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Shuai Lu

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning

Yuqin Dai
Shuo Yang
Guoqing Wang
Yong Deng
Zhanwei Zhang
Jun Yin
Pengyu Zeng
Zhenzhe Ying

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ultimately improving retrieval results in RAG systems. To address these issues, we propose WebFilter, a novel RAG framework that generates source-restricted queries and filters out unreliable content. This approach combines a retrieval filtering mechanism with a behavior- and outcome-driven reward strategy, optimizing both query formulation and retrieval outcomes. Extensive experiments demonstrate that WebFilter improves answer quality and retrieval precision, outperforming existing RAG methods on both in-domain and out-of-domain benchmarks.

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ICLR Conference 2025 Conference Paper

Alchemy: Amplifying Theorem-Proving Capability Through Symbolic Mutation

Shaonan Wu
Shuai Lu
Yeyun Gong
Nan Duan
Ping Wei

Formal proofs are challenging to write even for experienced experts. Recent progress in Neural Theorem Proving (NTP) shows promise in expediting this process. However, the formal corpora available on the Internet are limited compared to the general text, posing a significant data scarcity challenge for NTP. To address this issue, this work proposes Alchemy, a general framework for data synthesis that constructs formal theorems through symbolic mutation. Specifically, for each candidate theorem in Mathlib, we identify all invocable theorems that can be used to rewrite or apply to it. Subsequently, we mutate the candidate theorem by replacing the corresponding term in the statement with its equivalent form or antecedent. As a result, our method increases the number of theorems in Mathlib by an order of magnitude, from 110k to 6M. Furthermore, we perform continual pretraining and supervised finetuning on this augmented corpus for large language models. Experimental results demonstrate the effectiveness of our approach, achieving a 4.70% absolute performance improvement on Leandojo benchmark. Additionally, our approach achieves a 2.47% absolute performance gain on the out-of-distribution miniF2F benchmark based on the synthetic data. To provide further insights, we conduct a comprehensive analysis of synthetic data composition and the training paradigm, offering valuable guidance for developing a strong theorem prover.

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ICLR Conference 2025 Conference Paper

Automated Proof Generation for Rust Code via Self-Evolution

Tianyu Chen
Shuai Lu
Shan Lu 0001
Yeyun Gong
Chenyuan Yang
Xuheng Li
Md Rakib Hossain Misu
Hao Yu 0016

Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obsta- cle lies in the severe lack of data—there is much fewer proofs than code snippets for Large Language Models (LLMs) to train upon. In this paper, we introduce SAFE, a framework that overcomes the lack of human-written proofs to enable automated proof generation of Rust code. SAFE establishes a self-evolving cycle where data synthesis and fine-tuning collaborate to enhance the model capability, leveraging the definitive power of a symbolic verifier in telling correct proofs from incorrect ones. SAFE also re-purposes the large number of synthesized incorrect proofs to train the self-debugging capability of the fine-tuned models, empowering them to fix incorrect proofs based on the verifier’s feedback. SAFE demonstrates superior efficiency and precision compared to GPT-4o. Through tens of thousands of synthesized proofs and the self-debugging mechanism, we improve the capa- bility of open-source models, initially unacquainted with formal verification, to automatically write proofs for Rust code. This advancement leads to a signifi- cant improvement in performance, achieving a 52.52% accuracy rate in a bench- mark crafted by human experts, a significant leap over GPT-4o’s performance of 14.39%.

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ICML Conference 2025 Conference Paper

MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data

Yuqin Dai
Zhouheng Yao
Chunfeng Song
Qihao Zheng
Weijian Mai
Kunyu Peng
Shuai Lu
Wanli Ouyang

Brain decoding aims to reconstruct visual perception of human subject from fMRI signals, which is crucial for understanding brain’s perception mechanisms. Existing methods are confined to the single-subject paradigm due to substantial brain variability, which leads to weak generalization across individuals and incurs high training costs, exacerbated by limited availability of fMRI data. To address these challenges, we propose MindAligner, an explicit functional alignment framework for cross-subject brain decoding from limited fMRI data. The proposed MindAligner enjoys several merits. First, we learn a Brain Transfer Matrix (BTM) that projects the brain signals of an arbitrary new subject to one of the known subjects, enabling seamless use of pre-trained decoding models. Second, to facilitate reliable BTM learning, a Brain Functional Alignment module is proposed to perform soft cross-subject brain alignment under different visual stimuli with a multi-level brain alignment loss, uncovering fine-grained functional correspondences with high interpretability. Experiments indicate that MindAligner not only outperforms existing methods in visual decoding under data-limited conditions, but also provides valuable neuroscience insights in cross-subject functional analysis. The code will be made publicly available.

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ICRA Conference 2024 Conference Paper

A Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency

Miao Zhang 0010
Yiqing Shen 0003
Zhuowei Li 0007
Guofeng Pan
Shuai Lu

Images captured by robotics under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain. To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency. Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency. These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results. Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images.

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NeurIPS Conference 2023 Conference Paper

ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction

Jia Guo
Shuai Lu
Lize Jia
Weihang Zhang
Huiqi Li

Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e. g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in the target UAD domain, such as industrial inspection and medical imaging. In this paper, we propose a novel epistemic UAD method, namely ReContrast, which optimizes the entire network to reduce biases towards the pre-trained image domain and orients the network in the target domain. We start with a feature reconstruction approach that detects anomalies from errors. Essentially, the elements of contrastive learning are elegantly embedded in feature reconstruction to prevent the network from training instability, pattern collapse, and identical shortcut, while simultaneously optimizing both the encoder and decoder on the target domain. To demonstrate our transfer ability on various image domains, we conduct extensive experiments across two popular industrial defect detection benchmarks and three medical image UAD tasks, which shows our superiority over current state-of-the-art methods.

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NeurIPS Conference 2021 Conference Paper

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Shuai Lu
Daya Guo
Shuo Ren
Junjie Huang
Alexey Svyatkovskiy
Ambrosio Blanco
Colin Clement
Dawn Drain

Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison. CodeXGLUE also features three baseline systems, including the BERT-style, GPT-style, and Encoder-Decoder models, to make it easy for researchers to use the platform. The availability of such data and baselines can help the development and validation of new methods that can be applied to various program understanding and generation problems.

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ICLR Conference 2021 Conference Paper

GraphCodeBERT: Pre-training Code Representations with Data Flow

Daya Guo
Shuo Ren 0002
Shuai Lu
Zhangyin Feng
Duyu Tang
Shujie Liu 0001
Long Zhou
Nan Duan 0001

Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. Such a semantic-level structure is neat and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search.

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IJCAI Conference 2018 Conference Paper

Summarizing Source Code with Transferred API Knowledge

Xing Hu
Ge Li
Xin Xia
David Lo
Shuai Lu
Zhi Jin

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.

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