Papers by Yingjie Xia
VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection (2026.acl-long)
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| Challenge: | Existing static analysis tools focus on functional correctness and depend heavily on manual rules. |
| Approach: | They propose a framework that generates executable Traversal Detection Patterns (TDPs) to help detect hardware vulnerabilities. |
| Outcome: | The proposed framework improves the F1 score by 133% compared to LLM-based methods. |
CSTree-SRI: Introspection-Driven Cognitive Semantic Tree for Multi-Turn Question Answering over Extra-Long Contexts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in natural language processing (NLP), particularly in single-turn question answering (QA) on short-text. |
| Approach: | They propose a framework that captures logical correlations across chunks of ELC and maintains coherence of multi-turn Questions. |
| Outcome: | The proposed framework is able to capture logical correlations across chunks of ELC and maintain coherence of multi-turn Questions. |
SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking (2026.acl-long)
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| Challenge: | Existing methods for jailbreak ignore the semantic differences between categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness. |
| Approach: | They propose a category-aware jailbreak framework that incorporates the semantic category of harmful questions into prompt generation. |
| Outcome: | The proposed framework improves attack success rates and category alignment and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines. |
CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation (2026.findings-acl)
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| Challenge: | Existing methods for automating program repair face insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. |
| Approach: | They propose a multi-location automatic repair method via cascading planning and generation . they propose to model dependencies among bugs and cluster them to ensure rationality . |
| Outcome: | The proposed method resolves 84 multi-location bugs, achieving a 31% improvement over current methods. |