Author name cluster

Dan Luo

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9 papers

2 author rows

AAAI Conference 2026 System Paper

PAGER: Proactive Monitoring Agent for Enterprise AI Assistant

Sujan Dutta
Junior Francisco Garcia Ayala
Pranav Pujar
Sai Sree Harsha
Dan Luo
Nikhil Vasudeva
Bikas Saha
Pritom Baruah

We present a Proactive Monitoring Agent designed for large-scale customer data platforms, such as Adobe Experience Platform (AEP), to predict and prevent workflow disruptions before they impact business operations. Unlike existing reactive solutions that assist engineers only after failures occur, our agent anticipates potential failures across multiple workflow stages, explains its predictions in natural language, and interacts with customer support engineers through a conversational interface. The system integrates a machine learning-based Prediction Module, Knowledge Graph APIs for contextual data access, and a Query Processor that powers an interactive Q&A experience, enabling timely and actionable insights to minimize operational risks and maximize business continuity.

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AAAI Conference 2026 Conference Paper

SAFE: Semantic- and Frequency-Enhanced Curriculum for Cross-Domain Deepfake Detection

Yulin Yao
Kangfeng Zheng
Bin Wu
Chunhua Wu
Jujie Wang
Jiaqi Gao
Minjiao Yang
Dan Luo

Driven by advances in GANs and diffusion models, deepfake content has reached an unprecedented level of photorealism, causing detectors to deteriorate once they leave their training domain. Most prior studies adopt CLIP as the backbone of an image-level binary classifier, yet overlook CLIP’s core strength: text-to-image semantic alignment. Moreover, captions generated by CLIP-CAP lack sufficient high-level semantics to distinguish between authentic and manipulated faces. Deepfake generators often fail to maintain semantic coherence, resulting in contradictions that traditional visual models cannot capture. Existing approaches also intermingle all samples during training and thus lack a systematic, difficulty-aware curriculum. To bridge these gaps, we introduce Semantic- and Frequency-Enhanced (SAFE) deepfake detection, a two-component framework: 1) Semantic-enhanced multimodal alignment. Authenticity cues are injected into CLIP-CAP captions, and low-rank LoRA fine-tuning is applied to CLIP’s visual branch, yielding dual supervision for text–image alignment and forgery discrimination. 2) Dual-score curriculum learning. Fourier Correlation Variance (FCV) measures local spectral consistency and, combined with the loss value, is transformed into a difficulty score that ranks training samples from easy to hard, reducing training time by 23.3% and enhancing generalization. SAFE attains state-of-the-art performance on several cross-dataset and cross-manipulation benchmarks. Ablation studies confirm that semantic enhancement, LoRA fine-tuning, and dual-score curriculum are complementary, jointly delivering substantial gains in open-set generalization.

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IJCAI Conference 2025 Conference Paper

Flow Matching Based Sequential Recommender Model

Feng Liu
Lixin Zou
Xiangyu Zhao
Min Tang
Liming Dong
Dan Luo
Xiangyang Luo
Chenliang Li

Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model that employs a straight flow trajectory and a modified loss tailored for the recommendation task. Additionally, from the diffusion-model perspective, we integrate a reconstruction loss to improve robustness against noise perturbations, thereby retaining user preferences during the forward process. In the reverse process, we employ a deterministic reverse sampler, specifically an ODE-based updating function, to eliminate unnecessary randomness, thereby ensuring that the generated recommendations closely align with user needs. Extensive evaluations on four benchmark datasets reveal that FMRec achieves an average improvement of 6. 53% over state-of-the-art methods. The replication code is available at https: //github. com/FengLiu-1/FMRec.

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NeurIPS Conference 2025 Conference Paper

How Does Topology Bias Distort Message Passing in Graph Recommender? A Dirichlet Energy Perspective

Yanbiao Ji
Yue Ding
Dan Luo
Chang Liu
Yuxiang Lu
Xin Xin
Hongtao Lu

Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts the interaction graph’s structure—referred to as topology bias. This leads to overrepresentation of popular items, thereby reinforcing biases and fairness issues through the user-system feedback loop. Despite attempts to study this effect, most prior work focuses on the embedding or gradient level bias, overlooking how topology bias fundamentally distorts the message passing process itself. We bridge this gap by providing an empirical and theoretical analysis from a Dirichlet energy perspective, revealing that graph message passing inherently amplifies topology bias and consistently benefits highly connected nodes. To address these limitations, we propose Test-time Simplicial Propagation (TSP), which extends message passing to higher-order simplicial complexes. By incorporating richer structures beyond pairwise connections, TSP mitigates harmful topology bias and substantially improves the representation and recommendation of long-tail items during inference. Extensive experiments across five real-world datasets demonstrate the superiority of our approach in mitigating topology bias and enhancing recommendation quality. The implementation code is available at https: //github. com/sotaagi/TSP.

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IROS Conference 2025 Conference Paper

LOG-SLAM: Large-Scale Outdoor Gaussian SLAM for Dense Mapping and Loop Closure in Kilometer-Scale Scene Reconstruction

Long Wang
Haosong Liu
Haiyong Luo
Fang Zhao 0003
Runze Chen
Yushi Chen 0004
Jiaquan Yan
Dan Luo

The success of 3D Gaussian splatting in 3D reconstruction has recently led to efforts to integrate it with SLAM systems. However, most existing research has focused on indoor tracking and mapping, while outdoor Gaussian SLAM methods still heavily rely expensive LiDAR sensor. To address these challenges, we propose LOG-SLAM, a novel method for large-scale outdoor tracking and mapping using Gaussian Splatting. Our approach supports tracking through monocular or visual-inertial input, progressively constructing the 3D Gaussian map from depth and pose estimates obtained during the tracking process. Additionally, we introduce a submap-based strategy for managing large-scale maps, enabling the reconstruction of kilometer-scale environments. A loop closure detection module is also incorporated to reduce accumulated errors. Furthermore, we present a novel dynamic object removal method based on rendering loss that mitigates the interference of dynamic objects on the reconstruction. Our experiments on KITTI and KITTI-360 demonstrate that our method achieves localization performance comparable to traditional SLAM systems, while outperforming recent GS/NeRF-based SLAM approaches in terms of mapping and rendering quality.

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YNIMG Journal 2025 Journal Article

Neurocognitive mechanisms of age-related decline in global motion perception

Yaxi Hong
Ting Liu
Dan Luo
Ziliang Zhu
Shizhen Yan
Hua Jin

Age-related declines in global motion perception (GMP) may result from alterations in visual noise in combination with morphological changes in the visual cortices. However, the neurocognitive mechanisms that link cortical structural alterations to deficits in noise modulation remain unclear. In this study, we integrated psychophysical method, the perceptual template model (PTM), and structural magnetic resonance imaging to investigate the relationships among brain structure, cognitive processes, and perceptual performance underlying GMP aging. We compared motion coherence thresholds (MCT) of 106 younger and 94 older healthy adults using random-dot kinematograms. The PTM characterized age-related changes in internal additive noise and external noise, while voxel- and surface-based morphometry assessed gray matter volume, cortical thickness, and surface area in visual regions. Mediation models examined how changes in noise mediate the relationship between cortical structure and perceptual performance. PTM analysis revealed that reduced GMP in older adults was significantly associated with increased internal additive noise and external noise. Morphometric analyses indicated that GMP decline was associated with reductions in gray matter volume in right V4v, as well as cortical thinning in left V5 and right V8. Mediation analysis further demonstrated that internal additive noise fully mediated the relationship between cortical thickness in left V5 and MCT, whereas external noise partially mediated the relationships between right V4v gray matter volume and MCT, and between right V8 cortical thickness and MCT. These findings suggest that age-related cortical thickness reduction in left V5 amplifies internal noise, while cortical atrophy in right V4v and V8 impairs the extraction of motion signals from external noise. Overall, this study proposes a novel framework for understanding age-related GMP decline by linking cortical morphology, noise alterations, and perceptual performance.

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IJCAI Conference 2024 Conference Paper

FasterVD: On Acceleration of Video Diffusion Models

Pinrui Yu
Dan Luo
Timothy Rupprecht
Lei Lu
Zhenglun Kong
Pu Zhao
Yanyu Li
Octavia Camps

Equipped with Denoising Diffusion Probabilistic Models, video content generation has gained significant research interest recently. However, diffusion pipelines call for intensive computation and model storage, which poses challenges for their wide and efficient deployment. In this work, we address this issue by integrating LCM-LoRA to reduce the denoising steps and escalating the video generation process by frame skipping and interpolation. Our framework achieves an approximately 10× inference acceleration for high-quality realistic video generation on commonly available GPUs.

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IS Journal 2014 Journal Article

Behavior Informatics: A New Perspective

Longbing Cao
Thorsten Joachims
Can Wang
Eric Gaussier
Jinjiu Li
Yuming Ou
Dan Luo
Reza Zafarani

This installment of Trends & Controversies provides an array of perspectives on the latest research in behavior informatics. Longbing Cao introduces the work in "Behavior Informatics: A New Perspective. " Then, in "Behavior Computing, " Longbing Cao and Thorsten Joachims provide a basic overview of the topic. Next is "Coupled Behavior Representation, Modeling, Analysis, and Reasoning" by Can Wang, Longbing Cao, Eric Gaussier, Jinjiu Li, Yuming Ou, and Dan Luo. The fourth article is "Behavior Analysis in Social Media, " by Reza Zafarani and Huan Liu. The fifth article is "Group Recommendation and Behavior, " by Guandong Xu and Zhiang Wu. Gabriella Pasi wrote the sixth article, "Web Search and Behavior. " The seventh article, "Behaviors of IPTV Users, " is by Ya Zhang, Xiaokang Yang, and Hongyuan Zha. Finally, "Should Behavioral Models of Terror Groups Be Disclosed? " is by Edoardo Serra and V. S. Subrahmanian.

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TCS Journal 2012 Journal Article

The use of tail inequalities on the probable computational time of randomized search heuristics

Dong Zhou
Dan Luo
Ruqian Lu
Zhangang Han

For the purpose of analyzing the time cost of evolutionary algorithms (EAs) or other types of randomized search heuristics (RSHs) with certain requirements on the probability of obtaining a target solution, this paper proposes a new index, called the probable computational time (PCT), which complements expected running time analysis. Using simple tail inequalities, such as Markov’s inequality and Chebyshev’s inequality, we also provide basic properties of PCT, explicitly exhibiting the general relationships between the expected running time and the PCT. To present deeper estimations of the PCT for specific RSHs and problems, we demonstrate a new inequality that is based on the general form of the Chernoff inequality and previous methods such as “fitness-based partitions” and “potential functions”, which have been used to analyze the expected running time of RSHs. The precondition of the new inequality is that the total running time can be described as the sum of a linear combination of some independent geometrically distributed variables and a constant term. The new inequality always provides meaningful upper bounds for the PCT under such circumstances. Some applications of the new inequality for simple EAs, ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms on simple pseudo-Boolean functions are illustrated in this paper.

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