arXiv subjects

Bo Xiong

Publications and source records attributed to Bo Xiong.

At least 19 recordsLinked to original sources

arXiv2026

LLM-Enhanced Commit Message Generation via Issue Information: An Exploratory Study

Commit messages help developers understand code changes, support collaboration, and improve long-term maintenance. However, the use of issue information alone as the external context for LLM-based CMG has not been systematically studied. We propose an ISsue-Augmented framework for Commit message generation (ISAC) by combining code diffs with issue information as LLM input. To support the evaluation, we construct ApacheCM-Issue, a commit-issue aligned dataset built upon ApacheCM by linking commits with issues from GitHub and Apache Jira. Using samples from Scala, Java, and C++ projects, we evaluate four input configurations using two representative LLMs, GPT-5.5 and DeepSeek-V4-Flash in different reasoning configurations. The results show that incorporating issue information consistently improves LLM-based CMG across all evaluated model configurations and metrics, with the largest gains observed for CIDEr. Incorporating a similar historical commit further improves automatic metric scores, while replacing full issue information with a structured issue summary decreases them. ISAC also outperforms the four reproduced state-of-the-art (SOTA) CMG baselines across all five automatic metrics on the experimental dataset. The human evaluation further shows that structured issue summaries may improve perceived completeness, although replacing the original issue information can sacrifice contextual details and lead to worse results on automatic metrics.

cs.SE↗

arXiv2026

Assessing Language Models for Salient Class Identification

Code review requires reviewers to understand the core intent of code changes, which becomes difficult when a commit modifies multiple classes. In such commits, one or more primarily modified classes, referred to as salient classes, may induce modifications in other classes. Accurate identification of salient classes offers reviewers an effective entry point to navigate code changes and facilitates program comprehension. Existing state-of-the-art approaches rely on complex program-analysis procedures, including Abstract Syntax Tree (AST) parsing, class relation extraction, handcrafted feature engineering, or dependency graph construction. To this end, we study whether language models (LMs) can identify salient classes directly from commits without feature engineering, graph construction, or training. We first construct a new dataset ApacheJavaCM, derived from the ApacheCM dataset, containing 7,911 commits and 25,914 labeled classes. On this dataset, we systematically evaluate whether LMs can identify salient classes directly from commits and compare with the strongest reproducible state-of-the-art (SOTA) baseline. The evaluation covers two large language models (LLMs), GPT-5.4 and DeepSeek-V3.2, one small language model (SLM), Qwen3.5-9B, and three prompting strategies: zero-shot, few-shot, and chain-of-thought. The LMs substantially outperform the baseline while remaining stable across commit characteristics and selected LMs. We also found that, for salient class identification tasks, a 9B-parameter open-source SLM, Qwen3.5-9B, under few-shot prompting, achieves performance comparable to that of a much larger closed-source LLM, GPT-5.4. These results suggest that lightweight, locally deployable SLMs are feasible options for the salient class identification task and can reduce both cost and privacy barriers associated with relying on closed-source LLMs.

cs.SE↗

arXiv2026

CoRaCommit: A VS Code Extension for Commit Message Generation with Exemplar Retrieval

Commit messages are essential textual artifacts that describe the intent behind code changes, and play a critical role in version control, code review, and historical tracking. However, in practice, commit messages are primarily authored manually, which is time-consuming and often results in inconsistent quality and non-uniform expression. Existing VS Code extensions for commit message generation typically directly invoke large language models based on the code diff, without leveraging similar commit exemplars as references, and rarely support user feedback-driven LLM recommendation. To address these limitations, this paper presents CoRaCommit, a VS Code extension that enhances commit message generation by retrieving similar commit exemplars as prompt context, invoking multiple LLMs in parallel for candidate commit message comparison, and dynamically recommending LLMs based on user feedback. Experimental results on 945 commits from the ApacheCM dataset show that CoRaCommit outperforms existing VS Code extensions across BLEU, CIDEr, METEOR, and ROUGE-L metrics, demonstrating the effectiveness of retrieval-augmented context for commit message generation.

cs.SE↗

arXiv2026

Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy

Light-field microscopy (LFM) enables single-shot capture of multi-angular information from biological samples, supporting real-time volumetric imaging. However, traditional physics-based algorithms often suffer from limited spatial resolution, severe artifacts, and high computational costs. Existing learning-based methods improve inference efficiency but still face limitations in reconstruction accuracy and generalization capability. To address these challenges, this paper proposes a high-fidelity Three-Step Conditional Diffusion (TCD) 3D reconstruction method for LFM. Although conventional diffusion models have achieved remarkable success in generative modeling, their slow sampling process and the inherent trade-off between quality and efficiency hinder their application in real-time 3D imaging. We redesign the diffusion process through a deterministic three-step sampling strategy coupled with a lightweight conditional U-Net, establishing a new paradigm for fast and accurate volumetric reconstruction. Furthermore, an Inter-Class Detection (ICD) module is incorporated to identify out-of-distribution or anomalous inputs during inference, thereby enhancing model stability and reliability. Extensive experiments and cross-dataset evaluations demonstrate that TCD significantly outperforms state-of-the-art methods in both reconstruction fidelity and generalization, providing an efficient and practical 3D reconstruction solution for light-field microscopy.

cs.CV↗

arXiv2026

Agentic metasurface design with self-correcting language-model systems

Automated metasurface design is increasingly important, and recent advances in language-model systems are opening a route toward agentic optical design. Yet modern metasurface applications, from metalenses and holography to optical computing, require long design chains spanning modeling, simulation, coding, optimization and evaluation. These chains are error-prone, whereas existing language-model-based metasurface tools remain largely limited to simple objectives, predefined pipelines or language-to-layout generation. Here we introduce MetaDesigner, a self-correcting language-model system for agentic metasurface design. From a natural-language optical objective, MetaDesigner plans the design route, retrieves domain knowledge, invokes simulation and optimization tools, generates missing tool code and identifies errors through a dedicated Verifier. We demonstrate three tasks of increasing complexity: an RGB metalens with three independent focal spots, a six-plane full-color hologram with an average structural similarity index measure (SSIM) of 0.97, and an optoelectronic hybrid neural network for image style transfer. These tasks require 74, 136 and 90 reasoning steps, respectively, and the system self-corrects errors in frequency mapping, numerical aperture estimation, network-parameter counting and loss-function description. These results establish MetaDesigner as a self-correcting route to agentic metasurface design, where language-model systems can not only execute optical design tasks but also extend, inspect and repair the design process itself.

physics.optics↗

arXiv2026

The Lattice Representation Hypothesis of Large Language Models

We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry. Our framework unifies the Linear Representation Hypothesis with Formal Concept Analysis (FCA), showing that linear attribute directions with separating thresholds induce a concept lattice via half-space intersections. This geometry enables symbolic reasoning through geometric meet (intersection) and join (union) operations, and admits a canonical form when attribute directions are linearly independent. Experiments on WordNet sub-hierarchies provide empirical evidence that LLM embeddings encode concept lattices and their logical structure, revealing a principled bridge between continuous geometry and symbolic abstraction. Datasets and code are open available at https://github.com/xiongbo010/lattice-representation-hypothesis.

cs.AI↗

arXiv2026

Geometry-driven splitting dynamics of a triply quantized vortex in a ring-shaped condensate

We study the splitting dynamics of a triply quantized vortex (TQV) confined in a ring-shaped Bose-Einstein condensate under a weakly elliptical harmonic trap. Using full 3D simulations in cylindrical coordinates, combined with a semi-analytical energy analysis, we show that the vortex preferentially splits along the long axis of the trap, a direction that minimizes the kinetic-energy cost relative to the initial TQV state. Systematic parameter scans reveal that initial quantum fluctuations increase the splitting time and suppress the transient three-core pattern observed in noise-free simulations, whereas stronger nonlinear interactions accelerate the splitting. When the trap is nearly isotropic, the unstable Bogoliubov modes are dominated by both azimuthal quantum number $l_q=3$ and $l_q=2$; this leads to a dynamical sequence where three daughter vortices first form a triangular arrangement, later evolving into a linear chain. For stronger anisotropy, geometric coupling selectively enhances the $l_q=2$ mode, making it the sole dominant channel and resulting directly in linear vortex alignment -- a clear signature of geometry-induced mode competition explained through combined energy-based and Bogoliubov stability analysis. Our results provide a quantitative picture of how trap geometry can steer the instability pathway, splitting time, and final pattern of a multiply quantized vortex, offering a route toward geometry-controlled vortex engineering.

cond-mat.quant-gas↗

arXiv2025

Probing the Crossover between Dynamical Phases with Local Correlations in a Rydberg Atom Array

The experimental detection of non-equilibrium quantum criticality remains a challenge, as traditional signatures like dynamical quantum phase transitions rely on hard-to-measure global properties. Here, we demonstrate that local connected correlation functions provide a superior, practical means to directly probe the dynamics of magnetic order in a quenched Rydberg atom array. Using a Magnus expansion formalism, we derive analytic expressions for these correlations that capture a smooth crossover from antiferromagnetic to ferromagnetic dominance. Our analytic results, which reveal the critical parameter relationship $U_{c}(\delta)$, are validated against exact numerical simulations and exhibit robustness to finite-size effects. By shifting the focus from global singularities to local correlations, our protocol establishes a direct and feasible path to observe the rich critical dynamics in scalable quantum simulators.

cond-mat.quant-gas↗

arXiv2025

Are Large Language Models Effective Knowledge Graph Constructors?

Knowledge graphs (KGs) are widely used in knowledge-intensive applications, yet it remains unclear how effectively current large language models (LLMs) can construct document-grounded KGs in a zero-shot, schema-free setting without relying on complex task-specific frameworks. We introduce Detail-to-Abstract Hierarchical Knowledge Graph (D2A-HKG) construction framework, which decomposes KG construction into three stages: initial extraction, splitting, and abstraction, and evaluates the resulting graphs from both semantic and structural perspectives. Using seven frontier LLMs, we benchmark zero-shot KG construction on CMW-Lit, a dataset derived from published paediatric research articles on children's mental well-being. CMW-Lit provides a challenging test bed due to its heterogeneous evidence, interconnected factors, and complex, statistically qualified relationships. Our results show that state-of-the-art LLMs can generally produce relevant and document-faithful triples with limited hallucination, while exhibiting substantially different extraction behaviors across the construction stages. These findings provide empirical insight into the strengths and limitations of frontier LLMs for direct knowledge graph construction. We further release CMW-Lit and the resulting knowledge graphs as resources for future research, with the generated graphs providing a strong foundation for expert refinement and downstream knowledge-intensive applications.

cs.CL↗

arXiv2025

CALM: A Causal Analysis Language Model for Tabular Data in Complex Systems with Local Scores, Conditional Independence Tests, and Relation Attributes

Causal discovery from observational data is fundamental to scientific fields like biology, where controlled experiments are often impractical. However, existing methods, including constraint-based (e.g., PC, causalMGM) and score-based approaches (e.g., NOTEARS), face significant limitations. These include an inability to resolve causal direction, restrictions to linear associations, sensitivity to violations of the faithfulness assumption, and inefficiency in searching vast hypothesis spaces. While large language models (LLMs) offer powerful reasoning capabilities, their application is hindered by a fundamental discrepancy: they are designed for text, while most causal data is tabular. To address these challenges, we introduce CALM, a novel causal analysis language model specifically designed for tabular data in complex systems. CALM leverages a Mamba-based architecture to classify causal patterns from pairwise variable relationships. It integrates a comprehensive suite of evidence, including local causal scores, conditional independence tests, and relational attributes, to capture a wide spectrum of linear, nonlinear, and conditional causal mechanisms. Trained on a diverse corpus of synthetic data (from linear, mixed, and nonlinear models) and 10 real-world biological datasets with rigorously validated causal relationships, our model ensures robustness and generalizability. Empirical evaluation demonstrates that CALM significantly outperforms existing methods in both simulation studies, achieving over 91% accuracy, and in a real-world application identifying causal factors in Hepatitis C virus progression. This work represents a significant step towards accurate and generalizable causal discovery by successfully adapting the pattern recognition capabilities of language models to the intricacies of tabular data.

cs.LG↗

arXiv2025

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation

Commit messages play a key role in documenting the intent behind code changes. However, they are often low-quality, vague, or incomplete, limiting their usefulness. Commit Message Generation (CMG) aims to automatically generate descriptive commit messages from code diffs to reduce developers' effort and improve message quality. Although recent advances in LLMs have shown promise in automating CMG, their performance remains limited. This paper aims to enhance CMG performance by retrieving similar diff-message pairs to guide LLMs to generate commit messages that are more precise and informative. We proposed CoRaCMG, a Contextual Retrieval-augmented framework for Commit Message Generation, structured in three phases: (1) Retrieve: retrieving the similar diff-message pairs; (2) Augment: combining them with the query diff into a structured prompt; and (3) Generate: generating commit messages corresponding to the query diff via LLMs. CoRaCMG enables LLMs to learn project-specific terminologies and writing styles from the retrieved diff-message pairs. We evaluated CoRaCMG across multiple LLMs (e.g., GPT, DeepSeek, and Qwen) and compared its performance against SOTA baselines. Experimental results show that CoRaCMG significantly boosts LLM performance across four metrics (BLEU, Rouge-L, METEOR, and CIDEr). Specifically, DeepSeek-R1 achieves relative improvements of 76% in BLEU and 71% in CIDEr when augmented with a single retrieved example pair. After incorporating the single example pair, GPT-4o achieves the highest improvement rate, with BLEU increasing by 89%. Moreover, performance gains plateau after more than three examples are used, indicating diminishing returns. Further analysis shows that the improvements are attributed to the model's ability to capture the terminologies and writing styles of human-written commit messages from the retrieved example pairs.

cs.SE↗

arXiv2025

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation

Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents and performs deterministic reasoning under gradual semantics. This allows faithfully explaining and contesting decisions. Evaluated on two fact verification benchmarks, PubHealth and RAGuard, ArgRAG achieves strong accuracy while significantly improving transparency.

cs.AI↗

arXiv2025

Contextual Code Retrieval for Commit Message Generation: A Preliminary Study

A commit message describes the main code changes in a commit and plays a crucial role in software maintenance. Existing commit message generation (CMG) approaches typically frame it as a direct mapping which inputs a code diff and produces a brief descriptive sentence as output. However, we argue that relying solely on the code diff is insufficient, as raw code diff fails to capture the full context needed for generating high-quality and informative commit messages. In this paper, we propose a contextual code retrieval-based method called C3Gen to enhance CMG by retrieving commit-relevant code snippets from the repository and incorporating them into the model input to provide richer contextual information at the repository scope. In the experiments, we evaluated the effectiveness of C3Gen across various models using four objective and three subjective metrics. Meanwhile, we design and conduct a human evaluation to investigate how C3Gen-generated commit messages are perceived by human developers. The results show that by incorporating contextual code into the input, C3Gen enables models to effectively leverage additional information to generate more comprehensive and informative commit messages with greater practical value in real-world development scenarios. Further analysis underscores concerns about the reliability of similaritybased metrics and provides empirical insights for CMG.

cs.SE↗

arXiv2025

Lossless, Non-Volatile Post-Fabrication Trimming of PICs via On-Chip High-Temperature Annealing of Undercut Waveguides

Limited by equipment precision, manufacturing deviations in waveguide width, etch depth, and layer thickness inevitably occur in photonic integrated circuits (PICs). These variations cause initial phase errors, compromising the reliability of phase-sensitive devices such as Mach-Zehnder Interferometers (MZI) and microring resonators. To overcome this, we report a nonvolatile, near-lossless post-trimming method utilizing sufficient high-temperature thermal treatment for undercut waveguides, reported here for the first time to the best of our knowledge. This CMOS-compatible approach requires no additional processes or equipment, enables simple electrical heating for trimming, and retains long-term stability after high-temperature removal, ensuring high energy efficiency. Transmission electron microscopy indicates that high-temperature thermal treatment induces irreversible lattice expansion in silicon waveguides, leading to a reduction in the real refractive index and enabling compensation for process errors. Experimental results using MZIs confirm a permanent refractive index reduction of 0.0173 and high-resolution tuning up to 5.25 bits, effective across a broadband spectrum and stable for over 218 days after final trimming. Furthermore, 15 MZIs on a single wafer are precisely calibrated to BAR, CROSS, or orthogonal states, demonstrating the method universality. This practical and scalable technique enables reliable post-fabrication trimming for next-generation low-cost, energy-efficient PIC applications such as optical switches and optical computing.

physics.optics↗

arXiv2025

SEMMA: A Semantic Aware Knowledge Graph Foundation Model

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable textual semantics alongside structure. SEMMA leverages Large Language Models (LLMs) to enrich relation identifiers, generating semantic embeddings that subsequently form a textual relation graph, which is fused with the structural component. Across 54 diverse KGs, SEMMA outperforms purely structural baselines like ULTRA in fully inductive link prediction. Crucially, we show that in more challenging generalization settings, where the test-time relation vocabulary is entirely unseen, structural methods collapse while SEMMA is 2x more effective. Our findings demonstrate that textual semantics are critical for generalization in settings where structure alone fails, highlighting the need for foundation models that unify structural and linguistic signals in knowledge reasoning.

cs.CL↗

arXiv2025

Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings

Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty estimates by generating a set of answers that is guaranteed to include the true answer with a predefined confidence level. However, existing methods provide probabilistic guarantees averaged over a reference set of queries and answers (marginal coverage guarantee). In high-stakes applications such as medical diagnosis, a stronger guarantee is often required: the predicted sets must provide consistent coverage per query (conditional coverage guarantee). We propose CondKGCP, a novel method that approximates predicate-conditional coverage guarantees while maintaining compact prediction sets. CondKGCP merges predicates with similar vector representations and augments calibration with rank information. We prove the theoretical guarantees and demonstrate empirical effectiveness of CondKGCP by comprehensive evaluations.

cs.AI↗

arXiv2025

3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy

Compared to light-field microscopy (LFM), which enables high-speed volumetric imaging but suffers from non-uniform spatial sampling, Fourier light-field microscopy (FLFM) introduces sub-aperture division at the pupil plane, thereby ensuring spatially invariant sampling and enhancing spatial resolution. Conventional FLFM reconstruction methods, such as Richardson-Lucy (RL) deconvolution, exhibit poor axial resolution and signal degradation due to the ill-posed nature of the inverse problem. While data-driven approaches enhance spatial resolution by leveraging high-quality paired datasets or imposing structural priors, Neural Radiance Fields (NeRF)-based methods employ physics-informed self-supervised learning to overcome these limitations, yet they are hindered by substantial computational costs and memory demands. Therefore, we propose 3D Gaussian Adaptive Tomography (3DGAT) for FLFM, a 3D gaussian splatting based self-supervised learning framework that significantly improves the volumetric reconstruction quality of FLFM while maintaining computational efficiency. Experimental results indicate that our approach achieves higher resolution and improved reconstruction accuracy, highlighting its potential to advance FLFM imaging and broaden its applications in 3D optical microscopy.

eess.IV↗

arXiv2025

Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Cameras

Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras--ultra-high-speed, high-dynamic-range vision sensors--in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network's performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

cs.CV↗