arXiv subjects

Xinda Wang

Publications and source records attributed to Xinda Wang.

At least 19 recordsLinked to original sources

arXiv2026

Who Leads Now? Token-Level Modality Arbitration for Chart-to-Code Generation

Chart-to-code generation requires a model to read the fine-grained visual details of a chart and write executable code that reproduces it. Existing chart-to-code methods either train visual and coding abilities separately, or fine-tune on chart-to-code data with the two abilities entangled. Neither strategy accounts for the distinct nature of the two abilities or the interference that arises when they are optimized together. We propose MoCA (Mixture of Cross-modal Arbitration), which separates the two abilities rather than blending them. MoCA is built on Cross-modal Arbitration Block (CAB), which maintains a visual branch and a code branch as two distinct pathways, and a lightweight arbiter that arbitrates their relative contributions at every layer and generated token. We train MoCA in two stages: a supervised warm-up on self-distilled reasoning trajectories that decomposes visual understanding into explicit steps, followed by reinforcement learning with rewards on both the reasoning process and the final code. Analysis shows that the arbiter learns structured rather than arbitrary allocations, with expert contributions varying systematically across tokens, layers, and instances. Across three benchmarks, MoCA delivers competitive performance against general-domain and chart-specialized models. Ablation results show that the gains cannot be attributed to a larger model size alone, but instead arise from the joint contributions of complementary visual and code branch initialization and input-conditioned arbitration through CAB.

cs.AI↗

arXiv2026

Why Not Fix It Once and for All? An Empirical Study of Multiple Patches for Vulnerability Fixes in Open-Source Software

Security patches for open-source software constitute a foundational resource for vulnerability remediation research and practice. However, analyzing and applying multiple patches remains challenging, especially when trying to determine at what point in a patch sequence a vulnerability is fully remediated. This paper presents a systematic analysis of multi-patch vulnerability fixes, focusing on their root causes, characteristics, and methods for verifying remediation status throughout the fixing process. Through a manual examination of 1,646 multi-patch fix records, we develop a taxonomy with three primary categories and six subcategories based on their causes. We then compare the distinctive characteristics of multi-patch fixes with those of single-patch fixes and analyze feature variations across categories. In addition, we assess representative vulnerability detection methods for validating complete remediation during multi-patch fixing. Our findings provide new insights into multi-patch fixes and lay a foundation for future research in this field.

cs.CR↗

arXiv2026

Triviality Corrected Endogenous Reward

Reinforcement learning for open-ended text generation is constrained by the lack of verifiable rewards, necessitating reliance on judge models that require either annotated data or powerful closed-source models. Inspired by recent work on unsupervised reinforcement learning for mathematical reasoning using confidence-based endogenous rewards, we investigate whether this principle can be adapted to open-ended writing tasks. We find that directly applying confidence rewards leads to Triviality Bias: the policy collapses toward high-probability outputs, reducing diversity and meaningful content. We propose TCER (Triviality Corrected Endogenous Reward), which addresses this bias by rewarding the relative information gain between a specialist policy and a generalist reference policy, modulated by a probability-dependent correction mechanism. Across multiple writing benchmarks and model architectures, TCER achieves consistent improvements without external supervision. Furthermore, TCER also transfers effectively to mathematical reasoning, validating the generality of our approach across different generation tasks.

cs.CL↗

arXiv2026

From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection

The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While post-training techniques have shown promise in general coding tasks, their systematic application to VD remains underexplored. In this paper, we present the first comprehensive investigation into the post-training pipeline for LLM-based VD, demonstrating that on-policy RL with GRPO consistently outperforms SFT, off-policy preference optimization methods, and specialized VD LLMs. Our study further reveals VD-specific post-training guidelines and insights beyond common practices: (1) For data curation, contrary to the widespread use of rationalization-based supervision in prior VD work, SFT based on rejection sampling proves more effective, as rationalization can introduce hallucinations; in RL training, the inherently skewed difficulty distribution of vulnerabilities leads difficulty-aware data filtering to drastically reduce data coverage, causing non-negligible performance loss, and undermines curriculum learning, while pair-based data scheduling can partially mitigate this. (2) For stage interactions, unlike preference optimization typically applied to lightly trained SFT models, increasing SFT epochs consistently benefits off-policy preference optimization in VD tasks; however, excessive SFT suppresses self-exploration in on-policy RL, limiting its gains. (3) For reward mechanisms, naively treating vulnerability classification correctness as reward signals leads to reward hacking, whereas fine-grained root-cause judgments provide more reliable credit assignment; specification-based rewards further improve efficiency at the cost of additional design and generation effort. (4) For evaluation protocols, LLM-as-a-Judge based on root-cause analysis offers a more robust alternative, albeit with variability across judge models.

cs.CR↗

arXiv2025

VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization

Large language models (LLMs) have recently shown strong potential in vulnerability detection (VD). However, accurately detecting vulnerabilities in real-world repositories requires reasoning over complex contextual interactions. Existing LLM-based VD approaches remain limited because current datasets lack complete contextual information and high-quality reasoning supervision, while existing optimization methods primarily rely on coarse outcome-centric supervision signals that fail to model the vulnerability reasoning process. To address these limitations, we first construct ContextVul, a new dataset that augments high-quality function-level vulnerability benchmarks with repository-level contextual information and curated vulnerability reasoning traces. Building upon ContextVul, we introduce a two-stage optimization framework consisting of lightweight cold-start supervised fine-tuning followed by vulnerability-adaptive on-policy optimization (VULPO). VULPO incorporates multidimensional rewards that jointly evaluate vulnerability identification, vulnerability-relevant localization, and causal reasoning quality, along with difficulty-adaptive reward scaling to mitigate reward hacking and improve RL effectiveness. Extensive experiments demonstrate the superiority of VULPO for context-aware VD. Our VULPO-4B, the first specialized vulnerability reasoning LLM, substantially outperforms existing VD baselines, improving Pairwise Pass@1 by 203% relative to Qwen3-4B and achieving competitive performance against a 150% larger-scale LLM, DeepSeek-V3.1.

cs.CR↗

arXiv2025

Style Attack Disguise: When Fonts Become a Camouflage for Adversarial Intent

With social media growth, users employ stylistic fonts and font-like emoji to express individuality, creating visually appealing text that remains human-readable. However, these fonts introduce hidden vulnerabilities in NLP models: while humans easily read stylistic text, models process these characters as distinct tokens, causing interference. We identify this human-model perception gap and propose a style-based attack, Style Attack Disguise (SAD). We design two sizes: light for query efficiency and strong for superior attack performance. Experiments on sentiment classification and machine translation across traditional models, LLMs, and commercial services demonstrate SAD's strong attack performance. We also show SAD's potential threats to multimodal tasks including text-to-image and text-to-speech generation.

cs.CL↗

arXiv2025

NatGVD: Natural Adversarial Example Attack towards Graph-based Vulnerability Detection

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes NatGVD, a novel attack methodology that generates natural adversarial vulnerable code to circumvent GNN-based and graph-aware transformer-based vulnerability detectors. NatGVD employs a set of code transformations that modify graph structure while preserving code semantics. Instead of injecting dead or unrelated code like previous works, NatGVD considers naturalness requirements: generated examples should not be easily recognized by humans or program analysis tools. With extensive evaluation of NatGVD on state-of-the-art vulnerability detection systems, the results reveal up to 53.04% evasion rate across GNN-based detectors and graph-aware transformer-based detectors. We also explore potential defense strategies to enhance the robustness of these systems against NatGVD.

cs.CR↗

arXiv2025

MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement

The widespread adoption of open-source software (OSS) necessitates the mitigation of vulnerability risks. Most vulnerability detection (VD) methods are limited by inadequate contextual understanding, restrictive single-round interactions, and coarse-grained evaluations, resulting in undesired model performance and biased evaluation results. To address these challenges, we propose MAVUL, a novel multi-agent VD system that integrates contextual reasoning and interactive refinement. Specifically, a vulnerability analyst agent is designed to flexibly leverage tool-using capabilities and contextual reasoning to achieve cross-procedural code understanding and effectively mine vulnerability patterns. Through iterative feedback and refined decision-making within cross-role agent interactions, the system achieves reliable reasoning and vulnerability prediction. Furthermore, MAVUL introduces multi-dimensional ground truth information for fine-grained evaluation, thereby enhancing evaluation accuracy and reliability. Extensive experiments conducted on a pairwise vulnerability dataset demonstrate MAVUL's superior performance. Our findings indicate that MAVUL significantly outperforms existing multi-agent systems with over 62% higher pairwise accuracy and single-agent systems with over 600% higher average performance. The system's effectiveness is markedly improved with increased communication rounds between the vulnerability analyst agent and the security architect agent, underscoring the importance of contextual reasoning in tracing vulnerability flows and the crucial feedback role. Additionally, the integrated evaluation agent serves as a critical, unbiased judge, ensuring a more accurate and reliable estimation of the system's real-world applicability by preventing misleading binary comparisons.

cs.CR↗

arXiv2025

EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation

Although the effectiveness of Large Language Models (LLMs) as judges (LLM-as-a-judge) has been validated, their performance remains limited in open-ended tasks, particularly in story evaluation. Accurate story evaluation is crucial not only for assisting human quality judgment but also for providing key signals to guide story generation. However, existing methods face a dilemma: prompt engineering for closed-source models suffers from poor adaptability, while fine-tuning approaches for open-source models lack the rigorous reasoning capabilities essential for story evaluation. To address this, we propose the Self-Evolving Pairwise Reasoning (EvolvR) framework. Grounded in pairwise comparison, the framework first self-synthesizes score-aligned Chain-of-Thought (CoT) data via a multi-persona strategy. To ensure data quality, these raw CoTs undergo a self-filtering process, utilizing multi-agents to guarantee their logical rigor and robustness. Finally, the evaluator trained on the refined data is deployed as a reward model to guide the story generation task. Experimental results demonstrate that our framework achieves state-of-the-art (SOTA) performance on three evaluation benchmarks including StoryER, HANNA and OpenMEVA. Furthermore, when served as a reward model, it significantly enhances the quality of generated stories, thereby fully validating the superiority of our self-evolving approach.

cs.CL↗

arXiv2025

TASE: Token Awareness and Structured Evaluation for Multilingual Language Models

While large language models (LLMs) have demonstrated remarkable performance on high-level semantic tasks, they often struggle with fine-grained, token-level understanding and structural reasoning--capabilities that are essential for applications requiring precision and control. We introduce TASE, a comprehensive benchmark designed to evaluate LLMs' ability to perceive and reason about token-level information across languages. TASE covers 10 tasks under two core categories: token awareness and structural understanding, spanning Chinese, English, and Korean, with a 35,927-instance evaluation set and a scalable synthetic data generation pipeline for training. Tasks include character counting, token alignment, syntactic structure parsing, and length constraint satisfaction. We evaluate over 30 leading commercial and open-source LLMs, including O3, Claude 4, Gemini 2.5 Pro, and DeepSeek-R1, and train a custom Qwen2.5-14B model using the GRPO training method. Results show that human performance significantly outpaces current LLMs, revealing persistent weaknesses in token-level reasoning. TASE sheds light on these limitations and provides a new diagnostic lens for future improvements in low-level language understanding and cross-lingual generalization. Our code and dataset are publicly available at https://github.com/cyzcz/Tase .

cs.CL↗

arXiv2025

Revisiting Pre-trained Language Models for Vulnerability Detection

The rapid advancement of pre-trained language models (PLMs) has demonstrated promising results for various code-related tasks. However, their effectiveness in detecting real-world vulnerabilities remains a critical challenge. While existing empirical studies evaluate PLMs for vulnerability detection (VD), they suffer from data leakage, limited scope, and superficial analysis, hindering the accuracy and comprehensiveness of evaluations. This paper begins by revisiting the common issues in existing research on PLMs for VD through the evaluation pipeline. It then proceeds with an accurate and extensive evaluation of 18 PLMs on high-quality datasets that feature accurate labeling, diverse vulnerability types, and various projects. Specifically, we compare the performance of PLMs under both fine-tuning and prompt engineering, assess their effectiveness and generalizability across various training and testing settings, and analyze their robustness to a series of perturbations. Our findings reveal that PLMs incorporating pre-training tasks designed to capture the syntactic and semantic patterns of code outperform both general-purpose PLMs and those solely pre-trained or fine-tuned on large code corpora. However, these models face notable challenges in real-world scenarios, such as difficulties in detecting vulnerabilities with complex dependencies, handling perturbations introduced by code normalization and abstraction, and identifying semantic-preserving vulnerable code transformations. Also, the truncation caused by the limited context windows of PLMs can lead to a non-negligible number of labeling errors, which is overlooked by previous work. This study underscores the importance of thorough evaluations of model performance in practical scenarios and outlines future directions to help enhance the effectiveness of PLMs for realistic VD applications.

cs.CR↗

arXiv2025

OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases

Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning requires integrating structured external knowledge such as knowledge graphs, code snippets, or formal logic. This limitation is partly due to the absence of benchmarks capable of systematically evaluating LLM performance across diverse structured knowledge modalities. To address this gap, we introduce \textbf{\textsc{OneEval}}, a comprehensive benchmark explicitly designed to assess the knowledge-intensive reasoning capabilities of LLMs across four structured knowledge modalities, unstructured text, knowledge graphs, code, and formal logic, and five critical domains (general knowledge, government, science, law, and programming). \textsc{OneEval} comprises 4,019 carefully curated instances and includes a challenging subset, \textsc{OneEval}\textsubscript{Hard}, consisting of 1,285 particularly difficult cases. Through extensive evaluation of 18 state-of-the-art open-source and proprietary LLMs, we establish three core findings: a) \emph{persistent limitations in structured reasoning}, with even the strongest model achieving only 32.2\% accuracy on \textsc{OneEval}\textsubscript{Hard}; b) \emph{performance consistently declines as the structural complexity of the knowledge base increases}, with accuracy dropping sharply from 53\% (textual reasoning) to 25\% (formal logic); and c) \emph{diminishing returns from extended reasoning chains}, highlighting the critical need for models to adapt reasoning depth appropriately to task complexity. We release the \textsc{OneEval} datasets, evaluation scripts, and baseline results publicly, accompanied by a leaderboard to facilitate ongoing advancements in structured knowledge reasoning.

cs.CL↗

arXiv2025

PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models

Prompt optimization is a practical and widely applicable alternative to fine tuning for improving large language model performance. Yet many existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned. We present PMPO (Probabilistic Metric Prompt Optimization), a unified framework that uses token level cross entropy as a direct, lightweight evaluation signal. PMPO locates low quality prompt segments via a masking based analysis and iteratively rewrites them to propose improved variants. Crucially, during evaluation, PMPO selects among variants by minimizing loss in a single forward pass, eliminating output sampling and human or judge based scoring for selection while still using standard generation only to propose rewrites. This unified, loss based strategy supports both supervised and preference based tasks. Across model sizes and datasets, PMPO outperforms prior prompt optimizers: it achieves the highest average accuracy on BBH, performs strongly on GSM8K and AQUA RAT, and raises AlpacaEval 2.0 win rates by over 19 points. These results demonstrate PMPO's effectiveness, efficiency, and broad applicability.

cs.CL↗

arXiv2025

SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning

Recent advancements in large language models (LLMs) have accelerated progress toward artificial general intelligence, yet their potential to generate harmful content poses critical safety challenges. Existing alignment methods often struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks. In this work, we propose SAFER, a framework for Safety Alignment via eFficient Ex-Ante Reasoning. Our approach instantiates structured Ex-Ante reasoning through initial assessment, rule verification, and path calibration, and embeds predefined safety rules to provide transparent and verifiable safety judgments. Specifically, our approach consists of two training stages: (1) supervised fine-tuning with synthetic traces to teach the multi-stage Ex-Ante reasoning, and (2) step-level reasoning preference optimization to jointly enhance safety, utility, and efficiency. Experiments on multiple open-source LLMs demonstrate that SAFER significantly enhances safety performance while maintaining helpfulness and response efficiency.

cs.CL↗

arXiv2024

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization

Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non-IID) of user data and vulnerability to Byzantine threats. To address these challenges, in this paper, we propose FedCAP, a robust FL framework against both data heterogeneity and Byzantine attacks. The core of FedCAP is a model update calibration mechanism to help a server capture the differences in the direction and magnitude of model updates among clients. Furthermore, we design a customized model aggregation rule that facilitates collaborative training among similar clients while accelerating the model deterioration of malicious clients. With a Euclidean norm-based anomaly detection mechanism, the server can quickly identify and permanently remove malicious clients. Moreover, the impact of data heterogeneity and Byzantine attacks can be further mitigated through personalization on the client side. We conduct extensive experiments, comparing multiple state-of-the-art baselines, to demonstrate that FedCAP performs well in several non-IID settings and shows strong robustness under a series of poisoning attacks.

cs.LG↗

arXiv2024

Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation

With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-based vulnerability detection systems, particularly those leveraging pre-trained language models, have demonstrated significant potential in promptly identifying vulnerabilities in communication networks and reducing the risk of exploitation. However, the shortage of accurately labeled vulnerability datasets hinders further progress in this field. Failing to represent real-world vulnerability data variety and preserve vulnerability semantics, existing augmentation approaches provide limited or even counterproductive contributions to model training. In this paper, we propose a data augmentation technique aimed at enhancing the performance of pre-trained language models for vulnerability detection. Given the vulnerability dataset, our method performs natural semantic-preserving program transformation to generate a large volume of new samples with enriched data diversity and variety. By incorporating our augmented dataset in fine-tuning a series of representative code pre-trained models (i.e., CodeBERT, GraphCodeBERT, UnixCoder, and PDBERT), up to 10.1% increase in accuracy and 23.6% increase in F1 can be achieved in the vulnerability detection task. Comparison results also show that our proposed method can substantially outperform other prominent vulnerability augmentation approaches.

cs.CR↗

arXiv2024

Bridging the Gap: A Study of AI-based Vulnerability Management between Industry and Academia

Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management. AI-based models are reported to greatly outperform traditional static analysis tools, indicating a substantial workload relief for security engineers. However, the industry remains very cautious and selective about integrating AI-based techniques into their security vulnerability management workflow. To understand the reasons, we conducted a discussion-based study, anchored in the authors' extensive industrial experience and keen observations, to uncover the gap between research and practice in this field. We empirically identified three main barriers preventing the industry from adopting academic models, namely, complicated requirements of scalability and prioritization, limited customization flexibility, and unclear financial implications. Meanwhile, research works are significantly impacted by the lack of extensive real-world security data and expertise. We proposed a set of future directions to help better understand industry expectations, improve the practical usability of AI-based security vulnerability research, and drive a synergistic relationship between industry and academia.

cs.CR↗

arXiv2024

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of "scientific language", whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs.

cs.CL↗