1 citations · 4 across the 34 of their papers we have counts for
12 papers · 1 filter
DRIFT: Direct-Recursive Intervention-Conditioned Forecasting of ICU Physiological Trajectories
Weixin Liu, Juming Xiong, Congning Ni +4
Many time-series forecasts depend not only on prior observations but also on actions specified during the forecast period. In intensive care units (ICUs), future vital signs and la…
SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
Chongyu Qu, Can Cui, Zhengyi Lu +8
Does every cancer patient truly need a complete diagnostic workup for accurate survival prediction? In multimodal clinical oncology, diagnostic modalities follow a clinically manda…
CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning
Juming Xiong, Weixin Liu, Kevin Guo +9
Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly…
RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
Weixin Liu, Juming Xiong, Yang Li +5
Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertain…
It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behavior to sycophancy learned duri…
Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization
Weixin Liu, Bowen Qu, Juming Xiong +3
Large language model (LLM) summarization systems may pass compact vector representations of private inputs to downstream retrieval, monitoring, audit, or analytic workflows. Even w…
DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction
Junchao Zhu, Ruining Deng, Junlin Guo +11
Inferring spatially resolved gene expression from histology images offers a cost-effective complement to spatial transcriptomics (ST). However, existing methods reduce this task to…
CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Medical large language model (LLM) evaluations rely on simplified, exam-style benchmarks that rarely reflect the ambiguity of real-world medical inquiries. We introduce the CLinica…
Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Reliability
Kevin H. Guo, Chao Yan, Avinash Baidya +5
Large language models (LLMs) excel on static benchmarks, but their performance across multi-turn conversations, which better reflect real-world usage, remains understudied. Address…
Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni +7
Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incu…
AdaFuse: Adaptive Multimodal Fusion for Lung Cancer Risk Prediction via Reinforcement Learning
Chongyu Qu, Zhengyi Lu, Yuxiang Lai +10
Multimodal fusion has emerged as a promising paradigm for disease diagnosis and prognosis, integrating complementary information from heterogeneous data sources such as medical ima…
SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
Junchao Zhu, Ruining Deng, Junlin Guo +13
Spatial transcriptomics (ST) is an emerging technology that enables researchers to investigate the molecular relationships underlying tissue morphology. However, acquiring ST data…