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Quanqing Xu

Publications and source records attributed to Quanqing Xu.

At least 19 recordsLinked to original sources
arXiv2026

Time-Decayed Vector Search in the Rhythm of TANGO: Jointly Modeling Semantic Similarity and Temporal Freshness

Vector search typically measures relevance through semantic similarity under a fixed scoring function. However, in a growing range of applications, relevance may evolve over time, making temporal freshness an additional signal beyond semantic similarity. In this paper, we formalize time-decayed vector search (TDVS), which incorporates continuous temporal decay into the search objective so that relevance is jointly determined by semantic similarity and temporal freshness. We design Score-Preserving Temporal Reduction (STR) that enables existing Maximum Inner Product Search indexes to directly support TDVS. We further present Chronos, a TDVS-native framework that derives an exact metric formulation and introduces Query-Orthogonal TimeLift to control data--data geometry while preserving all query--data scores and rankings. Building on Chronos, we propose TANGO, a hierarchical graph index that adopts layer-specific TimeLift geometries to preserve temporal locality at the base layer while strengthening long-range semantic connectivity in upper layers. TANGO traverses the hierarchy using the exact TDVS score, caches temporal factors to reduce computation, and supports efficient online insertion. Extensive experiments show that TANGO achieves up to 3.5$\times$ higher query throughput and 4.05$\times$ faster index construction than state-of-the-art graph-based competitors. TANGO also maintains its advantage over all competitors across diverse temporal settings and enables efficient online insertion, demonstrating its robustness and practicality.

cs.DB↗
arXiv2026

LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes

Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. State-of-the-art annotators are pre-trained language models (PLMs) fine-tuned on one particular corpus of tables, i.e., a source data lake, and they degrade sharply once deployed on a new (i.e., target) lake, whose tables and semantic type set both differ. Retraining per lake is prohibitive because it demands large volumes of expert annotations. We recast cross-lake adaptation as a knowledge management problem and make the resulting decomposition explicit: relative to a target annotator, a source annotator holds knowledge that must be discarded (source-specific), realigned and reused (shared), or acquired (target-specific). This decomposition exposes which part of the gap a general-purpose LLM can close and which part only target supervision can. Guided by it, we present LakeHopper, which adapts a source annotator under a fixed annotation budget through three coupled mechanisms: label-set realignment that transplants the output layer for shared types, LLM-verified gap discovery that localizes columns the annotator handles unreliably, and cluster-based propagation plus rehearsal fine-tuning that generalizes each flagged column into a labeling batch without erasing shared knowledge. Casting the LLM as a verifier of the annotator's own predictions rather than an annotator keeps every output inside the target type set, so LakeHopper structurally emits no out-of-domain labels, whereas prompted LLMs hallucinate types on 2.7-47.6% of columns. Across three data lake transfers of differing difficulty, LakeHopperlifts three PLM backbones by up to 71.4% relative macro-F1, reaches near-full-data quality with under 6% of the target labels, and matches fine-tuned table LLMs while training 27-131 times faster.

cs.CL↗
arXiv2026

Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients

Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parameter models. Existing heterogeneous federated approaches attempt to bridge this gap through parameter-efficient tuning, model pruning, or knowledge distillation, yet each trades away a critical property, whether full-model memory reduction, architectural self-containedness, or representational fidelity, leaving the core tension unresolved. We propose FedSLM, a parameter-centric framework for federated fine-tuning with heterogeneous compressed clients. FedSLM uses SVD-based decomposition to produce self-contained client models, whose low-rank subspaces form nested manifolds that are structurally compatible for aggregation. It then applies a two-stage protocol that synchronizes lightweight adapters within compression groups and fuses full-rank reconstructions across groups via structural alignment. Finally, a weak-to-strong elicitation step with auxiliary confidence loss transfers the aggregated knowledge to the full-scale server, while an explicit bias--variance trade-off mitigates compression artifacts. We provide theoretical guarantees for adapter-level aggregation, subspace-alignment bounds for cross-group fusion, and a characterization of how the confidence loss mitigates weak-supervision noise. Experiments on natural language and vision--language benchmarks show that FedSLM outperforms existing federated baselines under both IID and non-IID partitions, while client models operate at roughly 50% of the GPU memory required by the full model.

cs.LG↗
arXiv2026

Efficient Multi-round LLM Inference over Disaggregated Serving

With the rapid evolution of Large Language Models (LLMs), multi-round workflows, such as autonomous agents and iterative retrieval, have become increasingly prevalent. However, this raises hurdles for serving LLMs under prefill-decode (PD) disaggregation, a widely adopted paradigm that separates the compute-bound prefill phase and memory-bound decode phase onto individual resources. Specifically, existing systems overlook the interleaved prefill-decode workload pattern in multi-round inference, leading to sub-optimal handling of the incremental prefill workloads and model deployment for the two phases. In this work, we present AMPD, a brand new disaggregated serving framework for multi-round LLM inference. The core of AMPD is to coordinate the prefill workloads based on real-time workloads by adaptively determining where to carry out these workloads and how they are scheduled, in order to maximize service level objective (SLO) attainment. In addition, we tailor a planning algorithm for our scenario, facilitating the deduction of optimal resource allocation and parallel strategies for the two phases. Empirical results demonstrate that AMPD substantially improves SLO attainment compared to state-of-the-art baselines.

cs.DC↗
arXiv2026

DocMaster: A Hierarchical Structure-Aware System for Document Analysis

Leveraging large language models (LLMs) to analyze complex documents -- such as academic papers, technical manuals, and financial reports -- has emerged as a mainstream and critical task in both research and industry. In practice, users must first filter relevant documents from large collections and then conduct in-depth analysis (e.g. question answering) over the selected subset, yet existing systems flatten documents into plain-text chunks, discarding the rich hierarchical structures (sections, tables, figures, equations) and degrading downstream performance. We present DocMaster, a hierarchical structure-aware document analysis system. DocMaster parses documents into hierarchical document trees preserving original layouts and constructs a structure-aware semantic index that enables accurate document filtering and in-depth analysis. We demonstrate DocMaster through an interactive web interface that enables users to upload document collections, construct tree-based and multi-view semantic indices, filter relevant documents via natural-language conditions, and perform follow-up question answering over the filtered results. The source code, data, and demo are available at https://doc-master.github.io/.

cs.DB↗
arXiv2026

HETERQA: Benchmarking Record Retrieval over Multiple Heterogeneous Sources

In emerging systems (e.g., social media and e-commerce platforms), data records are often drawn from heterogeneous sources, such as relational tables, text documents, image repositories, spatial databases, and knowledge graphs. Accordingly, retrieving target records for question-answering (QA) tasks requires us to jointly exploit these heterogeneous sources. However, most existing benchmarks are constructed from individual sources, and only a very few recent benchmarks have considered two or three sources. To alleviate this issue, we introduce HETERQA, a comprehensive benchmark with 857 QA pairs for record retrieval over five heterogeneous sources. HETERQA instantiates this setting with Yelp business records, each of which is grounded by multiple sources. We build HETERQA in an answer-driven manner: candidate records are first initialized with record-field constraints, then enriched through heterogeneous sources, and finally cross-verified across required sources before the natural-language question is retained. We validate the benchmark through contradiction detection and human validation, and further evaluate sparse, dense, hybrid, late-interaction, and agentic retrievers under the same metrics. The results show that HETERQA is challenging: hybrid retrieval achieves the strongest Recall@10, Self-RAG achieves the best MRR@10, and all evaluated methods remain far from saturating the benchmark. These findings indicate that HETERQA provides an effective testbed for record retrieval over heterogeneous sources and leaves substantial room for future retrieval methods. The benchmark dataset and source code are publicly available at https://huggingface.co/datasets/hanchang02/HeterQA and https://github.com/hanchang02/HeterQA, respectively.

cs.IR↗
arXiv2026

Defense against Poisoning Attacks under Shuffle-DP

Differential Privacy (DP) has become the gold standard for protecting individual privacy in data analytics, and the shuffle-DP model has attracted significant attention from both academia and industry due to its favorable balance between privacy and utility. However, existing shuffle-DP protocols rely on a strong assumption: all users behave honestly. In real-world scenarios, adversarial users can exploit this vulnerability through poisoning attacks, compromising both privacy guarantees and the utility of analytical results. While defending against poisoning attacks in the shuffle-DP model has recently gained interest, existing solutions are limited to frequency estimation tasks. To address this issue, we propose the first general defense framework for all union-preserving queries, capable of transforming any shuffle-DP protocol into a version resilient to poisoning attacks. Beyond robust defense against poisoning attacks, our framework achieves high utility of analytical results. Compared to the original shuffle-DP protocol, it retains asymptotically equivalent error in attack-free settings and incurs only a polylogarithmic increase in error when a constant number of attackers are present. We demonstrate the generality of our framework on several common queries, including summation, frequency estimation, and range counting. Experimental results confirm that our approach effectively defends against poisoning attacks while maintaining strong utility and communication efficiency.

cs.CR↗
arXiv2026

VABench: A Comprehensive Benchmark for Audio-Video Generation

Recent advances in video generation have been remarkable, enabling models to produce visually compelling videos with synchronized audio. While existing video generation benchmarks provide comprehensive metrics for visual quality, they lack convincing evaluations for audio-video generation, especially for models aiming to generate synchronized audio-video outputs. To address this gap, we introduce VABench, a comprehensive and multi-dimensional benchmark framework designed to systematically evaluate the capabilities of synchronous audio-video generation. VABench encompasses three primary task types: text-to-audio-video (T2AV), image-to-audio-video (I2AV), and stereo audio-video generation. It further establishes two major evaluation modules covering 15 dimensions. These dimensions specifically assess pairwise similarities (text-video, text-audio, video-audio), audio-video synchronization, lip-speech consistency, and carefully curated audio and video question-answering (QA) pairs, among others. Furthermore, VABench covers seven major content categories: animals, human sounds, music, environmental sounds, synchronous physical sounds, complex scenes, and virtual worlds. We provide a systematic analysis and visualization of the evaluation results, aiming to establish a new standard for assessing video generation models with synchronous audio capabilities and to promote the comprehensive advancement of the field.

cs.CV↗
arXiv2026

Automating Database-Native Function Code Synthesis with LLMs

Database systems incorporate an ever-growing number of functions in their kernels (a.k.a., database native functions) for scenarios like new application support and business migration. This growth causes an urgent demand for automatic database native function synthesis. While recent advances in LLM-based code generation (e.g., Claude Code) show promise, they are too generic for database-specific development. They often hallucinate or overlook critical context because database function synthesis is inherently complex and error-prone, where synthesizing a single function may involve registering multiple function units, linking internal references, and implementing logic correctly. To this end, we propose DBCooker, an LLM-based system for automatically synthesizing database native functions. It consists of three components. First, the function characterization module aggregates multi-source declarations, identifies function units that require specialized coding, and traces cross-unit dependencies. Second, we design operations to address the main synthesis challenges: (1) a pseudo-code-based coding plan generator that constructs structured implementation skeletons by identifying key elements such as reusable referenced functions; (2) a hybrid fill-in-the-blank model guided by probabilistic priors and component awareness to integrate core logic with reusable routines; and (3) three-level progressive validation, including syntax checking, standards compliance, and LLM-guided semantic verification. Finally, an adaptive orchestration strategy unifies these operations with existing tools and dynamically sequences them via the orchestration history of similar functions. Results show that DBCooker outperforms other methods on SQLite, PostgreSQL, and DuckDB (34.55% higher accuracy on average), and can synthesize new functions absent in the latest SQLite (v3.50).

cs.DB↗
arXiv2026

TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framework that provides a novel paradigm for similarity search and achieves superior indexing and query performance. However, the subspace collision framework remains data-agnostic and query-oblivious, resulting in imbalanced index construction and wasted query overhead. In this paper, we address these limitations from two aspects: first, we design a subspace-oriented data transformation mechanism by averaging the entropies computed over each subspace of the transformed data, which ensures balanced subspace partitioning (in an information theoretical sense) and enables data-adaptive subspace collision; second, we present query-aware and scalable query strategies that dynamically allocate overhead for each query and accelerate collision probing within subspaces. Building on these ideas, we propose a novel data-adaptive and query-aware subspace collision method, abbreviated as TaCo, which achieves efficient and accurate ANN search while maintaining an excellent balance between indexing and query performance. Extensive experiments on real-world datasets demonstrate that, when compared to state-of-the-art subspace collision methods, TaCo achieves up to 8x speedup in indexing and reduces to 0.6x memory footprint, while achieving over 1.5x query throughput. Moreover, TaCo achieves state-of-the-art indexing performance and provides an effective balance between indexing and query efficiency, even when compared with advanced methods beyond the subspace-collision paradigm. This paper was published in SIGMOD 2026.

cs.DB↗
arXiv2026

PDET-LSH: Scalable In-Memory Indexing for High-Dimensional Approximate Nearest Neighbor Search with Quality Guarantees

Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on improving the efficiency and accuracy of query phase by designing different query strategies, but pay little attention to improving the efficiency of the indexing phase. They typically fine-tune existing data-oriented partitioning trees to index data points and support their query strategies. However, their strategy to directly partition the multidimensional space is time-consuming, and performance degrades as the space dimensionality increases. In this paper, we design an encoding-based tree called Dynamic Encoding Tree (DE-Tree) to improve the indexing efficiency and support efficient range queries. Based on DE-Tree, we propose a novel LSH scheme called DET-LSH. DET-LSH adopts a novel query strategy, which performs range queries in multiple independent index DE-Trees to reduce the probability of missing exact NN points. Extensive experiments demonstrate that while achieving best query accuracy, DET-LSH achieves up to 6x speedup in indexing time and 2x speedup in query time over the state-of-the-art LSH-based methods. In addition, to further improve the performance of DET-LSH, we propose PDET-LSH, an in-memory method adopting the parallelization opportunities provided by multicore CPUs. PDET-LSH exhibits considerable advantages in indexing and query efficiency, especially on large-scale datasets. Extensive experiments show that, while achieving the same query accuracy as DET-LSH, PDET-LSH offers up to 40x speedup in indexing time and 62x speedup in query answering time over the state-of-the-art LSH-based methods. Our theoretical analysis demonstrates that DET-LSH and PDET-LSH offer probabilistic guarantees on query answering accuracy. This paper was published in TKDE.

cs.DB↗
arXiv2026

MCI-SQL: Text-to-SQL with Metadata-Complete Context and Intermediate Correction

Text-to-SQL aims to translate natural language queries into SQL statements. Existing methods typically follow a pipeline of pre-processing, schema linking, candidate SQL generation, SQL alignment, and target SQL selection. However, these methods face significant challenges. First, they often struggle with column filtering during schema linking due to difficulties in comprehending raw metadata. Also, the candidate SQL generation process often suffers from reasoning errors, which limits accuracy improvements. To address these limitations, we propose a framework, called MCI-SQL, to efficiently and precisely generate SQL queries. Specifically, we assign metadata-complete contexts to each column, which significantly improves the accuracy of column filtering for schema linking. Also, for candidate SQL generation, we propose an intermediate correction mechanism that validates SQL queries and revises errors in a timely way. Moreover, we also propose effective optimizations in subsequent SQL alignment and selection phases, which further enhance the performance. Experiments on the widely-used BIRD benchmark show that MCI-SQL achieves execution accuracy of 74.45% on the development set and 76.41% on the test set, surpassing current published state-of-the-art results. In addition, we manually identify and correct 412 samples in the BIRD dataset, forming a new version named BIRD-clear, which is released together with our code on GitHub. We also evaluate our methods on BIRD-clear and find that MCI-SQL outperforms baselines by 8.47 percentage points in execution accuracy, further demonstrating the effectiveness and reliability of our framework.

cs.DB↗
arXiv2026

The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI

Modern AI and vector search are rapidly converging, forming a promising research frontier in intelligent information systems. On one hand, advances in AI have substantially improved the semantic accuracy and efficiency of vector search, including learned indexing structures, adaptive pruning strategies, and automated parameter tuning. On the other hand, powerful vector search techniques have enabled new AI paradigms, notably Retrieval-Augmented Generation (RAG), which effectively mitigates challenges in Large Language Models (LLMs) like knowledge staleness and hallucinations. This mutual reinforcement establishes a virtuous cycle where AI injects intelligence and adaptive optimization into vector search, while vector search, in turn, expands AI's capabilities in knowledge integration and context-aware generation. This tutorial provides a comprehensive overview of recent research and advancements at this intersection. We begin by discussing the foundational background and motivations for integrating vector search and AI. Subsequently, we explore how AI empowers vector search (AI4VS) across each step of the vector search pipeline. We then investigate how vector search empowers AI (VS4AI), with a particular focus on RAG frameworks that integrate dynamic, external knowledge sources into the generative process of LLMs. Furthermore, we analyze end-to-end co-optimization strategies that fully unlock the potential of the ``virtuous cycle" between vector search and AI. Finally, we highlight key challenges and future research opportunities in this emerging area. This paper was published in ICDE 2026.

cs.DB↗
arXiv2026

A Tree-Structured Two-Phase Commit Framework for OceanBase: Optimizing Scalability and Consistency

Modern distributed databases face challenges in achieving transactional consistency across distributed partitions. Traditional two-phase commit (2PC) protocols incur high coordination overhead and latency, and require complex recovery for dynamic partition transfers. This paper introduces a novel tree-shaped 2PC framework for OceanBase that leverages single-machine log streams to address these challenges through three innovations. First, we propose log streams as atomic participants, replacing partition-level coordination. By treating each log stream as the commit unit, a transaction spanning $N$ co-located partitions interacts with one participant, reducing coordination overhead by orders of magnitude (e.g., 99 percent reduction for $N=100$). Second, we design a tree-shaped 2PC protocol with coordinator-rooted DAG topology that dynamically handles partition transfers by recursively constructing commit trees. When a partition migrates during a transaction, the protocol embeds migration contexts as leaf nodes, eliminating explicit participant list updates, resolving circular dependencies, and ensuring linearizable commits under topology changes. Third, we introduce prepare-unknown and trans-unknown states to prevent consistency violations when participants lose context. These states signal uncertainty during retries, avoiding erroneous aborts from so-called lying participants while isolating users from ambiguity. Experimental evaluation demonstrates performance approaching that of single-machine transactions, with reduced latency and bandwidth consumption, validating the framework's effectiveness for modern distributed databases.

cs.DB↗
arXiv2026

OceanBase Bacchus: a High-Performance and Scalable Cloud-Native Shared Storage Architecture for Multi-Cloud

Although an increasing number of databases now embrace shared-storage architectures, current storage-disaggregated systems have yet to strike an optimal balance between cost and performance. In high-concurrency read/write scenarios, B+-tree-based shared storage struggles to efficiently absorb frequent in-place updates. Existing LSM-tree-backed disaggregated storage designs are hindered by the intricate implementation of cross-node shared-log mechanisms, where no satisfactory solution yet exists. This paper presents OceanBase Bacchus, an LSM-tree architecture tailored for object storage provided by cloud vendors. The system sustains high-performance reads and writes while rendering compute nodes stateless through shared service-oriented PALF (Paxos-backed Append-only Log File system) logging and asynchronous background services. We employ a Shared Block Cache Service to flexibly utilize cache resources. Our design places log synchronization into a shared service, providing a novel solution for log sharing in storage-compute-separated databases. The architecture decouples functionality across modules, enabling elastic scaling where compute, cache, and storage resources can be resized rapidly and independently. Through experimental evaluation using multiple benchmark tests, including SysBench and TPC-H, we confirm that OceanBase Bacchus achieves performance comparable to or superior to that of HBase in OLTP scenarios and significantly outperforms StarRocks in OLAP workloads. Leveraging Bacchus's support for multi-cloud deployment and consistent performance, we not only retain high availability and competitive performance but also achieve substantial reductions in storage costs by 59% in OLTP scenarios and 89% in OLAP scenarios.

cs.DB↗
arXiv2026

Building an OceanBase-based Distributed Nearly Real-time Analytical Processing Database System

The growing demand for database systems capable of efficiently managing massive datasets while delivering real-time transaction processing and advanced analytical capabilities has become critical in modern data infrastructure. While traditional OLAP systems often fail to meet these dual requirements, emerging real-time analytical processing systems still face persistent challenges, such as excessive data redundancy, complex cross-system synchronization, and suboptimal temporal efficiency. This paper introduces OceanBase Mercury as an innovative OLAP system designed for petabyte-scale data. The system features a distributed, multi-tenant architecture that ensures essential enterprise-grade requirements, including continuous availability and elastic scalability. Our technical contributions include three key components: (1) an adaptive columnar storage format with hybrid data layout optimization, (2) a differential refresh mechanism for materialized views with temporal consistency guarantees, and (3) a polymorphic vectorization engine supporting three distinct data formats. Empirical evaluations under real-world workloads demonstrate that OceanBase Mercury outperforms specialized OLAP engines by 1.3X to 3.1X speedup in query latency while maintaining sub-second latency, positioning it as a groundbreaking AP solution that effectively balances analytical depth with operational agility in big data environments.

cs.DB↗
arXiv2026

LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts

Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from limited video duration, low-quality captions, and coarse annotation granularity, which hinder the evaluation of advanced video-text retrieval methods. To address these limitations, we introduce LoVR, a benchmark specifically designed for long video-text retrieval. LoVR contains 467 long videos and over 40,804 fine-grained clips with high-quality captions. To overcome the issue of poor machine-generated annotations, we propose an efficient caption generation framework that integrates VLM automatic generation, caption quality scoring, and dynamic refinement. This pipeline improves annotation accuracy while maintaining scalability. Furthermore, we introduce a semantic fusion method to generate coherent full-video captions without losing important contextual information. Our benchmark introduces longer videos, more detailed captions, and a larger-scale dataset, presenting new challenges for video understanding and retrieval. Extensive experiments on various advanced embedding models demonstrate that LoVR is a challenging benchmark, revealing the limitations of current approaches and providing valuable insights for future research. We release the code and dataset link at https://lovrbench.github.io/

cs.CV↗
arXiv2026

Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization

Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential factors for investors. To tackle this issue, we introduce a Multi-Task Learning (MTL) framework for stock recommendation, \textbf{M}omentum-\textbf{i}ntegrated \textbf{M}ulti-task \textbf{Stoc}k \textbf{R}ecommendation with Converge-based Optimization (\textbf{MiM-StocR}). To improve the model's ability to capture short-term trends, we incorporate a momentum line indicator in model training. To prioritize top-performing stocks and optimize investment allocation, we propose a listwise ranking loss function called Adaptive-k ApproxNDCG. Moreover, due to the volatility and uncertainty of the stock market, existing MTL frameworks face overfitting issues when applied to stock time series. To mitigate this issue, we introduce the Converge-based Quad-Balancing (CQB) method. We conducted extensive experiments on three stock benchmarks: SEE50, CSI 100, and CSI 300. MiM-StocR outperforms state-of-the-art MTL baselines across both ranking and profitability evaluations.

q-fin.ST↗