Papers by Lun Wang
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
Attacker’s Noise Can Manipulate Your Audio-based LLM in the Real World (2026.eacl-long)
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| Challenge: | a recent study examines the real-world vulnerabilities of audio-based large language models such as Qwen2 . despite their impressive capabilities, large language model's are susceptible to various security exploits . |
| Approach: | They investigate the real-world vulnerabilities of audio-based large language models . they show that an adversary can craft stealthy audio perturbations to manipulate ALLMs . |
| Outcome: | The proposed attacks can be adapted to real-world scenarios and impact innocent users . the proposed attacks are scalable and can be exploited in real-time . |
From Factuality to Meta-Factivity: A Cognitive Blueprint for Trustworthy LLMs (2026.acl-short)
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| Challenge: | Current evaluation paradigms on Event Factuality Prediction (EFP) focus on static classification tasks and shortcut learning and unreliable reasoning. |
| Approach: | They propose a meta-factivity framework that moves evaluation beyond surface recognition to belief trajectory reasoning and epistemic regulation. |
| Outcome: | The proposed framework shifts from event factuality to meta-factivity . the proposed framework lays the groundwork for a more rigorous benchmark for explainable self-governance . |
RACE: Retrieval-augmented Commit Message Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to automatically generate commit messages are repetitive or redundant. |
| Approach: | They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. |
| Outcome: | The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation. |
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)
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| Challenge: | Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually. |
| Approach: | They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model . |
| Outcome: | The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST. |
Tackling Long Code Search with Splitting, Encoding, and Aggregating (2024.lrec-main)
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| Challenge: | Existing pretraining models take the first 256 tokens of code snippets by default, limiting the input length to 512. |
| Approach: | They propose a baseline SEA model which splits long code into code blocks and aggregates them to obtain a comprehensive long code representation. |
| Outcome: | The proposed model can model long code without changing their internal structure and re-pretraining. |
Accelerating Code Search with Deep Hashing and Code Classification (2022.acl-long)
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| Challenge: | Code search is to search reusable code snippets from source code corpus based on natural languages queries. |
| Approach: | They propose a method to accelerate code search with deep hashing and code classification by using deep hashes and code hash. |
| Outcome: | The proposed method can save 90% of retrieval time while preserving at least 99% of retrievals accuracy. |