arXiv subjects

Ziyao He

Publications and source records attributed to Ziyao He.

4 recordsLinked to original sources

arXiv2026

Noisy Issues, Noisy Results: Mislabeling in Information Retrieval-based Bug Localization

Information Retrieval-based Bug Localization (IRBL) leverages bug reports to identify faulty source files. Evaluations of IRBL approaches primarily rely on labeled issues from issue tracking systems. However, prior research has shown that issue labels are frequently incorrect in open-source projects, threatening the validity of IRBL evaluations. Existing studies have primarily examined traditional IRBL techniques on small datasets, overlooking modern approaches and the partial overlap between noisy and cleaned datasets. In this paper, we conduct a large-scale empirical study to investigate the prevalence and impact of mislabeled issues in IRBL benchmarks. We analyze five benchmark datasets comprising over 55,000 issue reports and find that, on average, 34.82% of issues are mislabeled. We further evaluate six IRBL techniques, spanning traditional, deep learning-based, and large language model-based approaches using the widely adopted Ye et al. benchmark and statistical methods that account for dataset overlap. Our results show that removing mislabeled issues from evaluation datasets significantly reduces measured performance across all six techniques, with Mean Reciprocal Rank decreasing by 4.68%-21.74%. Although mislabeling does not change the relative ranking of techniques, it affects the magnitude of their performance differences. These findings highlight the importance of reliable evaluation data for ensuring valid and meaningful comparisons of IRBL techniques.

cs.SE↗

arXiv2026

Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding

Accurate 3D scene description is fundamental to robotic navigation and augmented reality, yet current dense captioning methods face significant limitations in processing sparse point cloud data. % Existing approaches that apply Euclidean embedding spaces struggle to simultaneously preserve fine-grained local geometric details and model exponentially growing global semantic hierarchies, leading to either inaccurate localization or disjointed, shallow scene descriptions. % In this work, we propose a novel \textbf{\textsc{Curvature-Aware Captioning}} framework, integrating novel non-Euclidean geodesic attention mechanisms, to resolve the localization-contextualization conflict. % Specifically, self-attention within Oblique space enforces dimensional homogeneity while establishing long-range dependencies. Bidirectional geodesic cross-attention within Lorentz space models hierarchical semantic relationships across scene instances, enabling simultaneous precision in object localization and coherence in scene descriptions. % Theoretical analysis confirms that the curvature complementarity between the Oblique manifold and Lorentz hyperboloid resolves the Euclidean-hyperbolic conflict, ensuring feature stability via isotropic optimization while preserving inherent hierarchical relationships. Extensive experiments on ScanRefer and Nr3D benchmarks demonstrate state-of-the-art performance, with significant gains in both localization accuracy and descriptive richness.

cs.CV↗

arXiv2026

OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing

Scientific paper generation requires document-level planning and factual grounding, but current large language models, despite their strong local fluency, often fail in global structure, input coverage, and citation consistency. We present a reinforcement learning framework that casts scientific outline construction as a long-horizon planning problem over hierarchical document structures. Our approach models edit evolving outlines through structured actions, enabling the system to incrementally build a complete scientific manuscript. To support effective and stabilize learning,we introduce a two-stage optimization procedure consisting of (i) backward outline reconstruction from partial plans to enforce global structural consistency, and (ii) forward value-guided reinforcement learning with rewards explicitly modeling scientific correctness, discourse coherence, and citation fidelity. In addition, We further introduce a benchmark for scientific paper generation that evaluates document planning, input utilization, reference faithfulness, outline organization, and content-level factual accuracy. Our results show consistent improvements over strong neural and LLM baselines, particularly in long-range structural coherence and citation reliability.

cs.CL↗

arXiv2025

Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding

Hyperbolic spaces allow for more efficient modeling of complex, hierarchical structures, which is particularly beneficial in tasks involving multi-modal data. Although hyperbolic geometries have been proven effective for language-image pre-training, their capabilities to unify language, image, and 3D Point Cloud modalities are under-explored. We extend the 3D Point Cloud modality in hyperbolic multi-modal contrastive pre-training. Additionally, we explore the entailment, modality gap, and alignment regularizers for learning hierarchical 3D embeddings and facilitating the transfer of knowledge from both Text and Image modalities. These regularizers enable the learning of intra-modal hierarchy within each modality and inter-modal hierarchy across text, 2D images, and 3D Point Clouds. Experimental results demonstrate that our proposed training strategy yields an outstanding 3D Point Cloud encoder, and the obtained 3D Point Cloud hierarchical embeddings significantly improve performance on various downstream tasks.

cs.CV↗