3 citations · 4 across the 10 of their papers we have counts for
12 papers
TxnQA: Benchmarking Analytical Reasoning and Evidence Grounding over Transaction Event Sequences
Rabimba Karanjai, Yiming Bian, Yun-Shiuan Chuang +9
Analyzing merchant transaction histories requires dynamic temporal filtering, entity resolution, and aggregation over irregular event streams--operations poorly captured by static…
Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
Xuyang Shen, Zijie Pan, Diego Cerrai +4
Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These out…
Ada-RS: Adaptive Rejection Sampling for Selective Thinking
Yirou Ge, Yixi Li, Alec Chiu +8
Large language models (LLMs) are increasingly being deployed in cost and latency-sensitive settings. While chain-of-thought improves reasoning, it can waste tokens on simple reques…
Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents
Yun-Shiuan Chuang, Chaitanya Kulkarni, Alec Chiu +8
Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks for these agents must bo…
Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
Kanghui Ning, Zijie Pan, Yushan Jiang +3
Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. T…
Multi-modal Time Series Analysis: A Tutorial and Survey
Yushan Jiang, Kanghui Ning, Zijie Pan +7
Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, i…
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning, Zijie Pan, Yu Liu +7
Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they of…
International Trade Flow Prediction with Bilateral Trade Provisions
Zijie Pan, Stepan Gordeev, Jiahui Zhao +4
This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global…
IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Zijie Pan, Yushan Jiang, Sahil Garg +3
Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, establ…
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Zijie Pan, Yushan Jiang, Dongjin Song +4
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting per…
Empowering Time Series Analysis with Large Language Models: A Survey
Yushan Jiang, Zijie Pan, Xikun Zhang +4
Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, complet…
Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements
Jiahui Zhao, Ziyi Meng, Stepan Gordeev +4
With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction…