3 citations · 5 across the 8 of their papers we have counts for
9 papers
Entertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
Zhizhuo Kou, Jian Yang, Pengfei Hao +4
When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but we do n…
Cache-Aware I/O Cost Modeling for Disk-Based Learned Indexes
Zhanwei Shi, Meng Zhang, Guangyi Zhang +5
Learned indexes have shown attractive space-time trade-offs in main-memory settings, yet a principled I/O cost model for their disk-resident deployments is still missing, which is…
CARPO: Leveraging Listwise Learning-to-Rank for Context-Aware Query Plan Optimization
Wenrui Zhou, Qiyu Liu, Jingshu Peng +2
Efficient data processing is increasingly vital, with query optimizers playing a fundamental role in translating SQL queries into optimal execution plans. Traditional cost-based op…
Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
Hao Wang, Jingshu Peng, Yanyan Shen +4
Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series fore…
Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu +7
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded…
Learned Data Compression: Challenges and Opportunities for the Future
Qiyu Liu, Siyuan Han, Jianwei Liao +4
Compressing integer keys is a fundamental operation among multiple communities, such as database management (DB), information retrieval (IR), and high-performance computing (HPC).…
Why Are Learned Indexes So Effective but Sometimes Ineffective?
Qiyu Liu, Siyuan Han, Yanlin Qi +4
Learned indexes have attracted significant research interest due to their ability to offer better space-time trade-offs compared to traditional B+-tree variants. Among various lear…
Automate Strategy Finding with LLM in Quant Investment
Zhizhuo Kou, Holam Yu, Junyu Luo +7
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our a…
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models
Jiawei Li, Jingshu Peng, Haoyang Li +1
Time-series analysis plays a pivotal role across a range of critical applications, from finance to healthcare, which involves various tasks, such as forecasting and classification.…