97 citations · 137 across the 31 of their papers we have counts for
13 papers · 1 filter
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs
Yanrui Du, Danyang Zhao, Sendong Zhao +6
Jev has attracted considerable attention for its rapid, generalizable decision-making, yet its medical reliability remains underexplored. To address this gap, our study compares va…
DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting
Wuzhou Lian, Haochun Wang, Yuefan Deng
Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available. However, adaptation objectives that focus primarily on p…
Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
Yuzheng Fan, Haochun Wang, Sendong Zhao +3
Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how these data types differentially…
Analyzing and Mitigating Cross-Lingual Degradation in Multilingual Medical VQA
Jingbo Wang, Sendong Zhao, Haochun Wang +2
Medical visual question answering (VQA) is a crucial task in clinical AI, yet its evaluation has so far centered almost exclusively on English, limiting its relevance to linguistic…
SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
Haochun Wang, Sendong Zhao, Jingbo Wang +3
Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy interve…
Easier to Judge than to Find: Predicting In-Context Learning Success for Demonstration Selection
Haochun Wang, Chaofen Yang, Jiatong Liu +5
In-context learning (ICL) is highly sensitive to which demonstrations appear in the prompt, but selecting them is expensive because the space of possible demonstration contexts and…
Towards Principled Test-Time Adaptation for Time Series Forecasting
Haochun Wang, Ruichen Xu, Georgios Kementzidis +3
Test-time adaptation (TTA) has recently emerged as a promising approach for improving time series forecasting (TSF) under distribution shift. Existing TSF-TTA methods differ in how…
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Boyu Xiao, Xiuqi Tian, Xuwen Song +4
Despite strong medical benchmark accuracy, LLMs can exhibit severe multi-turn sycophancy in clinical dialogue, abandoning initial correct diagnosis under escalating pressure. We pr…
GSEM: Graph-based Self-Evolving Memory for Experience Augmented Clinical Reasoning
Xiao Han, Yuzheng Fan, Sendong Zhao +2
Clinical decision-making agents can benefit from reusing prior decision experience. However, many memory-augmented methods store experiences as independent records without explicit…
From Latent Signals to Reflection Behavior: Tracing Meta-Cognitive Activation Trajectory in R1-Style LLMs
Yanrui Du, Yibo Gao, Sendong Zhao +6
R1-style LLMs have attracted growing attention for their capacity for self-reflection, yet the internal mechanisms underlying such behavior remain unclear. To bridge this gap, we a…
Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models
Jingbo Wang, Sendong Zhao, Jiatong Liu +4
While multi-agent systems (MAS) have demonstrated superior performance over single-agent approaches in complex reasoning tasks, they often suffer from significant computational ine…
ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG
Jianbo Li, Yi Jiang, Sendong Zhao +3
Retrieval-Augmented Generation (RAG) helps LLMs stay accurate, but feeding long documents into a prompt makes the model slow and expensive. This has motivated context compression,…
OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation
Yi Jiang, Sendong Zhao, Jianbo Li +4
Retrieval-Augmented Generation (RAG) improves generation quality by incorporating evidence retrieved from large external corpora. However, most existing methods rely on statically…