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Yi Lu

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16 papers

2 author rows

TMLR Journal 2026 Journal Article

StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

Jialin Yang
Dongfu Jiang
Tony He
Sherman Siu
Yuxuan Zhang
Disen Liao
Zhuofeng Li
Huaye Zeng

As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. We introduce $\textbf{StructEval}$, a comprehensive benchmark for evaluating LLMs' capabilities in producing both non-renderable (JSON, YAML, CSV) and renderable (HTML, React, SVG) structured formats. Unlike prior benchmarks, StructEval systematically evaluates structural fidelity across diverse formats through two paradigms: $\textbf{1)}$ generation tasks, producing structured output from natural language prompts, and $\textbf{2)}$ conversion tasks, translating between structured formats. Our benchmark encompasses 18 formats and 44 types of task, with novel metrics for format adherence and structural correctness. Results reveal significant performance gaps—even state-of-the-art models like o1-mini achieve only $75.58$ average score, with open-source alternatives lagging approximately $10$ points behind. We find generation tasks more challenging than conversion tasks, and producing correct visual content more difficult than generating text-only structures.

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AAAI Conference 2026 Conference Paper

Unlearning in Cross-Modal Retrieval via Prior-Prototype Guided Partitioned Dampening

Yi Lu
Shu Li
Yurong Qian

Selective deletion of data from deep models, known as unlearning, has become crucial for enforcing the right to be forgotten, while also mitigating the negative impact of flawed training data. Retraining deep models is often impractical due to data access restrictions and computational overhead. Existing retraining-free methods are typically based on the Fisher Information Matrix (FIM), which quantifies the importance of model parameters with respect to forgetting classes, applying equal dampening to these parameters. This approach implicitly assumes a semantically uniform representation space, where all retained classes are equidistant from the forgetting classes. However, this assumption often fails in real-world cross-modal retrieval scenarios characterized by multi-label and non-orthogonal semantics. To overcome this limitation, we propose Prior-Prototype guided Partitioned dampening (PPP), an effective strategy for selective forgetting in cross-modal retrieval. First, PPP defines prior-prototypes, which are semantic centers derived from well-trained models, to identify neighbor classes semantically close to the forgetting set. Then, PPP uses Fisher information to identify parameters sensitive to forgetting and partitions them into buffer and core regions based on their relative importance to the neighbor and retained sets. Finally, PPP applies a hierarchical dampening strategy, where core parameters receive stronger suppression guided by prototype-based semantic disparities. Comprehensive evaluations on four large-scale benchmarks show that PPP performs competitively with retraining-based baselines, highlighting its effectiveness and generalizability in selective unlearning for cross-modal retrieval.

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EAAI Journal 2025 Journal Article

A novel object detection model for sugar beet Cercospora leaf spot in field scenarios based on large kernel decomposition and spatial channel interaction attention

Hualong Dong
Yi Lu
Yurong Qian
Xuefei Ning
Ting Chen
Ke Tang

Cercospora leaf spot (CLS) is a widespread disease that seriously threatens beet yield and sugar quality. Timely detection enables farmers to take early control measures and reduce economic losses. Although artificial intelligence (AI)-based methods are replacing manual inspection in agriculture, CLS detection in complex field environments remains highly challenging due to subtle early-stage symptoms and severe occlusions caused by overlapping leaves and weeds. To address these challenges, this paper presents Cercospora Leaf Spot–You Only Look Once (CLS–YOLO), an enhanced detection model built upon You Only Look Once version 11 (YOLOv11), incorporating novel modules specifically designed for accurate CLS detection under challenging field conditions. To improve the detection of weak and early-stage symptoms, we design the Multi-Scale Large Kernel Decomposition (MSLKD) module, which enhances feature extraction for subtle lesions. Furthermore, we develop the Spatial-Channel Interaction Attention (SCIA) module to mitigate detection errors arising from occlusion and fragmented disease patterns by refining multi-scale feature representations. Experimental results demonstrate CLS–YOLO achieves superior performance, reaching an mAP@0. 5 of 73. 6% ± 0. 2% and an mAP@0. 5: 0. 95 of 40. 6% ± 0. 3% over five independent runs, outperforming twelve mainstream object detection algorithms while maintaining lightweight efficiency. To validate generalization capability across scenarios, crops, and diseases, we conducted comparative experiments on two public crop disease datasets, where our method achieved superior overall performance. In summary, this study provides an effective AI-driven solution for precise crop disease detection, contributing to the practical advancement of intelligent agriculture.

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YNICL Journal 2025 Journal Article

Alterations of long-range association fibers in patients with anti-N-methyl-D-aspartate receptor encephalitis

Xiaodong Chen
Ling Fang
Yiying Huang
Yu Huang
Yi Lu
Jinhui Wang
Chunxin Liu
Huanquan Liao

BACKGROUND: Patients with anti-NMDAR encephalitis typically exhibit impaired cognitive integration, which relies on the integrity of long-range association fibers connecting diverse brain regions. However, the microstructural integrity of long-range association fibers in this population remains unknown. METHODS: Diffusion tensor imaging (DTI) data were collected from 32 patients with anti-NMDAR encephalitis and 30 healthy controls. Patients were further categorized into early and delayed immunotherapy subgroups based on a 2-week threshold for immunotherapy initiation. The diffusion properties of major long-range association fibers were quantified at both the bundle and node levels. RESULTS: Compared with healthy controls, patients exhibited widespread microstructural damage within long-range association fibers, with more severe alterations in the delayed immunotherapy subgroup (FDR-corrected p < 0.05). In this subgroup(n = 14), radial diffusivity (RD) of left inferior fronto-occipital fasciculus (IFOF), left inferior longitudinal fasciculus (ILF), left superior longitudinal fascicles (SLF), and bilateral arcuate fascicles correlated significantly with global cognition (MMSE, FDR-corrected p < 0.05). Notably, RD also strongly correlated with working memory in the delayed immunotherapy subgroup, showing bundle-wise associations for IFOF (left: r = -0.8315, p = 0.0112; right: r = -0.7044, p = 0.0295), ILF (left: r = -0.7473, p = 0.0243), SLF (left: r = -0.7562, p = 0.0243; right: r = -0.6599, p = 0.0391), and arcuate fasciculus (left: r = -0.7240, p = 0.0272; right: r = -0.6835, p = 0.0333), with left-hemisphere predominance confirmed by node-wise analyses of IFOF, ILF, SLF, and arcuate fasciculus (FDR-corrected p < 0.05). CONCLUSIONS: Our findings highlight widespread microstructural damage in long-range association fibers in patients with anti-NMDAR encephalitis, particularly in those with delayed immunotherapy. This damage may serve as the neurophysiological basis for cognitive impairments, with working memory being most affected.

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NeurIPS Conference 2025 Conference Paper

Understanding Parametric and Contextual Knowledge Reconciliation within Large Language Models

Jun Zhao
Yongzhuo Yang
Xiang Hu
Jingqi Tong
Yi Lu
Wei Wu
Tao Gui
Qi Zhang

Retrieval-Augmented Generation (RAG) provides additional contextual knowledge to complement the parametric knowledge in Large Language Models (LLMs). These two knowledge interweave to enhance the accuracy and timeliness of LLM responses. However, the internal mechanisms by which LLMs utilize these knowledge remain unclear. We propose modeling the forward propagation of knowledge as an entity flow, employing this framework to trace LLMs' internal behaviors when processing mixed-source knowledge. Linear probing utilizes a trainable linear classifier to detect specific attributes in hidden layers. However, once trained, a probe cannot adapt to dynamically specified entities. To address this challenge, we construct an entity-aware probe, which introduces special tokens to mark probing targets and employs a small trainable rank-8 lora update to process these special markers. We first verify this approach through an attribution experiment, demonstrating that it can accurately detect information about ad-hoc entities from complex hidden states. Next, we trace entity flows across layers to understand how LLMs reconcile conflicting knowledge internally. Our probing results reveal that contextual and parametric knowledge are routed between tokens through distinct sets of attention heads, supporting attention competition only within knowledge types. While conflicting knowledge maintains a residual presence across layers, aligned knowledge from multiple sources gradually accumulates, with the magnitude of this accumulation directly determining its influence on final outputs.

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AAAI Conference 2025 Conference Paper

Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark

Yongliang Wu
Wenbo Zhu
Jiawang Cao
Yi Lu
Bozheng Li
Weiheng Chi
Zihan Qiu
Lirian Su

The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times. Despite notable advancements in the fields of video summarization and highlight detection, which can create partially usable short films from raw videos, these approaches are often domain-specific and require an in-depth understanding of real-world video content. To tackle this predicament, we propose Repurpose-10K, an extensive dataset comprising over 10,000 videos with more than 120,000 annotated clips aimed at resolving the video long-to-short task. Recognizing the inherent constraints posed by untrained human annotators, which can result in inaccurate annotations for repurposed videos, we propose a two-stage solution to obtain annotations from real-world user-generated content. Furthermore, we offer a baseline model to address this challenging task by integrating audio, visual, and caption aspects through a cross-modal fusion and alignment framework. We aspire for our work to ignite groundbreaking research in the lesser-explored realms of video repurposing.

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NeurIPS Conference 2025 Conference Paper

VisualLens: Personalization through Task-Agnostic Visual History

Wang Bill Zhu
Deqing Fu
Kai Sun
Yi Lu
Zhaojiang Lin
Seungwhan Moon
Kanika Narang
Mustafa Canim

Existing recommendation systems either rely on user interaction logs, such as online shopping history for shopping recommendations, or focus on text signals. However, item-based histories are not always accessible and generalizable for multimodal recommendation. We hypothesize that a user's visual history --- comprising images from daily life --- can offer rich, task-agnostic insights into their interests and preferences, and thus be leveraged for effective personalization. To this end, we propose VisualLens, a novel framework that leverages multimodal large language models (MLLMs) to enable personalization using task-agnostic visual history. VisualLens extracts, filters, and refines a spectrum user profile from the visual history to support personalized recommendation. We created two new benchmarks, Google-Review-V and Yelp-V, with task-agnostic visual histories, and show that VisualLens improves over state-of-the-art item-based multimodal recommendations by 5-10\% on Hit@3, and outperforms GPT-4o by 2-5\%. Further analysis shows that VisualLens is robust across varying history lengths and excels at adapting to both longer histories and unseen content categories.

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NeurIPS Conference 2024 Conference Paper

APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments

Xin Jin
Qianqian Qiao
Yi Lu
Huaye Wang
Heng Huang
Shan Gao
Jianfei Liu
Rui Li

Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Existing painting datasets are often characterized by limited scoring dimensions and insufficient annotations, thereby constraining the advancement and application of automatic aesthetic evaluation methods in the domain of painting. To bridge this gap, we introduce the Aesthetics Paintings and Drawings Dataset (APDD), the first comprehensive collection of paintings encompassing 24 distinct artistic categories and 10 aesthetic attributes. Building upon the initial release of APDDv1, our ongoing research has identified opportunities for enhancement in data scale and annotation precision. Consequently, APDDv2 boasts an expanded image corpus and improved annotation quality, featuring detailed language comments to better cater to the needs of both researchers and practitioners seeking high-quality painting datasets. Furthermore, we present an updated version of the Art Assessment Network for Specific Painting Styles, denoted as ArtCLIP. Experimental validation demonstrates the superior performance of this revised model in the realm of aesthetic evaluation, surpassing its predecessor in accuracy and efficacy. The dataset and model are available at https: //github. com/BestiVictory/APDDv2. git.

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IJCAI Conference 2024 Conference Paper

Paintings and Drawings Aesthetics Assessment with Rich Attributes for Various Artistic Categories

Xin Jin
Qianqian Qiao
Yi Lu
Huaye Wang
Shan Gao
Heng Huang
Guangdong Li

Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. Our research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application. Dataset is available at https: //github. com/BestiVictory/APDD. git

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JBHI Journal 2021 Journal Article

Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition

Lung-Hao Lee
Yi Lu

Named Entity Recognition (NER) is a natural language processing task for recognizing named entities in a given sentence. Chinese NER is difficult due to the lack of delimited spaces and conventional features for determining named entity boundaries and categories. This study proposes the ME-MGNN (Multiple Embeddings enhanced Multi-Graph Neural Networks) model for Chinese NER in the healthcare domain. We integrate multiple embeddings at different granularities from the radical, character to word levels for an extended character representation, and this is fed into multiple gated graph sequence neural networks to identify named entities and classify their types. The experimental datasets were collected from health-related news, digital health magazines and medical question/answer forums. Manual annotation was conducted for a total of 68, 460 named entities across 10 entity types (body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment and time) in 30, 692 sentences. Experimental results indicated our ME-MGNN model achieved an F1-score result of 75. 69, outperforming previous methods. In practice, a series of model analysis implied that our method is effective and efficient for Chinese healthcare NER.

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YNICL Journal 2020 Journal Article

Age-related atrophy of cortical thickness and genetic effect of ANK3 gene in first episode MDD patients

Yuqi Cheng
Jian Xu
Chenglong Dong
Zonglin Shen
Cong Zhou
Na Li
Yi Lu
Liuyi Ran

Brain ageing is thought to be related to geriatric depression, but the relationship between ageing and depression among middle aged individuals is unknown. The present study aimed to evaluate whether the age-related reduction of brain cortical thickness (CT) can be found in adult first-episode MDD patients, as well as to identify the possible genetic effect of the ANK3 gene polymorphism age-relates CT reduction. This study recruited 153 first-episode MDD patients with a disease duration < 2 years and 276 healthy controls (HC), and the CT of 68 whole brain regions and two ANK3 SNPs (rs1994336 and rs10994359) were analyzed. The results showed that although the CT of both groups was negative correlated with age, the MDD group had significant greater age-related decrease in CT than the HC group (–9. 35 × 10−3 mm/year for MDD vs. –1. 23 × 10−3 mm/year for HC in the left lateral orbitofrontal lobe). The multivariate analysis of covariance (MANCOVA) results yielded significant interactions of diagnosis × age, genotype × age and diagnosis × genotype interaction for rs10994359. In HC, the C allele showed a protective effect on age-related CT reduction. The reduction in CT with age was several times as greater in non-C carriers as in C carriers (–3. 54 × 10−3 vs. –0. 15 × 10−3 mm/year in left supramarginal gyrus) for HC. However, this protective effect disappeared in patients with MDD. We did not find a clear effect of rs1994336 on the age-related CT reduction. The findings indicate that the widespread accelerated brain ageing occurs early in adult-onset depression and this ageing may be a pathological mechanisms of depression rather than an outcome of the disease. The ANK3 rs10994359 polymorphism may partially affect regional cortical ageing in MDD.

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IROS Conference 2017 Conference Paper

Preliminary study on magnetic tracking based navigation for wire-driven flexible robot

Changchun Zhang
Yi Lu
Xiaoxiao Qiu
Shuang Song 0002
Li Liu 0017
Max Q. -H. Meng

Flexible manipulator enables curvilinear accessibility through small incisions or natural orifices for minimally invasive surgery and diagnosis, which makes it a good choice for minimally invasive surgery. In order to control the robot precisely and safely, the real-time position and shape information of the robot need to be measured well. In this paper, we propose a magnetic tracking based tip pose and shape detection method for wire driven flexible robots. A permanent magnet is mounted at the distal end of the robot. Its magnetic field can be sensed with a sensor array. Therefore, position and orientation of the tip can be estimated utilizing the tracking method. A shape sensing algorithm is then carried out to estimate the real-time shape based on the tip pose. With the tip pose and shape display in the reconstructed visual environment, navigation can be achieved. This method provides the advantages that no sensors are needed to mount on the robot and has no line-of-sight problem. Experimental results verified the feasibility of the proposed method. A navigation error of 1. 9mm is achieved.

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YNICL Journal 2016 Journal Article

Changes of grey matter volume in first-episode drug-naive adult major depressive disorder patients with different age-onset

Zonglin Shen
Yuqi Cheng
Shuran Yang
Nan Dai
Jing Ye
Xiaoyan Liu
Jin Lu
Na Li

OBJECTIVE: Little is known about the pathological mechanism of early adult onset depression (EOD) and later adult onset depression (LOD). We seek to determine whether grey matter volume (GMV) change in EOD and LOD are different, which could also delineate EOD and LOD. METHODS: In present study, 147 first-episode, drug-naive patients with major depressive disorder (MDD), age between 18 and 45, were divided into two groups on the basis of age of MDD onset: the early adult onset group (age 18-29) and the later adult onset group (age 30-44), and a total of 130 gender-, and age-, matched healthy controls (HC) were also divided into two groups which fit for each patient group. Magnetic resonance imaging was conducted on all subjects. The voxel-based morphometry (VBM) approach was employed to analyze the images. RESULTS: Widespread abnormalities of GMV throughout parietal, temporal, limbic regions, occipital cortex and cerebellum were observed in MDD patients. Compare to young HC, reduced GMV in right fusiform gyrus, right middle temporal gyrus, vermis III and increased GMV in right middle occipital gyrus were seen in the EOD group. In contrast, relative to old HC, decreased GMV in the right hippocampus and increased GMV in the left middle temporal gyrus were observed in the LOD group. Compared to the LOD group, the EOD group had smaller GMV in right posterior cingulate cortex. There was no significant correlation between GMV of the right posterior cingulate cortex and the score of the depression rating scale in patients group. CONCLUSIONS: The GMV of the brain areas that were related to mood regulation was decreased in the first-episode, drug-naive adult patients with MDD. Adult patients with EOD and LOD exhibited different GMV changes relative to each age-matched comparison group, suggesting depressed adult patients with different age-onset might have different pathological mechanism.

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YNICL Journal 2016 Journal Article

The volumetric and shape changes of the putamen and thalamus in first episode, untreated major depressive disorder

Yi Lu
Hongmin Liang
Dan Han
Yin Mo
Zongfang Li
Yuqi Cheng
Xiufeng Xu
Zonglin Shen

Previous MRI studies confirmed abnormalities in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) network or limbic-cortico-striatal-thalamic-cortical (LCSTC) circuits in patients with major depressive disorder (MDD), but few studies have investigated the subcortical structural abnormalities. Therefore, we sought to determine whether focal subcortical grey matter (GM) changes might be present in MDD at an early stage. We recruited 30 first episode, untreated patients with major depressive disorder (MDD) and 26 healthy control subjects. Voxel-based morphometry was used to evaluate cortical grey matter changes, and automated volumetric and shape analyses were used to assess volume and shape changes of the subcortical GM structures, respectively. In addition, probabilistic tractography methods were used to demonstrate the relationship between the subcortical and the cortical GM. Compared to healthy controls, MDD patients had significant volume reductions in the bilateral putamen and left thalamus (FWE-corrected, p < 0.05). Meanwhile, the vertex-based shape analysis showed regionally contracted areas on the dorsolateral and ventromedial aspects of the bilateral putamen, and on the dorsal and ventral aspects of left thalamus in MDD patients (FWE-corrected, p < 0.05). Additionally, a negative correlation was found between local atrophy in the dorsal aspects of the left thalamus and clinical variables representing severity. Furthermore, probabilistic tractography demonstrated that the area of shape deformation of the bilateral putamen and left thalamus have connections with the frontal and temporal lobes, which were found to be related to major depression. Our results suggested that structural abnormalities in the putamen and thalamus might be present in the early stages of MDD, which support the role of subcortical structure in the pathophysiology of MDD. Meanwhile, the present study showed that these subcortical structural abnormalities might be the potential trait markers of MDD.

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JBHI Journal 2014 Journal Article

The Sensitive and Efficient Detection of Quadriceps Muscle Thickness Changes in Cross-Sectional Plane Using Ultrasonography: A Feasibility Investigation

Jizhou Li
Yongjin Zhou
Yi Lu
Guangquan Zhou
Lei Wang
Yong-Ping Zheng

As a direct determinant parameter to quantify muscle activity, the muscle thickness (MT) has been investigated in many aspects and for various purposes. Ultrasonography (US) is a promising modality to detect muscle morphological changes during contractions since it is portable, noninvasive, and real time. However, there are few reports on sensitive and efficient estimation of changes of MT in a cross-sectional plane. In this feasibility investigation, we proposed a coarse-to-fine method based on a compressive-tracking algorithm for estimation of MT changes during an example task of isometric knee extension using ultrasound images. The sensitivity and efficiency are evaluated with 1920 US images from quadriceps muscle (QM) in eight subjects. The detection results were compared with those obtained from both traditional manual measurement and the well known normalized cross-correlation method, and the effect of the size of tracking window on detection performance was evaluated as well. It is demonstrated that the proposed method agrees well with the manual measurement. Meanwhile, it is not only sensitive to relatively small changes of MT but also computationally efficient.

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IJCAI Conference 2013 Conference Paper

Fault-Tolerant Planning under Uncertainty

Luis Pineda
Yi Lu
Shlomo Zilberstein
Claudia V. Goldman

A fault represents some erroneous operation of a system that could result from an action selection error or some abnormal condition. We formally define error models that characterize the likelihood of various faults and consider the problem of faulttolerant planning, which optimizes performance given an error model. We show that factoring the possibility of errors significantly degrades the performance of stochastic planning algorithms such as LAO*, because the number of reachable states grows dramatically. We introduce an approach to plan for a bounded number of faults and analyze its theoretical properties. When combined with a continual planning paradigm, the k-fault-tolerant planning method can produce near-optimal performance, even when the number of faults exceeds the bound. Empirical results in two challenging domains confirm the effectiveness of the approach in handling different types of runtime errors.

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