Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification.

Second, the model is more data-and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.

Second, the model is more data-and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.

SEAL, Distance Encoding, and ID-GNN, all used node labeling.

SEAL, Distance Encoding, and ID-GNN, all used node labeling.

Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification.

We introduce the MultiLang Code Parser Dataset (MLCPD), a large-scale, language-agnostic dataset unifying syntactic and structural representations of code across ten major programming languages. MLCPD contains over seven million parsed source files normalized under our proposed universal Abstract Syntax Tree (AST) schema, enabling consistent cross-language reasoning, structural learning, and multilingual software analysis. Unlike existing corpora that focus purely on token-level code or isolated parsers, MLCPD provides both hierarchical tree representations and rich metadata for every file, ensuring lossless syntactic coverage and structural uniformity. Each entry includes a normalized schema, language-level metadata, and abstracted node semantics stored in Parquet format for scalable retrieval. Empirical analyses reveal strong cross-language structural regularities-demonstrating that syntactic graphs from languages as diverse as Python, Java, and Go can be aligned under a shared schema. We release the dataset publicly on Hugging Face and the accompanying codebase on GitHub, which includes complete pipelines for dataset reproduction, grammar compilation, and a visualization tool for exploring the unified AST across languages. Together, these resources establish MLCPD as an open, reproducible foundation for future research in cross-language representation learning and program analysis.

Automated malware classification has achieved strong detection performance. Yet, malware behavior auditing seeks causal and verifiable explanations of malicious activities -- essential not only to reveal what malware does but also to substantiate such claims with evidence. This task is challenging, as adversarial intent is often hidden within complex, framework-heavy applications, making manual auditing slow and costly. Large Language Models (LLMs) could help address this gap, but their auditing potential remains largely unexplored due to three limitations: (1) scarce fine-grained annotations for fair assessment; (2) abundant benign code obscuring malicious signals; and (3) unverifiable, hallucination-prone outputs undermining attribution credibility. To close this gap, we introduce MalEval, a comprehensive framework for fine-grained Android malware auditing, designed to evaluate how effectively LLMs support auditing under real-world constraints. MalEval provides expert-verified reports and an updated sensitive API list to mitigate ground truth scarcity and reduce noise via static reachability analysis. Function-level structural representations serve as intermediate attribution units for verifiable evaluation. Building on this, we define four analyst-aligned tasks -- function prioritization, evidence attribution, behavior synthesis, and sample discrimination -- together with domain-specific metrics and a unified workload-oriented score. We evaluate seven widely used LLMs on a curated dataset of recent malware and misclassified benign apps, offering the first systematic assessment of their auditing capabilities. MalEval reveals both promising potential and critical limitations across audit stages, providing a reproducible benchmark and foundation for future research on LLM-enhanced malware behavior auditing. MalEval is publicly available at https://github.com/ZhengXR930/MalEval.git