6 papers · 1 filter
CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning
Ke Sun, Yizhou Zhao, Jiayi Xin +2
Context or prompt-level reweighting has emerged as a central algorithmic lever in Reinforcement Learning with Verified Rewards (RLVR) for improving the reasoning capability of larg…
UCS: Estimating Unseen Coverage for Improved In-Context Learning
Jiayi Xin, Xiang Li, Evan Qiang +4
In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or…
CAMEL: An ECG Language Model for Forecasting Cardiac Events
Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya +9
Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a pr…
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
Jiayi Xin, Sukwon Yun, Jie Peng +4
Modality fusion is a cornerstone of multimodal learning, enabling information integration from diverse data sources. However, vanilla fusion methods are limited by (1) inability to…
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
Sukwon Yun, Inyoung Choi, Jie Peng +6
Multimodal learning has gained increasing importance across various fields, offering the ability to integrate data from diverse sources such as images, text, and personalized recor…
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting
Linfeng Du, Ji Xin, Alex Labach +3
Transformer-based models have greatly pushed the boundaries of time series forecasting recently. Existing methods typically encode time series data into using on…