48 citations · 52 across the 17 of their papers we have counts for
21 papers
Self-Evolution Reasoner: Continuous Optimization via Policy-Intrinsic Exploration and Exploitation
Wenhang Shi, Yiren Chen, Zhe Zhao +3
Reinforcement learning with verifiable rewards (RLVR) enables large language models (LLMs) to continuously self-improve their reasoning capabilities. Compared to RLVR's scalar rewa…
DBRepro: Automated Database Synthesis via a Hybrid Constraint-Solving Approach for Reproducing Slow Queries
Zhaoyang Zhang, Shuang Liu, Dengfeng Xu +4
Slow queries frequently cause severe performance bottlenecks in database management systems. Diagnosing their root causes online risks exacerbating resource contention, while data…
DBcover: A White-box SQL Test Generation Framework for Coverage Improvement
Yankai Rong, Shuang Liu, Jinhao Dong +4
Relational Database Management Systems (RDBMSs) are the backbone of modern data-intensive applications, making reliability and robustness critical. However, achieving high coverage…
From Blind Search to Memory-Aware Evolution: Efficient DBMS Tuning via Collaborative Diagnosis and Utility-Aware Retrieval
Zhaoyan Hong, Yishen Sun, Xinyi Zhang +7
Modern DBMSs expose multiple configurable components (e.g., knobs, query hints, and indexes) that jointly determine query performance. Multi-component tuning is challenging due to…
TVA: A Version-aware Temporal Graph Storage System for Real-time Analytics
Wenhao Li, Zhanhao Zhao, Jinhao Dong +4
Analyzing temporal graphs can reveal valuable insights that are typically hidden in static graphs. Unfortunately, existing graph storage systems either lack native temporal support…
RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference
Wenhao Li, Jinhao Dong, Hailin Zhang +3
Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budge…
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Wenhang Shi, Jinhao Dong, Yiren Chen +4
General agentic intelligence hinges on the ability to interact with diverse real-world tools to complete complex tasks, a capability fundamentally tied to the quality of interactio…
Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Wenhang Shi, Yiren Chen, Shuqing Bian +5
While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during training remains critically und…
Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents
Xinyi Zhang, Tiantian Chen, Zhentao Han +9
Modern database management systems (DBMSs) expose hundreds of configuration knobs that critically influence performance. Existing automated tuning methods either adopt a data-drive…
Performant Synchronization in Geo-Distributed Databases
Duling Xu, Tong Li, Zegang Sun +5
The deployment of databases across geographically distributed regions has become increasingly critical for ensuring data reliability and scalability. Recent studies indicate that d…
Anomaly Pattern-guided Transaction Bug Testing in Relational Databases
Huicong Xu, Shuang Liu, Xianyu Zhu +3
Concurrent transaction processing is a fundamental capability of Relational Database Management Systems (RDBMSs), widely utilized in applications requiring high levels of parallel…
Testing Question Answering Software with Context-Driven Question Generation
Shuang Liu, Zhirun Zhang, Jinhao Dong +5
Question-answering software is becoming increasingly integrated into our daily lives, with prominent examples including Apple Siri and Amazon Alexa. Ensuring the quality of such sy…
Investigating the Impact of Rationales for LLMs on Natural Language Understanding
Wenhang Shi, Shuqing Bian, Yiren Chen +5
Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by ge…
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
Wenhang Shi, Yiren Chen, Shuqing Bian +6
Prompt engineering is crucial for leveraging the full potential of large language models (LLMs). While automatic prompt optimization offers a scalable alternative to costly manual…
TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection
Qiyu Zhuang, Wei Lu, Shuang Liu +6
Achieving the serializable isolation level, regarded as the gold standard for transaction processing, is costly. Recent studies reveal that adjusting specific query patterns within…
Joint Knowledge Editing for Information Enrichment and Probability Promotion
Wenhang Shi, Yiren Chen, Shuqing Bian +5
Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods f…
GeoTP: Latency-aware Geo-Distributed Transaction Processing in Database Middlewares (Extended Version)
Qiyu Zhuang, Xinyue Shi, Shuang Liu +6
The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed acr…
Conformance Testing of Relational DBMS Against SQL Specifications
Shuang Liu, Chenglin Tian, Jun Sun +6
A Relational Database Management System (RDBMS) is one of the fundamental software that supports a wide range of applications, making it critical to identify bugs within these syst…
A Two-Phase Recall-and-Select Framework for Fast Model Selection
Jianwei Cui, Wenhang Shi, Honglin Tao +2
As the ubiquity of deep learning in various machine learning applications has amplified, a proliferation of neural network models has been trained and shared on public model reposi…
Large Language Model for Table Processing: A Survey
Weizheng Lu, Jing Zhang, Ju Fan +3
Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table quest…
AeonG: An Efficient Built-in Temporal Support in Graph Databases
Jiamin Hou, Zhanhao Zhao, Zhouyu Wang +4
Real world graphs are often dynamic and evolve over time. It is crucial for storing and querying graph evolution in graph databases. However, existing works either suffer from high…