arXiv subjects

Sirong Lu

Publications and source records attributed to Sirong Lu.

4 recordsLinked to original sources

arXiv2026

DeepEye: A Steerable Self-driving Data Agent System

Large Language Models (LLMs) have revolutionized natural language interaction with data. The "holy grail" of data analytics is to build autonomous Data Agents that can self-drive complex data analysis workflows. However, current implementations are still limited to linear "ChatBI" systems. These systems struggle with joint analysis across heterogeneous data sources (e.g., databases, documents, and data files) and often encounter "context explosion" in complex and iterative data analysis workflows. To address these challenges, we present DeepEye, a production-ready data agent system that adopts a workflow-centric architecture to ensure scalability and trustworthiness. DeepEye introduces a Unified Multimodal Orchestration protocol, enabling seamless integration of structured and unstructured data sources. To mitigate hallucinations, it employs Hierarchical Reasoning with context isolation, decomposing complex intents into autonomous AgentNodes and deterministic ToolNodes. Furthermore, DeepEye incorporates a database-inspired Workflow Engine (comprising a Compiler, Validator, Optimizer, and Executor) that guarantees structural correctness and accelerates execution via runtime topological optimization. In this demonstration, we showcase DeepEye's ability to orchestrate complex workflows to generate diverse multimodal outputs -- including Data Videos, Dashboards, and Analytical Reports -- highlighting its advantages in transparent execution, automated optimization, and human-in-the-loop reliability.

cs.DB↗

arXiv2025

VisJudge-Bench: Aesthetics and Quality Assessment of Visualizations

Visualization, a domain-specific yet widely used form of imagery, is an effective way to turn complex datasets into intuitive insights, and its value depends on whether data are faithfully represented, clearly communicated, and aesthetically designed. However, evaluating visualization quality is challenging: unlike natural images, it requires simultaneous judgment across data encoding accuracy, information expressiveness, and visual aesthetics. Although multimodal large language models (MLLMs) have shown promising performance in aesthetic assessment of natural images, no systematic benchmark exists for measuring their capabilities in evaluating visualizations. To address this, we propose VisJudge-Bench, the first comprehensive benchmark for evaluating MLLMs' performance in assessing visualization aesthetics and quality. It contains 3,090 expert-annotated samples from real-world scenarios, covering single visualizations, multiple visualizations, and dashboards across 32 chart types. Systematic testing on this benchmark reveals that even the most advanced MLLMs (such as GPT-5) still exhibit significant gaps compared to human experts in judgment, with a Mean Absolute Error (MAE) of 0.553 and a correlation with human ratings of only 0.428. To address this issue, we propose VisJudge, a model specifically designed for visualization aesthetics and quality assessment. Experimental results demonstrate that VisJudge significantly narrows the gap with human judgment, reducing the MAE to 0.421 (a 23.9% reduction) and increasing the consistency with human experts to 0.687 (a 60.5% improvement) compared to GPT-5. The benchmark is available at https://github.com/HKUSTDial/VisJudgeBench.

cs.CL↗

arXiv2024

Debugging Defective Visualizations: Empirical Insights Informing a Human-AI Co-Debugging System

Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches, or how they can be effectively combined. We analyze Vega-Lite debugging cases from Stack Overflow, categorizing question types by askers, evaluating human responses, and assessing AI performance. Guided by these findings, we design a human-AI co-debugging system that combines LLM-generated suggestions with forum knowledge. We evaluated this system in a user study on 36 unresolved problems, comparing it with forum answers and LLM baselines. Our results show that while forum contributors provide accurate but slow solutions and LLMs offer immediate but sometimes misaligned guidance, the hybrid system resolves 86% of cases, higher than either alone.

cs.HC↗

arXiv2024

Near-Room-Temperature Field-Controllable Exchange Bias in 2D van der Waals Ferromagnet Fe3GaTe2

Exchange bias (EB) is a cornerstone of modern magnetic memory and sensing technologies. Its extension to the realm of two-dimensional (2D) van der Waals (vdW) magnets holds promise for revolutionary advancements in miniaturized and efficient atomic spintronic devices. However, the blocking temperature of EB in 2D vdW magnets is currently well below room temperature ~130 K. This study reports a robust EB phenomenon in Fe3GaTe2 thin-layer devices, which significantly increases the blocking temperature to a near-room-temperature record of 280 K. Both the bias direction and magnitude can be isothermally tuned by adjusting the field sweep range, in striking contrast to the conventional EB in ferromagnetic/antiferromagnetic (FM/AFM) bilayers. We propose an exchange spring model in which crystal defects with higher coercivity act as the pivotal pinning source for the observed EB phenomenon, deviating from the conventional FM/AFM interface mechanism. Cumulative growth of minor loops and multiple magnetization reversal paths are observed in field cycles below the saturation field, consistent with the hard FM defects behavior of our exchange spring model. These findings provide insights into the complex magnetic order in 2D ferromagnets and open new avenues for developing practical ultrathin vdW spintronic devices with EB-like properties at room temperature.

cond-mat.mes-hall↗