged Large Language Models (LLMs) to enhance retr…
GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data
Jiacheng Lin, Kun Qian, Haoyu Han +9
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive e…
Panacea: A foundation model for clinical trial search, summarization, design, and recruitment
Jiacheng Lin, Hanwen Xu, Zifeng Wang +2
Clinical trials are fundamental in developing new drugs, medical devices, and treatments. However, they are often time-consuming and have low success rates. Although there have bee…
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Pengcheng Jiang, Jiacheng Lin, Zifeng Wang +2
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). How…
CAMBranch: Contrastive Learning with Augmented MILPs for Branching
Jiacheng Lin, Meng Xu, Zhihua Xiong +1
Recent advancements have introduced machine learning frameworks to enhance the Branch and Bound (B\&B) branching policies for solving Mixed Integer Linear Programming (MILP). These…
Cascade Speculative Drafting for Even Faster LLM Inference
Ziyi Chen, Xiaocong Yang, Jiacheng Lin +3
Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft. A larger target model then r…