## 1 Introduction  
With the rapid advancement of digital technologies, foundational network infrastructure, computer hardware, and intelligent devices are playing an increasingly vital role across social, economic, and defense sectors. These technologies have improved the efficiency of information processing and transmission, accelerating the intelligent transformation of numerous industries. However, software security challenges, such as cyberattacks, unauthorized access, and leakage of personal information, are increasingly severe, posing significant threats to system reliability and operational safety. Attackers frequently exploit software vulnerabilities to conduct remote intrusions, which may result in severe disruptions to critical systems. Consequently, enhancing software security, particularly the detection of code vulnerabilities, has become an urgent area of research. Code vulnerability detection aims to identify potential security flaws in software systems. Traditional approaches are primarily based on machine learning techniques, such as static analysis, dynamic analysis, manually designed, or learning-based feature extraction methods [1, 2, 3]. In recent years, deep learning-based methods have emerged as effective alternatives, with Graph Neural Networks (GNNs) gaining particular attention [4, 5]. GNNs are capable of modeling syntactic and semantic relationships through graph-structured representations, enabling more accurate detection. As a result, GNN-based techniques have been widely adopted in the prior studies on vulnerability detection [6, 7]. The emergence of Large Language Models (LLMs), such as GPT and DeepSeek, has significantly advanced natural language and code understanding tasks. These models, trained on large-scale datasets and comprising billions of parameters, have demonstrated strong performance in areas which is previously considered challenging for artificial intelligence. The ChatGPT series has been applied in creative writing, programming assistance, and problem-solving through interactive dialogues [8; 9; 10]. This success has inspired researchers to investigate the applicability of LLMs in the field of code vulnerability detection [11; 12; 13; 14; 15]. [PERSON] et al. [14] evaluated the performance of ChatGPT across the pipeline of software vulnerability analysis. [PERSON] et al. [15] showed that a prompting strategy can improve vulnerability detection performance in non-reasoning GPT models. The preliminary studies have explored vulnerability detection using models like GPT for identifying code-level bugs; they often fail to fully consider the reasoning capabilities inherent in reasoning LLMs. Some existing works choose to focus on the prompt engineering with LLMs like GPT-4o or Claude-3.5, while comprehensive evaluations comparing reasoning and non-reasoning models remain scarce. In particular, little attention has been given to reasoning-enhanced models like DeepSeek-R1, which not only generate predictions but also provide a critical and valuable reasoning analysis process compared with the prior black-box approaches. The detailed study on the multi-step reasoning traces for vulnerability detection and the limitations in error analysis has not been fully investigated. To address these gaps, this study presents a reasoning-aware vulnerability detection framework based on DeepSeek-R1. In contrast to prior work, the proposed framework conducts the first comprehensive comparative analysis between reasoning-capable and non-reasoning LLMs on vulnerability detection. Furthermore, this work explicitly incorporates and evaluates reasoning traces, offering more accurate predictions and valuable interpretability for vulnerability remediation. The key contributions of this work are summarized as follows:  
* A novel LLM-based framework for code vulnerability detection is proposed, which explicitly incorporates reasoning traces through DeepSeek-R1. This enables interpretable detection workflows that go beyond traditional black-box results.