With the development of large language models (LLMs) in the field of programming, intelligent programming coaching systems have gained widespread attention. However, most research focuses on repairing the buggy code of programming learners without providing the underlying causes of the bugs. To address this gap, we introduce a novel task, namely LPR (Learner-Tailored Program Repair). We then propose a novel and effective framework, LSGen (Learner-Tailored Solution Generator), to enhance program repair while offering the bug descriptions for the buggy code. In the first stage, we utilize a repair solution retrieval framework to construct a solution retrieval database and then employ an edit-driven code retrieval approach to retrieve valuable solutions, guiding LLMs in identifying and fixing the bugs in buggy code. In the second stage, we propose a solution-guided program repair method, which fixes the code and provides explanations under the guidance of retrieval solutions. Moreover, we propose an Iterative Retrieval Enhancement method that utilizes evaluation results of the generated code to iteratively optimize the retrieval direction and explore more suitable repair strategies, improving performance in practical programming coaching scenarios. The experimental results show that our approach outperforms a set of baselines by a large margin, validating the effectiveness of our framework for the newly proposed LPR task.

Natural language to SQL (NL2SQL) provides an intuitive interface for querying structured data, yet real user questions are often noisy, ambiguous, and weakly grounded to database semantics.As a result, token-level schema linking and single-pass SQL decoding can be brittle: small misunderstandings in language or schema grounding may propagate into incorrect generation.We present QBridge, an agentic, feedback-driven NL2SQL framework based on a Refined Gold Query Paradigm, which bridges natural language and SQL via Gold Query-a structured, SQL-aligned intermediate representation.A core insight of QBridge is Distilled Back-Translation (DBT) for SL-independent rewriting.DBT converts SQL-grounded supervision into execution-verified Gold-Querystyle rewrites from a teacher model, and distills a lightweight, plug-and-play rewriter that generates schema-aware rewrites without requiring explicit schema linking at inference.QBridge then (i) verifies and conservatively refines the rewrite into a high-fidelity Refined Gold Query, and (ii) refines the generated SQL with dual feedback from execution validity and semantic consistency, enabling interpretable self-correction while remaining compatible with diverse SQL backbones.Extensive experiments on Spider, BIRD, and three robustness variants demonstrate that QBridge consistently improves zero-shot NL2SQL, outperforming strong prompting and agentic baselines while showing strong robustness and generalization.Code and data are available at https: //github.com/WannaBSteve/QBridge.

Detecting early brain anomalies is crucial for patient prognosis and recovery, but obtaining expert-annotated data is challenging, especially for clinically silent early brain anomalies. Unsupervised brain anomaly detection, which identifies anomalous regions by modeling normal brain patterns, has gained interest for its label efficiency. However, the inherent variability in normal brains and subtle anomalies that closely resemble normal tissue pose challenges for traditional autoencoders in distinguishing anomalies. Denoising AutoEncoder (DAE) methods have been explored to enhance the model's ability, while their success hinges on effective noise generation strategies. In this paper, we introduce a novel, structure-preserving noise generation scheme based on cross-modal CutMix, aiming to enhance the diversity of noise patterns while preserving the anatomical structure of the brain. To enhance the robustness of DAE learning, we propose an ensemble approach featuring dual experts, each incorporating distinct scale of noise. This dual-expert scheme effectively amplifies reconstruction errors in anomalous regions and suppresses false alarms in healthy areas. Additionally, we propose an anatomically-aware bidirectional consistency loss to ensure high-fidelity reconstruction at the regional level, using superpixels for anatomy perception and bidirectional distillation for reliable knowledge transfer. Extensive experiments across two different settings demonstrate the effectiveness and generalization ability of our proposed method.

A precise workload forecaster is the key to effective resource management, system scalability, and overall operational efficiency in cloud environments. However, real-world cloud systems frequently operate in dynamic and unpredictable settings, causing workloads that exhibit significant diversity and fluctuations. To address these problems, we introduce OMCR, a novel online multivariate forecaster for cloud resource management, that overcomes the limitations of existing static forecasting methods through online learning. OMCR integrates long-term memory with a rapid response mechanism to short-term changes in cloud systems, while also considering the impact of multivariate relationships on workload prediction. OMCR minimizes its reliance on historical data, thereby reducing training difficulty and maintaining lower prediction loss in the long run. OMCR also offers an adaptive approach to forecasting peak workloads in a certain time span, which helps cloud resource management. Experimental results demonstrate the superior performance of our proposed framework compared to state-of-the-art methods in MAE and MSE metrics when forecasting cloud workloads.

Generation-based testing techniques have proven effective in detecting logic bugs in DBMS, often stemming from the improper implementation of query optimizers. However, existing generation-based debugging tools predominantly rely on random testing, which tends to overlook critical error-prone areas such as multi-table joining and grouped aggregation. In this article, we propose TQS, a novel testing framework targeted at detecting logic bugs arising from SPJG (Select-Project-Join-Group By) query optimizations. Given a target DBMS, TQS achieves the goal with two key components: Data-guided Schema and Query Generation (DSG) and Knowledge-guided Query Space Exploration (KQE). DSG addresses the key challenge of multi-table query debugging: how to generate ground-truth (query, result) pairs for verification. DSG utilizes data derived from dimensionally aggregated data cubes, which store data of grouped metric columns. It maps data from data cubes to a wide table, applies database normalization techniques to the wide table to generate a testing schema and maintains a bitmap index for result tracking. To improve debug efficiency, DSG also artificially inserts some noises into the generated data. To avoid repetitive query space search, KQE guides the generation of error-prone cubes, and forms the problem as isomorphic graph set discovery and combines the graph embedding and weighted random walk for query generation. We evaluated TQS on four popular DBMSs: MySQL, MariaDB, TiDB, and PolarDB. Experimental results show that TQS is effective in finding logic bugs of SPJG query optimization in database management systems. It successfully detected 226 bugs within 24 hours, including 63 bugs in MySQL, 52 in MariaDB, 68 in TiDB, and 43 in PolarDB, respectively.

Automated Program Repair (APR) is a task to automatically generate patches for the buggy code. However, most research focuses on generating correct patches while ignoring the consistency between the fixed code and the original buggy code. How to conduct adaptive bug fixing and generate patches with minimal modifications have seldom been investigated. To bridge this gap, we first introduce a novel task, namely AdaPR (Adaptive Program Repair). We then propose a two-stage approach AdaPatcher (Adaptive Patch Generator) to enhance program repair while maintaining the consistency. In the first stage, we utilize a Bug Locator with self-debug learning to accurately pinpoint bug locations. In the second stage, we train a Program Modifier to ensure consistency between the post-modified fixed code and the pre-modified buggy code. The Program Modifier is enhanced with a location-aware repair learning strategy to generate patches based on identified buggy lines, a hybrid training strategy for selective reference and an adaptive preference learning to prioritize fewer changes. The experimental results show that our approach outperforms a set of baselines by a large margin, validating the effectiveness of our two-stage framework for the newly proposed AdaPR task.

Large language models (LLMs) have significantly advanced the performance of various natural language processing tasks, including text-to-SQL. Current LLM-based text-to-SQL schemes mainly focus on improving the understanding of natural language questions (NLQs) or refining the quality of generated SQLs. While these strategies are effective, they often address specific, nuanced aspects. In contrast, humans approach text-to-SQL with a holistic view, applying transitional logical reasoning across multiple steps to arrive at the final answer. We believe LLMs can leverage human cognitive processes to achieve greater accuracy in text-to-SQL. In this paper, we present COGSQL, a framework featuring a suite of tailored models and strategies aimed at replicating human cognitive processes for enhanced LLM-based text-to-SQL. COGSQL consists of three key modules: (1) SQL preparation: we employ a coarse-to-fine schema linking and syntax keyword prediction, akin to how human recall and align key concepts for better understanding. (2) SQL generation: we introduce a concept-enhanced chain-of-thought prompting, enhancing NLQ interpretation and SQL composition of LLMs, similar to humans drafting SQL query. (3) SQL correction: we develop NLQ consistency and result consistency techniques to correct various errors, mirroring how humans evaluate and refine reasoning. We conduct extensive experiments using diverse benchmarks and LLMs. The results and analysis verify the effectiveness and generalizability of COGSQL.

Sequence distance computation is a critical and fundamental task in many fields, such as bioinformatics, and time series analysis. Traditional functions for computing the distance between sequences are often based on dynamic programming to find a globally optimal alignment, which has quadratic complexity and is difficult to parallelize, thus limiting their application in large-scale datasets with long sequences. To solve this problem, various fields have designed some specialized models to approximate these distance functions inspired by deep representation learning, i.e., projecting the sequence into a geometric embedding space through an embedding function, so that the distance between sequences can be approximated by the distance in the high-dimensional embedding space, thereby reducing the quadratic complexity to linear. However, we note that even though the element types in sequence and distance functions are different across various fields, the core problem that needs to be solved remains the same. In this paper, we attempt to unify the sequence distance computation approximation from various fields and propose GnesDA. Specifically, we first unify the input representation of sequences in which the element type is the symbol and numeric values. We then encode the sequence using a convolutional block and a Transformer block sequentially, which can effectively capture local patterns and long dependencies respectively. Extensive experiments on four distance functions as well as four large-scale real-world datasets demonstrate that GnesDA achieves state-of-the-art in terms of both versatility and effectiveness. For the task of similarity retrieval, GnesDA can improve the edit distance, NW distance, DTW, and EDR by an average of 10.55%, 6.67%, 4.51%, and 12.00% on all metrics.

The increasing demand for deep neural inference within database environments has driven the emergence of AI?native DBMSs. However, existing solutions either rely on model-centric designs requiring developers to manually select, configure, and maintain models, resulting in high development overhead, or adopt task-centric AutoML approaches with high computational costs and poor DBMS integration. We present MorphingDB, a task-centric AI-native DBMS that automates model storage, selection, and inference within PostgreSQL. To enable flexible, I/O-efficient storage of deep learning models, we first introduce specialized schemas and multi-dimensional tensor data types to support BLOB-based all-in-one and decoupled model storage. Then we design a transfer learning framework for model selection in two phases, which builds a transferability subspace via offline embedding of historical tasks and employs online projection through feature-aware mapping for real-time tasks. To further optimize inference throughput, we propose pre-embedding with vectoring sharing to eliminate redundant computations and DAG-based batch pipelines with cost-aware scheduling to minimize the inference time. Implemented as a PostgreSQL extension with LibTorch, MorphingDB outperforms AI-native DBMSs (EvaDB, Madlib, GaussML) and AutoML platforms (AutoGluon, AutoKeras, AutoSklearn) across nine public datasets, encompassing series, NLP, and image tasks. Our evaluation demonstrates a robust balance among accuracy, resource consumption, and time cost in model selection and significant gains in throughput and resource efficiency.

Querying and analyzing data in data lakes requires substantial manual intervention, including numerous data preprocessing steps, and often demands complex domain expertise. However, the advent of Large Language Models (LLMs) has introduced a promising solution to these challenges by providing a unified framework for interpreting the heterogeneous datasets within data lakes. In this paper, we demonstrate QueryArtisan, a novel LLM-powered analytical system tailored for data lakes. It enables users to issue complex queries in natural language without the need for domain-specific expertise. The system automatically executes user-submitted queries and performs data processing and analysis based on the query results. QueryArtisan extends beyond traditional ETL (Extract, Transform, Load) processes by generating just-in-time code customized for dataset-specific tasks. A suite of heterogeneous operators is developed to process data across various modalities. In addition, a cost-based query optimization mechanism is integrated to improve the efficiency of the generated code. Furthermore, QueryArtisan can dynamically instantiate multiple agents in response to user-defined analytical requirements to perform further in-depth analysis of the retrieved data.

Many GaussDB customers, particularly small and medium-sized enterprises (SMEs), require high transaction throughput with occasional analytical queries. HTAP systems that deploy both OLTP and OLAP engines on a single server to manage hybrid workloads have become increasingly popular among customers for achieving high cost-efficiency and data freshness. However, co-locating these systems can lead to resource contention, particularly for memory, potentially degrading overall system performance and causing Service-Level Agreements (SLA) violations. To address this issue, we propose T 2 (Twisted Twin), an adaptive memory management approach that dynamically allocates memory between OLTP and OLAP components. This approach ensures OLTP meets SLA while optimizing the efficiency of OLAP query processing. However, this is non-trivial, as memory allocation triggers a cascade of effects, including in-memory column selection and data synchronization, both critical in HTAP systems. To overcome these challenges, we introduce a Bayesian optimization framework tailored for fluctuating workloads that adjusts memory allocation responsively. Experiments conducted on the real-world HTAP system, GaussDB-HTAP, demonstrate the effectiveness and efficiency of T 2 .

Log-based anomaly detection plays a crucial role in ensuring the reliability of systems. While deep learning-based small detection models (SDMs) are efficient, the large language models (LLMs) are accurate and capable of providing explanations. Intuitively, a compelling question arises: Can we seamlessly combine the advantages of both approaches? In this work, we delve into this underexplored research direction and propose CoLA, a novel collaborative log anomaly detection framework. During collaborative inference, an SDM serves as a filter to select potentially anomalous instances, while a downstream LLM acts as an expert to detect anomalies, offer explanations, and refine the SDM. Extensive experiments on three large real-world datasets demonstrate that CoLA significantly outperforms state-of-the-art methods in terms of effectiveness, efficiency, and explainability, while also greatly reducing labor costs.

Auto-scaling is crucial for achieving elasticity in cloud databases as well as other cloud systems. Predictive auto-scaling, which leverages forecasting techniques to adjust resources based on predicted workload, has been widely adopted. However, the inherent inaccuracy of forecasting presents a significant challenge, potentially causing resource under-provisioning. To address this challenge, we propose robust predictive auto-scaling that considers the uncertainty in forecasts. Unlike previous predictive approaches that rely on single-valued forecasts, we leverage probabilistic forecasting techniques to generate quan-tile forecasts, providing a more comprehensive understanding of the potential future workloads. By formulating the auto-scaling problem as a robust optimization problem, we enable the implementation of auto-scaling strategies with customizable levels of robustness, which can be determined by considering various quantile levels of forecasts. Moreover, we enhance the adaptability of our strategy by incorporating different quantile levels through-out the entire decision horizon, allowing for dynamic adjustments in the conservatism of our auto-scaling decisions. This enables us to strike a balance between resource efficiency and system robustness. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving robust auto-scaling in cloud databases, while maintaining reasonable resource efficiency.

Scene text spotting plays a pivotal role in image understanding.However, building a robust model for such a task necessitates substantial annotated data.Various efforts have been made to reduce the burden of extensive data labeling.In this paper, we focus on the minimum labor cost approach that solely relies on text-only annotations.Under this weakly supervised paradigm, existing methods encounter intrinsic difficulties since the location information is not available for training.To compensate, these methods often employ attention maps generated from models pre-trained on tasks such as text recognition or classification to predict spatial information.This approach, however, impedes the possibility of comprehensive end-to-end training and does not ensure optimal performance outcomes.What's more, the attention map for a single word tends to focus on the distinguishing areas, which often yields location predictions with suboptimal boundaries.To overcome these limitations, we introduce an innovative methodology that integrates pseudo-label generation to enable end-to-end training of the spotting network that optimizes text recognition and location estimation at the same time called TWIST.During the training, to address the problem associated with incomplete attention maps and obtain pseudo-labels that can cover the whole word, TWIST treats characters as elemental units.So the pseudo-label for each given text instance is generated by aggregating the inferred locations of their constituent characters, through a masked character prediction task.Then the generated pseudo-labels with corresponding textual content are used to further optimize the parameters of the spotting network.This integrated approach facilitates end-to-end training and achieves new state-of-the-art results in several public detection and end-to-end recognition benchmarks under text-only supervision.

Query processing over data lakes is a challenging task, often requiring extensive data pre-processing activities such as data cleaning, transformation, and loading. However, the advent of Large Language Models (LLMs) has illuminated a new pathway to address these complexities by offering a unified approach to understanding the diverse datasets submerged in data lakes. In this paper, we introduce QueryArtisan, a novel LLM-powered analytic tool specifically designed for data lakes. QueryArtisan transcends traditional ETL (Extract, Transform, Load) processes by generating just-intime code for dataset-specific queries. It eliminates the need for an intermediary schema, enabling users to query the data lake directly using natural language. To achieve this, we have developed a suite of heterogeneous operators capable of processing data across various modalities. Additionally, QueryArtisan incorporates a cost model-based query optimization technique, significantly enhancing its code generation capabilities for efficient query resolution. Our extensive experimental evaluations, conducted with real-life datasets, demonstrate that QueryArtisan markedly outperforms existing solutions in terms of effectiveness, efficiency and usability.

In the era of big data, data annotation is integral to numerous applications. However, it is widely acknowledged as a laborious and time-consuming process, significantly impeding the scalability and efficiency of data-driven applications. To reduce the human cost, we demonstrate CORAL, a collaborative automatic labeling system driven by large language models (LLMs), which achieves high-quality annotation with the least human effort. Firstly, CORAL employs LLM to automatically annotate vast datasets, generating coarse-grained labels. Subsequently, a weakly-supervised learning module trains small language models (SLMs) using noisy label learning techniques to distill accurate labels from LLM's annotations. It also allows statistical analysis of model outcomes to identify potentially erroneous labels, reducing the human cost of error detection. Furthermore, CORAL supports iterative refinement by LLMs and SLMs using manually corrected labels, thereby ensuring continual enhancement in annotation quality and model performance. A visual interface enables annotation process monitoring and result analysis.

Generation-based testing techniques have shown their effectiveness in detecting logic bugs of DBMS, which are often caused by improper implementation of query optimizers. Nonetheless, existing generation-based debug tools are limited to single-table queries and there is a substantial research gap regarding multi-table queries with join operators. In this paper, we propose TQS, a novel testing framework targeted at detecting logic bugs derived by queries involving multi-table joins. Given a target DBMS, TQS achieves the goal with two key components: Data-guided Schema and Query Generation (DSG) and Knowledge-guided Query Space Exploration (KQE). DSG addresses the key challenge of multi-table query debugging: how to generate ground-truth (query, result) pairs for verification. It adopts the database normalization technique to generate a testing schema and maintains a bitmap index for result tracking. To improve debug efficiency, DSG also artificially inserts some noises into the generated data. To avoid repetitive query space search, KQE forms the problem as isomorphic graph set discovery and combines the graph embedding and weighted random walk for query generation. We evaluated TQS on four popular DBMSs: MySQL, MariaDB, TiDB and PolarDB. Experimental results show that TQS is effective in finding logic bugs of join optimization in database management systems. It successfully detected 115 bugs within 24 hours, including 31 bugs in MySQL, 30 in MariaDB, 31 in TiDB, and 23 in PolarDB respectively.

Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.

Recently, the learning-based models are shown to outperform the conventional methods for many database tasks such as cardinality estimation, join order selection and performance tuning. However, most existing learning-based methods adopt the one-hot encoding for SQL query representation, unable to catch complicated semantic context, e.g. structure of query, database schema definition and distribution variance of columns. To address such above problem, we propose a novel pre-trained SQL representation model, called PreQR, which extends the language representation approach to SQL queries. We propose an automaton to encode the query structures, and apply a graph neural network to encode database schema information conditioned on the query. A new SQL encoder is then established by adopting the attention mechanism to support on-the-fly query-aware schema linking. Experimental results on real datasets show that replacing the one-hot encoding with our query representation can significantly improve the performances of existing learning-based models on several database tasks.

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with a well-defined performance bound. The success of LSH family mainly depends on the design of randomly projected hash functions. However, instead of randomly drawing hash functions from a conventional hashing family such as Gaussian projection for Euclidean space, we argue that whether there could be a set of data sensitive hashing functions with higher capacity to distinguish nearby points and far away points, which could have rigorous performance guarantee like conventional LSH. To this end, we propose a learning to tune framework, called LSH-tuning, which consists of a pruning model and a learning to rank model. The pruning model reduces the total number of hash tables to maximize the separating capacity on the given data distribution and minimize the storage overhead. The learning to rank model ranks hash tables based on their effectiveness on NN retrieval. We also have a theoretic model that guides us to gradually search more hash tables and probe nearby buckets. Extensive experiments with real-world data demonstrate that LSH-tuning is capable of outperforming existing proposals with respect to both efficiency and storage overhead.