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Hai Jin 0001

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3 papers

1 author row

ICML Conference 2025 Conference Paper

CodeSync: Synchronizing Large Language Models with Dynamic Code Evolution at Scale

Chenlong Wang
Zhaoyang Chu
Zhengxiang Cheng
Xuyi Yang
Kaiyue Qiu
Yao Wan 0001
Zhou Zhao 0001
Xuanhua Shi

Large Language Models (LLMs) have exhibited exceptional performance in software engineering yet face challenges in adapting to continually evolving code knowledge, particularly the frequent updates of third-party library APIs. This limitation, rooted in the static pre-training datasets, often results in non-executable code or implementations with suboptimal safety and efficiency. To this end, we introduce CodeSync, a data engine to identify outdated code patterns and collect real-time code knowledge updates from Python third-party libraries. Building upon CodeSync, we develop CodeSyncBench, a comprehensive benchmark for assessing LLMs’ ability to stay synchronized with code evolution, which covers real-world updates for 220 APIs from six Python libraries. Our benchmark offers 3, 300 test cases spanning three evaluation tasks and an update-aware instruction tuning dataset of 2, 200 training samples. Extensive experiments on 14 LLMs reveal that they struggle with dynamic code evolution, even with the support of advanced knowledge updating methods (e. g. , DPO, ORPO, and SimPO). Our CodeSync lays a strong foundation for developing more effective and robust methods for real-time code knowledge updating in the future. The experimental code is available at: https: //github. com/CGCL-codes/naturalcc/tree/main/examples/codesync.

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ICML Conference 2025 Conference Paper

Dataflow-Guided Neuro-Symbolic Language Models for Type Inference

Ge Li 0001
Yao Wan 0001
Hongyu Zhang 0002
Zhou Zhao 0001
Wenbin Jiang 0001
Xuanhua Shi
Hai Jin 0001
Zheng Wang 0001

Language Models (LMs) are increasingly used for type inference, aiding in error detection and software development. Some real-world deployments of LMs require the model to run on local machines to safeguard the intellectual property of the source code. This setting often limits the size of the LMs that can be used. We present Nester, the first neuro-symbolic approach that enhances LMs for type inference by integrating symbolic learning without increasing model size. Nester breaks type inference into sub-tasks based on the data and control flow of the input code, encoding them as a modular high-level program. This program executes multi-step actions, such as evaluating expressions and analyzing conditional branches of the target code, combining static typing with LMs to infer potential types. Evaluated on the ManyTypes4Py dataset in Python, Nester outperforms two state-of-the-art type inference methods (HiTyper and TypeGen), achieving 70. 7% Top-1 Exact Match, which is 18. 3% and 3. 6% higher than HiTyper and TypeGen, respectively. For complex type annotations like typing. Optional and typing. Union, Nester achieves 51. 0% and 16. 7%, surpassing TypeGen by 28. 3% and 5. 8%.

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ICML Conference 2024 Conference Paper

Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models

Xiaofan Bai
Chaoxiang He
Xiaojing Ma 0002
Bin Zhu 0008
Hai Jin 0001

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples’ expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at https: //github. com/CGCL-codes/IBSF.

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