arXiv subjects

Tianhao Mao

Publications and source records attributed to Tianhao Mao.

7 recordsLinked to original sources

arXiv2026

A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories

Large language models (LLMs) are rapidly transforming software engineering by enabling developers to generate code ranging from small snippets to entire projects. As AI-assisted code becomes increasingly integrated into real-world systems, understanding its characteristics and impact is critical. Existing study on AI-generated code is usually limited in the lab setting with synthetic benchmarks and small-scale coding tasks and covers limited metrics. AI-assisted code's manifestation in real-world codebases and its differences between human-written one remain unclear. To close this gap, we perform a first large-scale measurement study of AI-assisted code, in comparison with the human-written, in real-world repositories. We study a comprehensive set of metrics including both code-level aspects (e.g., structural and graph-level complexity, coding style, security quality, etc.) and commit-level characteristics (e.g., commit size, frequency, post-commit stability, etc.). Our results provide new findings and insights: some contrast previous observations in the lab setting (e.g., we conclude that real-world AI-Human differences on code-level metrics are rather small instead of more pronounced), some extend prior results with finer-grained observations (e.g., the variance of security quality across different programming languages), yet more are presented for the first time on aspects not covered before (e.g., code duplication rate, commit size and stability, etc.). Based on these comprehensive real-world results, we also discuss the practical implications of AI-assisted programming.

cs.SE↗

arXiv2026

Beam Prediction Based on Multimodal Large Language Models

Accurate beam prediction is a key enabler for next-generation wireless communication systems. In this paper, we propose a multimodal large language model (LLM)-based beam prediction framework that effectively utilizes contextual information, provided by sensory data including RGB camera images and LiDAR point clouds. To effectively fuse heterogeneous modalities, we design specialized modality encoders together with a beam-guided attention masking mechanism and a high-frequency temporal alignment strategy, enabling robust cross-modal feature integration under dynamic environments. Furthermore, we construct a large-scale multimodal dataset for communication, named Multimodal-Wireless, which covers diverse weather and traffic conditions with high-fidelity ray-tracing labels. Extensive simulation results demonstrate that the proposed approach significantly reduces the reliance on oracle angle-of-departure knowledge and consistently outperforms state-of-the-art multimodal LLM-based beam prediction methods in terms of beam accuracy and communication performance, improving the average Top-1 accuracy to 80.8% and the average normalized gain to 89.1%.

eess.SP↗

arXiv2026

Multimodal-Wireless: A Large-Scale Dataset for Sensing and Communication

This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five other sensor modalities, namely LiDAR, RGB and depth camera, inertial measurement unit (IMU) and radar, all sampled at 100 Hz. It contains approximately 160,000 frames collected across four virtual towns, sixteen communication scenarios, and three weather conditions. This paper provides a comprehensive overview of the dataset, outlining its key features, overall framework, and technical implementation details. In addition, it explores potential research applications concerning communication and collaborative perception, exemplified by beam prediction using a multimodal large language model. The dataset is open in https://le-liang.github.io/mmw/.

eess.SP↗

arXiv2025

IndirectAD: Practical Data Poisoning Attacks against Recommender Systems for Item Promotion

Recommender systems play a central role in digital platforms by providing personalized content. They often use methods such as collaborative filtering and machine learning to accurately predict user preferences. Although these systems offer substantial benefits, they are vulnerable to security and privacy threats, especially data poisoning attacks. By inserting misleading data, attackers can manipulate recommendations for purposes ranging from boosting product visibility to shaping public opinion. Despite these risks, concerns are often downplayed because such attacks typically require controlling at least 1% of the platform's user base, a difficult task on large platforms. We tackle this issue by introducing the IndirectAD attack, inspired by Trojan attacks on machine learning. IndirectAD reduces the need for a high poisoning ratio through a trigger item that is easier to recommend to the target users. Rather than directly promoting a target item that does not match a user's interests, IndirectAD first promotes the trigger item, then transfers that advantage to the target item by creating co-occurrence data between them. This indirect strategy delivers a stronger promotion effect while using fewer controlled user accounts. Our extensive experiments on multiple datasets and recommender systems show that IndirectAD can cause noticeable impact with only 0.05% of the platform's user base. Even in large-scale settings, IndirectAD remains effective, highlighting a more serious and realistic threat to today's recommender systems.

cs.CR↗

arXiv2025

LineBreaker: Finding Token-Inconsistency Bugs with Large Language Models

Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often lead to software bugs. Unlike simple syntactic bugs, TIBs occur at the semantic level and are subtle - sometimes they remain undetected for years. Traditional detection methods, such as static analysis and dynamic testing, often struggle with TIBs due to their versatile and context-dependent nature. However, advancements in large language models (LLMs) like GPT-4 present new opportunities for automating TIB detection by leveraging these models' semantic understanding capabilities. This paper reports the first systematic measurement of LLMs' capabilities in detecting TIBs, revealing that while GPT-4 shows promise, it exhibits limitations in precision and scalability. Specifically, its detection capability is undermined by the model's tendency to focus on the code snippets that do not contain TIBs; its scalability concern arises from GPT-4's high cost and the massive amount of code requiring inspection. To address these challenges, we introduce \name, a novel and cascaded TIB detection system. \name leverages smaller, code-specific, and highly efficient language models to filter out large numbers of code snippets unlikely to contain TIBs, thereby significantly enhancing the system's performance in terms of precision, recall, and scalability. We evaluated \name on 154 Python and C GitHub repositories, each with over 1,000 stars, uncovering 123 new flaws, 45\% of which could be exploited to disrupt program functionalities. Out of our 69 submitted fixes, 41 have already been confirmed or merged.

cs.CR↗

arXiv2025

Hybrid Beamforming Design for Bistatic Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities. The associated high costs can be mitigated by adopting a hybrid beamforming structure. However, the well-studied monostatic ISAC systems face challenges related to full-duplex operation. To address this issue, this paper focuses on a three-dimensional bistatic configuration that requires only half-duplex base stations. To intuitively evaluate the error bound of bistatic sensing using orthogonal frequency division multiplexing waveforms, we propose a positioning scheme that combines angle-of-arrival and time-of-arrival estimation, deriving the closed-form expression of the position error bound (PEB). Using this PEB, we develop two hybrid beamforming algorithms for joint waveform design, aimed at maximizing achievable spectral efficiency (SE) while ensuring a predefined PEB threshold. The first algorithm leverages a Riemannian trust-region approach, achieving superior performance in terms of global optima and convergence speed compared to conventional gradient-based methods, but with higher complexity. In contrast, the second algorithm, which employs orthogonal matching pursuit, offers a more computationally efficient solution, delivering reasonable SE while maintaining the PEB constraint. Numerical results are provided to validate the effectiveness of the proposed designs.

eess.SP↗

arXiv2024

Toward Unbiased Multiple-Target Fuzzing with Path Diversity

In this paper, we propose a novel directed fuzzing solution named AFLRun, which features target path-diversity metric and unbiased energy assignment. Firstly, we develop a new coverage metric by maintaining extra virgin map for each covered target to track the coverage status of seeds that hit the target. This approach enables the storage of waypoints into the corpus that hit a target through interesting path, thus enriching the path diversity for each target. Additionally, we propose a corpus-level energy assignment strategy that guarantees fairness for each target. AFLRun starts with uniform target weight and propagates this weight to seeds to get a desired seed weight distribution. By assigning energy to each seed in the corpus according to such desired distribution, a precise and unbiased energy assignment can be achieved. We built a prototype system and assessed its performance using a standard benchmark and several extensively fuzzed real-world applications. The evaluation results demonstrate that AFLRun outperforms state-of-the-art fuzzers in terms of vulnerability detection, both in quantity and speed. Moreover, AFLRun uncovers 29 previously unidentified vulnerabilities, including 8 CVEs, across four distinct programs.

cs.CR↗