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Shuo Lu

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Beyond Boundaries: Leveraging Vision Foundation Models for Source-Free Object Detection

Huizai Yao
Sicheng Zhao
Pengteng Li
Yi Cui
Shuo Lu
Weiyu Guo
Yunfan Lu
Yijie Xu

Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transferability and discriminability. In contrast, Vision Foundation Models (VFMs), pretrained on massive and diverse data, exhibit strong perception capabilities and broad generalization, yet their potential remains largely untapped in the SFOD setting. In this paper, we propose a novel SFOD framework that leverages VFMs as external knowledge sources to jointly enhance feature alignment and label quality. Specifically, we design three VFM-based modules: (1) Patch-weighted Global Feature Alignment (PGFA) distills global features from VFMs using patch-similarity–based weighting to enhance global feature transferability; (2) Prototype-based Instance Feature Alignment (PIFA) performs instance-level contrastive learning guided by momentum-updated VFM prototypes; and (3) Dual-source Enhanced Pseudo-label Fusion (DEPF) fuses predictions from detection VFMs and teacher models via an entropy-aware strategy to yield more reliable supervision. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art SFOD performance, validating the effectiveness of integrating VFMs to simultaneously improve transferability and discriminability.

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AAAI Conference 2026 Conference Paper

GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging

Ziyi Ni
Huacan Wang
Shuo Zhang
Shuo Lu
Ziyang He
WangYou
Zhenheng Tang
Sen Hu

Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evaluate code agents in such authentic, workflow-driven scenarios. To bridge this gap, we introduce GitTaskBench, a benchmark designed to systematically assess this capability via 54 realistic tasks across 7 modalities and 7 domains. Each task pairs a relevant repository with an automated, human-curated evaluation harness specifying practical success criteria. Beyond measuring execution and task success, we also propose the alpha-value metric to quantify the economic benefit of agent performance, which integrates task success rates, token cost, and average developer salaries. Experiments across three state-of-the-art agent frameworks with multiple advanced LLMs show that leveraging code repositories for complex task solving remains challenging: even the best-performing system, OpenHands+Claude 3.7, solves only 48.15% of tasks. Error analysis attributes over half of failures to seemingly mundane yet critical steps like environment setup and dependency resolution, highlighting the need for more robust workflow management and increased timeout preparedness. By releasing GitTaskBench, we aim to drive progress and attention toward repository-aware code reasoning, execution, and deployment---moving agents closer to solving complex, end-to-end real-world tasks.

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AAAI Conference 2026 Conference Paper

RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation

Run Ling
Wenji Wang
Yuting Liu
Guibing Guo
Haowei Liu
Jian Lu
Quanwei Zhang
Yexing Xu

Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effective, existing methods face two main issues. First, existing methods treat all items in the user's historical sequence equally when extracting user preferences, overlooking the varying semantic similarities between historical items and the reference item. Disproportionately high weights for low-similarity items distort user visual preferences for the reference item. Second, existing methods heavily rely on consistency between generated and reference images to optimize generation, which leads to underfitting user preferences and hinders personalization. To address these issues, we propose Retrieval Augmented Personalized Image GenerAtion guided by Recommendation (RAGAR). Our approach uses a retrieval mechanism to assign different weights to historical items according to their similarities to the reference item, thereby extracting more refined users' visual preferences for the reference item. Then we introduce a novel rank task based on the multi-modal ranking model to optimize the personalization of the generated images instead of forcing depend on consistency. Extensive experiments and human evaluations on three real-world datasets demonstrate that RAGAR achieves significant improvements in both personalization and semantic metrics compared to five baselines.

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YNIMG Journal 2025 Journal Article

Natural rhythmic speech activates network reorganization with frontal community enhancing communication efficiency in patients with intrinsic brain tumor

Leyan Gao
Zhirui Yang
Yuyao Zhou
Jingwen Yang
Qinqin Luo
Ruiyan Feng
Keting Ou
Rui Feng

Brain tumors provide unique insights into brain plasticity due to their slow growth compared to acute cerebrovascular diseases. Despite relying on sophisticated functional networks, patients with brain tumors exhibit minimal deficits in higher language functions and demonstrate positive post-injury plasticity; however, the underlying neural mechanisms remain unclear. We utilized high-density electroencephalography to investigate language network plasticity in brain tumor patients without evident language deficits. Natural rhythmic sentences and non-rhythmic sentences with contrasting speech prosodic harmony were employed to examine the impact of task integrativeness on functional network reorganization. Our study reveals that rhythmic speech perception, characterized by higher processing integrativeness, induced inhibited task engagement in the frontal lobe but evoked enhanced hubness and modularity, which supported the generation of new connections and promoted the efficiency of global connectivity. Furthermore, local invasion in the frontal lobe prompted adjacent hubs to generate enriched connections during the early processing phase, facilitating later functional reorganization. Our findings underscore the significant role of global hubs in language network plasticity and reveal the importance of highly integrated tasks for network reorganization in language rehabilitation.

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NeurIPS Conference 2025 Conference Paper

RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving

Huacan Wang
Ziyi Ni
Shuo Zhang
Shuo Lu
Sen Hu
Ziyang He
Chen Hu
Jiaye Lin

The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks typically demand full-fledged code repositories rather than simple scripts. Building such repositories from scratch remains a major challenge. Fortunately, GitHub hosts a vast, evolving collection of open-source repositories, which developers frequently reuse as modular components for complex tasks. Yet, existing frameworks like OpenHands and SWE-Agent still struggle to effectively leverage these valuable resources. Relying solely on README files provides insufficient guidance, and deeper exploration reveals two core obstacles: overwhelming information and tangled dependencies of repositories, both constrained by the limited context windows of current LLMs. To tackle these issues, we propose RepoMaster, an autonomous agent framework designed to explore and reuse GitHub repositories for solving complex tasks. For efficient understanding, RepoMaster constructs function-call graphs, module-dependency graphs, and hierarchical code trees to identify essential components, providing only identified core elements to the LLMs rather than the entire repository. During autonomous execution, it progressively explores related components using our exploration tools and prunes information to optimize context usage. Evaluated on the adjusted MLE-bench, RepoMaster achieves a 110\% relative boost in valid submissions over the strongest baseline OpenHands. On our newly released GitTaskBench, RepoMaster lifts the task-pass rate from 40. 7% to 62. 9% while reducing token usage by 95%. Our code and demonstration materials are publicly available at https: //github. com/QuantaAlpha/RepoMaster.

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YNIMG Journal 2024 Journal Article

Integrated sentence-level speech perception evokes strengthened language networks and facilitates early speech development

Qinqin Luo
Leyan Gao
Zhirui Yang
Sihui Chen
Jingwen Yang
Shuo Lu

Natural poetic speeches (i.e., proverbs, nursery rhymes, and commercial ads) with strong prosodic regularities are easily memorized by children and the harmonious acoustic patterns are suggested to facilitate their integrated sentence processing. Do children have specific neural pathways for perceiving such poetic utterances, and does their speech development benefit from it? We recorded the task-induced hemodynamic changes of 94 children aged 2 to 12 years using functional near-infrared spectroscopy (fNIRS) while they listened to poetic and non-poetic natural sentences. Seventy-three adult as controls were recruited to investigate the developmental specificity of children group. The results indicated that poetic sentences perceiving is a highly integrated process featured by a lower brain workload in both groups. However, an early activated large-scale network was induced only in the child group, coordinated by hubs for connectivity diversity. Additionally, poetic speeches evoked activation in the phonological encoding regions in the children's group rather than adult controls which decreases with children's ages. The neural responses to poetic speeches were positively linked to children's speech communication performance, especially the fluency and semantic aspects. These results reveal children's neural sensitivity to integrated speech perception which facilitate early speech development by strengthening more sophisticated language networks and the perception-production circuit.

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