arXiv subjects

Jianjun Chen

Publications and source records attributed to Jianjun Chen.

At least 19 recordsLinked to original sources

arXiv2026

RemoteANN: A Native Vector Search Engine for Cloud Object Storage

Vector search engines built on cloud object storage have drawn growing attention from both academia and industry. These systems typically adopt a disaggregated storage-compute architecture, persisting the primary vector payload in object stores (e.g., Amazon S3) while retaining compact index metadata on compute nodes. However, conventional vector indexing structures are not natively tailored to the pronounced network latency and per-request access costs of cloud object stores, resulting in network I/O amplification, degraded query throughput, and higher API expenses. To resolve these challenges, we present RemoteANN, a vector search engine explicitly co-designed for cloud object storage across indexing and querying. To mitigate the dominant network I/O bottleneck, RemoteANN couples an Inverted File (IVF) index with RaBitQ quantization, further optimized by inter-cluster and intra-cluster data reordering for improved data locality. Building on this tailored data layout, RemoteANN's query engine combines cluster pruning, fine-grained Range-GET requests, request merging, and local NVMe SSD caching, dramatically reducing discrete storage requests and network transfer volume without sacrificing accuracy. To address the index construction bottleneck, we further develop QGKMeans, which substantially reduces construction latency without compromising clustering quality. Extensive evaluations on real-world datasets demonstrate that, compared with representative baselines, RemoteANN achieves up to a $5.9\times$ throughput speedup while issuing up to $16.1\times$ fewer object storage requests at 97% target recall.

cs.DB↗

arXiv2026

Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost

Many potential future events have devastating consequences, from the onset of earthquakes to financial black swan events. Accurate predictions of their probability of occurrence are vital. Yet as the stochastic systems underlying such events become increasingly complex, these probabilities can depend on ever-growing amounts of past data, which require ever more memory to track. The accuracy of our estimates is ultimately limited by both computational and memory constraints; the former to reduce sampling error, the latter to avoid sampling bias. Traditional quantum algorithms -- such as amplitude estimation -- address only the former, and become untenable especially in near-future quantum devices where quantum memory is a premium. Here, we offer quantum-algorithmic means to reduce systematic bias while maintaining a quadratic speed-up. Our results thus advance the timeline for the practical deployment of quantum technologies, and illustrate their additional benefits when simulating processes of growing complexity.

quant-ph↗

arXiv2026

Spatiotemporal quantum advantages for rare-event sampling

In a world fraught with uncertainty, it behooves us to consider the most disastrous possible outcomes, in order that we can best mitigate against them. Often these are extreme risk events -- of high consequence, but low probability. Yet, the rarity of such events limits our ability to study them; there is a paucity of past occurrences to draw upon, and sampling such rare events from models is typically costly in time. Here, we introduce an approach for rare-event sampling based on quantum stochastic models. This integrates the memory advantages of quantum stochastic modelling -- and its associated efficient means of preparing quantum sample states -- with the `speed-ups' of quantum amplitude amplification, to yield simultaneous reductions in spatial and temporal computational complexity compared to classical Monte Carlo-based approaches.

quant-ph↗

arXiv2026

A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection

Air-ground cross-view referring person detection is a necessary component in the language-to-perception-to-control chain of collective embodied intelligence, grounding a language command into the same physical target before ground and aerial agents can coordinate downstream actions. Existing referring expression comprehension and open-vocabulary grounding methods do not jointly account for cross-view identity consistency, making them insufficient for Air-Ground Cross-View Referring Person Detection (AGCV-RPD), which involves similar pedestrian distractors, weak aerial appearance cues, and cross-view identity consistency. To study this problem, we introduce Air-Ground Paired Identity-Aware Referring (A-PAIR), the first comprehensive AGCV-RPD benchmark, containing 22,137 cross-view referring samples. To construct A-PAIR efficiently, we propose Factorized Annotation and Referential Alignment (FARA), a semi-automatic annotation framework that generates factorized referring descriptions and identity-consistency supervision at reduced cost. We propose Identity-Consistent Referring Grounding (ICRG), a framework that combines factorized referential grounding, candidate-completeness supervision, and cross-view consistency calibration for joint air-ground pair selection. ICRG improves ground, aerial, and pair-level detection over strong baselines, increasing pair F1 from 16.65% to 22.28%. These results show that AGCV-RPD requires paired detection and identity-consistent reasoning.

cs.CV↗

arXiv2026

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing SQL correction approaches either rely on large-scale, high-quality training data with substantial overhead, or adopt single-path agentic workflows that are brittle to early mistakes and prone to error propagation. To develop a practical SQL correctness system for industrial scenarios, we present a training-free framework that formulates SQL correction as a plan-guided, tree-structured debugging process. By maintaining multiple correction strategies and enabling backtracking, the framework mitigates error accumulation during iterative refinement. We further integrate execution-based verification and clause-level diagnostic tools to support strategy pruning and precise error localization. We evaluate the system on the BIRD-Critic benchmark and observe consistent accuracy gains over strong LLM backbones and representative agent-based baselines, achieving a 9.42% improvement over the previous state-of-the-art method. The framework is also deployed in the Torch Log Service (TLS) of Volcano Engine to support an online Text-to-TLS API. In production, it improves execution accuracy from 36.77% to 53.61% on real user queries with a representative strong LLM backbone (GPT-5). These results demonstrate the effectiveness and stability of our approach in real-world deployments.

cs.AI↗

arXiv2026

Towards a new paradigm of scientific discovery with socialized artificial intelligence

Scientific discovery has advanced through successive transformations in the organization of knowledge. Observation and experimentation established the empirical foundations of science. Theory made it possible to derive general principles from particular phenomena. Computation extended inquiry into systems beyond direct observation, while data-intensive methods opened new spaces of pattern and prediction. Science now confronts a different frontier. The central challenge is no longer simply to produce more information, but to organize expanding knowledge, reasoning, and evidence into a coherent process of discovery. Here, we introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence. BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery. It connects persistent knowledge, collective reasoning, empirical validation, and human judgment within a continuous research lifecycle, transforming fragmented activities into a cumulative process of inquiry, criticism, and revision. The central premise of BLAZE is that scientific intelligence does not arise from computation alone. It emerges from the sustained interaction among knowledge, hypotheses, experiments, and collective verification. By organizing humans and machines within a shared scientific process, BLAZE makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility. Socialized scientific intelligence may provide a foundation for the next era of science. Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.

cs.AI↗

arXiv2026

MSCM-net: A hyperspectral image classiffcation method based on multi-scale convolution and Mamba

Hyperspectral imaging is widely used in remote sensing and engineering. Therefore, research on its classification methods is crucial. While CNN and Transformer-based methods have advanced, they still face locality constraints and high computational complexity. To address these issues, we propose an innovative hyperspectral image classification model, MSCM-net. Specifically, first of all, a model architecture combining multi-scale CNN and Mamba is proposed. It consists of a multi-scale feature extraction module (MCSE) and multiple stacked Mamba blocks, which integrates the local feature extraction capability of multi-scale CNN and the long sequence modeling advantage of Mamba. Secondly, the proposed MCSE module consists of multi-scale convolution and SENet. Convolution kernels of different scales extract local information with different receptive fields, enhancing the fusion of spatial and spectral information. Meanwhile, the SENet enables the model to automatically learn the importance of each channel in the multi-scale features. Furthermore, we also propose a dual-branch feature aggregation module, which further effectively extracts and integrates the spectral information contained in the central pixel and the spatial information in the surrounding pixels. Our model has undergone numerous experiments on three widely used benchmark datasets. The experimental results show that MSCM-net can achieve advanced classification performance while reducing computational complexity.

cs.CV↗

arXiv2026

Galactic HII regions in LAMOST Medium-Resolution Spectroscopic Survey of Nebulae

Based on LAMOST Medium-Resolution Spectroscopic Survey of Nebulae (MRS-N) data and WISE Galactic HII region catalog, we construct a sample of 280 Galactic HII regions and candidates in the Outer Galaxy (80$^{\circ}$ $\lesssim$ l $\lesssim$ 220$^{\circ}$). Using MRS-N optical spectra, we measure four emission lines (H$α$, [NII]$λ$6584, [SII]$λλ$6717,6731) and use line-ratios to spectroscopically confirm 255 HII regions, including 90 previously "Known" HII regions and 165 newly classified ones. We measure their $T_{\rm e}$, $n_{\rm e}$ and oxygen abundance, and determine distances via associated OB stars and the kinematic method. The sample spans $R_{\rm gal}$ from 8.16 to 15.36 kpc, enabling investigation of radial gradients in physical properties. We find [NII]/H$α$ and [SII]/H$α$ decrease with increasing $R_{\rm gal}$, while [SII]/[NII] remains nearly flat; these trends are quite different from diffuse ionized gas (DIG). We derive the $T_{\rm e}$ gradient of 344.530 $\pm$ 78.083 K kpc$^{-1}$, and the $\log n_{\rm e}$ gradient of -0.143 $\pm$ 0.041 cm$^{-3}$ kpc$^{-1}$. Oxygen abundance shows a steep slope of -0.044 $\pm$ 0.010 dex kpc$^{-1}$ in the inner disk and a shallow slope of -0.016 $\pm$ 0.005 dex kpc$^{-1}$ in the outer disk, with a global slope of -0.014 $\pm$ 0.005 dex kpc$^{-1}$. We also examine the two-dimensional distributions of $T_{\rm e}$, $n_{\rm e}$, and oxygen abundance, and find the gradients vary with azimuth. There is no obvious difference between spiral arm and interarm regions, and no trend appears along individual arms. From [NII]/H$α$-[SII]$λ$6717/H$α$ diagram, HII regions have a S$^+$/S ratio (0.32), lower than DIG (0.43); however, heavy overlap prevents clear separation from this diagram alone.

astro-ph.GA↗

arXiv2026

NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; 2) it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play (PnP) proximal alternating-minimization network (PAN), incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

cs.RO↗

arXiv2026

Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication

Cooperative perception, leveraging shared information from multiple vehicles via vehicle-to-vehicle (V2V) communication, plays a vital role in autonomous driving to alleviate the limitation of single-vehicle perception. Existing works have explored the effects of V2V communication impairments on perception precision, but they lack generalization to different levels of impairments. In this work, we propose a joint weighting and denoising framework, Coop-WD, to enhance cooperative perception subject to V2V channel impairments. In this framework, the self-supervised contrastive model and the conditional diffusion probabilistic model are adopted hierarchically for vehicle-level and pixel-level feature enhancement. An efficient variant model, Coop-WD-eco, is proposed to selectively deactivate denoising to reduce processing overhead. Rician fading, non-stationarity, and time-varying distortion are considered. Simulation results demonstrate that the proposed Coop-WD outperforms conventional benchmarks in all types of channels. Qualitative analysis with visual examples further proves the superiority of our proposed method. The proposed Coop-WD-eco achieves up to 50% reduction in computational cost under severe distortion while maintaining comparable accuracy as channel conditions improve.

cs.CV↗

arXiv2026

Stellar Density Classification and Regression for CSST Multi-color Imaging Using Deep Learning

The Chinese Space Station Survey Telescope (CSST) aims to map the universe across an unprecedented dynamic range of stellar densities, spanning from extragalactic voids to the crowded Galactic center (e.g. a few stars and galaxies in the voids and $>10^5$ stars per detector in Galactic center). However, processing such heterogeneous data with a general source extraction pipeline introduces significant systematic uncertainties, standard algorithms exhibit poor accuracy in crowded fields and suffer from increased astrometric uncertainty in void regions. To mitigate these systematics, we propose a hierarchical, two-stage deep learning model for adaptive data reduction. The first stage ('classification') employs a ResNet-34 model to classify images into six discrete density categories, achieving $98.83\%$ in global accuracy. This classification acts as a critical decision gate, ensuring high calibration accuracy in the crowded fields. In the second stage ('regression'), a ResNet-50 regression model predicts the bright stars ($<23.5$ mag) in the field, which is essential for astrometric calibration, achieving a mean absolute error (MAE) of 0.0824 dex. By decoupling density characterization from source extraction, our model ensures that photometric and astrometric algorithms are optimally matched to the stellar density environment, thereby enhancing the fidelity and homogeneity of CSST as well as future large sky survey data products.

astro-ph.IM↗

arXiv2026

SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing

Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on $k$-nearest neighbors ($k$NN) or other node-level full search algorithms (FSA) to mine semantic relationships via exhaustive pairwise similarity computation, which suffer from high computational complexity and rigid neighbor selection, limiting scalability and introducing noisy connections. In this paper, we propose the Semantic Consistency enhanced Graph Neural Network (SCGNN), a novel plug-and-play framework that leverages granular-ball computing (GBC) to efficiently capture semantic consistency in a scalable manner. Unlike node-level FSA methods, SCGNN models group-level semantic structure by adaptively partitioning nodes into granular balls, significantly reducing computational cost while improving robustness to noise. To effectively utilize the discovered group-level semantic consistency, we design a dual enhancement strategy. Specifically, (1) a structure enhancement module constructs an anchor-based graph structure, where each anchor is a virtual node representing the group-level semantic carried by a granular ball, then injecting group-level semantic information into the graph structure; and (2) a supervision enhancement module performs label consistency checking (LCC) by combining GBC predictions with model-generated pseudo-labels, thereby producing more reliable supervision signals. SCGNN is compatible with various GNN backbones. During the forward propagation of SCGNN, the vanilla graph and the augment graph are jointly encoded, and their predictions are fused; during the backpropagation, the supervision enhancement module provides enhanced supervision signals to guide parameter updates.

cs.AI↗

arXiv2026

Quantum Monte Carlo algorithm for option pricing and its complexity analysis

In this paper we provide a quantum Monte Carlo algorithm to solve multidimensional Black-Scholes PDEs with correlation for option pricing. The payoff function of the option is of general form and is only required to be continuous and piecewise affine, which covers most of the relevant payoff functions used in finance. We provide a rigorous error analysis and complexity analysis of our algorithm. In particular, we prove that the computational complexity of our algorithm is bounded polynomially in the space dimension $d$ of the PDE and the reciprocal of the prescribed accuracy $\varepsilon$. Moreover, we show that for payoff functions which are bounded, our algorithm indeed has a speed-up compared to classical Monte Carlo methods. Furthermore, we provide numerical simulations in two dimensions using our developed package within the Qiskit framework tailored to price continuous piecewise affine options with respect to the Black-Scholes model, as well as discuss the potential extension of the numerical simulations to arbitrary space dimension.

quant-ph↗

arXiv2026

KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction

Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when stored as flat sequences. As a result, state-of-the-art methods often miss true dependencies within executions while learning spurious correlations across unrelated events. We propose KRONE, the first hierarchical anomaly detection framework that automatically derives execution hierarchies from flat logs to enable modular, multi-level anomaly detection. At its core, the KRONE Log Abstraction Model extracts application-specific semantic hierarchies, which are used to recursively decompose log sequences into coherent execution units, referred to as KRONE Seqs. This transforms sequence-level detection into a set of modular KRONE Seq-level detection tasks. For each test KRONE Seq, KRONE adopts a hybrid modular detection strategy that routes between an efficient level-independent Local-Context detector for rapid filtering and a Nested-Aware detector that captures cross-level semantic dependencies, augmented with LLM-based anomaly detection and explanation. KRONE further optimizes detection through cached result reuse and early-exit strategies along the hierarchy. Experiments on three public benchmarks and one industrial dataset from ByteDance Cloud demonstrate that KRONE achieves substantial improvements in accuracy (42.49% to 87.98%), F1 score, data efficiency (117.3x reduction), resource efficiency (43.7x reduction), and interpretability. KRONE improves F1-score by 10.07% (82.76% to 92.83%) over prior methods while reducing LLM usage to only 1.1% to 3.3% of the test data. Code: https://github.com/LeiMa0324/KRONE Demo: https://leima0324.github.io/KRONE_Demo_official/

cs.DB↗

arXiv2026

Can Large Language Models be a Cardinality Estimator? An Empirical study

Cardinality estimation (CardEst) still remains a challenging problem for DBMS. Recent years have witnessed the success of ML-based cardinality estimators in outperforming traditional methods. However, these solutions suffer from poor generalizability to new data or query distribution, inability to handle complex queries, and substantial data preparation overhead, thus preventing their wide adoption in the real-world DBMS. Some recent efforts have been dedicated to addressing some but not all of these issues. We notice that the recent emerging Large Language Models (LLMs) have shown their remarkable generalizability to unseen tasks, capabilities to understand complex programs, and power to perform data-efficient fine-tuning. In light of this, we propose to leverage LLMs to mitigate the above issues. Specifically, we carefully craft prompts, and subsequently perform fine-tuning and self-correction during inference with LLMs for CardEst task. We then extensively evaluate LLMs' in-distribution and out-of-distribution generalizability, feasibility to support complex queries, and training data efficiency during fine-tuning LLMs on pre-training datasets. The results suggest that LLMs outperform the state-of-the-art in almost all settings, thus indicating their potential for the CardEst task. We further measure the end-to-end query execution time in DBMS by using the estimated cardinalities of LLMs in some practical settings, which suggests that the inference overhead of LLMs can be outweighed by the benefits brought by LLMs for CardEst.

cs.DB↗

arXiv2026

LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs

Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimated costs, leading to low efficiency (due to the vast search space) and unsatisfactory actual latency (due to inaccurate estimations). Inspired by the refinement strategies of experienced DBAs-who efficiently identify and iteratively refine indexes with database feedback-we present LLMIA, an out-of-the-box, tuning-free index advisor leveraging large language models (LLMs) through in-context learning for index recommendation. LLMIA injects database expertise into the LLM using a high-quality demonstration pool and comprehensive workload feature extraction, while iteratively incorporating database feedback to guide the index refinement. This design enables LLMIA to emulate the decision-making process of expert DBAs: efficiently recommending and refining indexes for various workloads within just a few interactions with the DBMS. We validate LLMIA with extensive experiments on five standard OLAP benchmarks (TPC-H with different scales, JOB, TPC-DS, SSB), where it consistently outperforms or matches 12 baselines by producing superior index recommendations with minimal database interactions. Additionally, LLMIA demonstrates robust generalization on two real-world commercial workloads, delivering high-quality recommendations without the need for additional adaptation or retraining, highlighting its out-of-the-box capability.

cs.DB↗

arXiv2026

The size-velocity dispersion relationship of Galactic HII regions

The size-velocity dispersion ($σ$) relation, while well established for giant HII regions, remains uncertain for their smaller counterparts (physical radii R < 20 pc). Thanks to the LAMOST MRS-N dataset's large sky coverage and high spatial/spectral resolution, we examined this relationship using 10 isolated Galactic HII regions with R < 20 pc. Our results reveal two key findings: (1) these small-size HII regions remarkably follow the same size-$σ$ relation as giant HII regions, suggesting this correlation could serve as a novel distance indicator for Galactic HII regions; and (2) we find distinct dynamical behaviors between younger and older HII regions. Specifically, in younger (< 0.5 Myr), ionization-bounded HII regions, the velocity dispersion shows no correlation with expansion velocity, indicating that turbulence is driven primarily by stellar winds and ionization processes. In contrast, in older (> 0.5 Myr), matter-bounded HII regions, a clear correlation emerges, implying that expansion-driven processes begin to play a significant role in generating turbulence. We therefore propose an evolutionary transition in the primary turbulence mechanisms, from being dominated by stellar winds and radiation to being increasingly influenced by expansion-driven dynamics, during the evolution of HII regions. Considering the small sample size used in this work, particularly the inclusion of only two young HII regions, which also have large uncertainties in their expansion velocities, further confirmation of this interpretation will require higher-resolution 2D spectroscopy to resolve blended kinematic components along the line of sight for more accurate estimation of expansion velocities, along with an expanded sample that specifically includes more young HII regions.

astro-ph.GA↗

arXiv2026

Mock Observations for the CSST Mission: Integral Field Spectrograph--GEHONG: A Package for Generating Ideal Datacubes

We developed a Python package GEHONG to mock the three-dimensional spectral data cube under the observation of an ideal telescope for the Integral Field Spectrograph of the Chinese Space Station Telescope (CSST-IFS). This package can generate one-dimensional spectra corresponding to local physical properties at specific positions according to a series of two-dimensional distributions of physical parameters of target sources. In this way, it can produce a spatially resolved spectral cube of the target source. Two-dimensional distributions of physical parameters, including surface brightness, stellar population, and line-of-sight velocity, can be modeled using the parametric model or based on real observational data and numerical simulation data. For the generation of one-dimensional spectra, we have considered four types of spectra, including the stellar continuum spectra, ionized gas emission lines, AGN spectra, and stellar spectra. That makes GEHONG able to mock various types of targets, including galaxies, AGNs, star clusters, and HII regions.

astro-ph.IM↗