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Fine-grained action evaluation in medical vision faces unique challenges due to the unavailability of comprehensive datasets, stringent precision requirements, and insufficient spatiotemporal dynamic modeling of very rapid actions. 
To support development and evaluation, we introduce CPREval-6k, a multi-view, multi-label medical action benchmark containing 6,372 expert-annotated videos with 22 clinical labels.
Using this dataset, we present GaussMedAct, a multivariate Gaussian encoding framework, to advance medical motion analysis through adaptive spatiotemporal representation learning. Multivariate Gaussian Representation projects the joint motions to a temporally scaled multi-dimensional space, and decomposes actions into adaptive 3D Gaussians that serve as tokens. These tokens preserve motion semantics through anisotropic covariance modeling while maintaining robustness to spatiotemporal noise. Hybrid Spatial Encoding, employing a Cartesian and Vector dual-stream strategy, effectively utilizes skeletal information in the form of joint and bone features.
The proposed method achieves 92.1\% Top-1 accuracy with real-time inference on the benchmark, outperforming the ST-GCN baseline by +5.9\% accuracy with only 10\% FLOPs. Cross-dataset experiments confirm the superiority of our method in robustness.

AAAI 2026

Multivariate Gaussian Representation Learning for Medical Action Evaluation

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address the limitations of traditional crime linkage methods when handling high-dimensional, sparse, and heterogeneous data, this paper proposes a Siamese Autoencoder framework to learn meaningful latent representations and uncover correlations in highly complex data. Using a dataset from the Violent Crime Linkage Analysis System—a database maintained by the Serious Crime Analysis Section of the UK’s National Crime Agency—our approach mitigates signal dilution in high-dimensional sparse data through decoder-stage integration of geographic-temporal features. This integration amplifies learned behavioral representations rather than allowing them to be overwhelmed at the input stage, leading to consistent improvements over baseline methods across multiple metrics. We further examine how different data reduction strategies based on domain-expert can impact model performance, offering practical insights into preprocessing for crime linkage. Our solution shows that advanced machine learning approaches can enhance linkage accuracy, improving AUC by up to 9% over traditional methods and providing insights to support human decision-making in crime investigation.

Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network

Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights. Researchers attempt to prevent malicious personalization using adversarial perturbations. However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations. They utilize global adversarial perturbations, which introduce noticeable alterations to original images and significantly degrade visual quality. In this work, we propose the Visual-Friendly Concept Protection (VCPro) framework, which prioritizes the protection of key concepts chosen by the image owner through adversarial perturbations with lower perceptibility. To ensure these perturbations are as inconspicuous as possible, we introduce a relaxed optimization objective to identify the least perceptible yet effective adversarial perturbations, solved using the Lagrangian multiplier method. Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.

Visual-Friendly Concept Protection via Selective Adversarial Perturbations

LLMs have transformed NLP, yet deploying them on edge devices poses great carbon challenges. Prior estimators remain incomplete, neglecting peripheral energy use, distinct prefill/decode behaviors, and SoC design complexity. This paper presents \textit{CO2-Meter}, a unified framework for estimating operational and embodied carbon in LLM edge inference. Contributions include: (1) equation-based peripheral energy models and datasets; (2) a GNN-based predictor with phase-specific LLM energy data; (3) a unit-level embodied carbon model for SoC bottleneck analysis; and (4) validation showing superior accuracy over prior methods. Case studies show \textit{CO2-Meter}'s effectiveness in identifying carbon hotspots and guiding sustainable LLM design on edge platforms. Source code: \url{https://github.com/fuzhenxiao/CO2-Meter}.

CO2-Meter: A Comprehensive Carbon Footprint Estimator for LLMs on Edge Devices

Lane change prediction, encompassing both intention recognition and trajectory forecasting, is essential for the safe operation of autonomous vehicles in mixed-traffic environments. Existing models predominantly follow a data-driven paradigm, learning directly from historical vehicle states through an end-to-end approach. Inspired by the emerging paradigm of enhancing model generalizability through domain knowledge, we propose KnowLCP to explicitly model and integrate driving knowledge into the lane change prediction task. Specifically, we incorporate three types of knowledge: traffic risk awareness to improve intention prediction, vehicle kinematics to ensure the physical feasibility of predicted trajectories, and intention intensity to refine trajectory forecasting. Furthermore, we introduce a novel knowledge injection strategy that enhances mutual information during integration and proves superior to the traditional parallel input mechanism, which simply feeds knowledge features alongside historical states. Extensive experiments on two real-world trajectory datasets demonstrate that KnowLCP achieves average improvements of 8.3-10.3% in intention prediction and 10.1-10.3% in trajectory prediction over the best-performing baselines.

KnowLCP: Knowledge Augmented Lane Change Prediction for Autonomous Driving

Illegal related-party transactions (RPT) are federal felonies that pose a severe threat to the stability and integrity of modern financial systems. The increasing frequency of RPTs forms complex and dynamic networks. Existing temporal graph learning methods tend to treat entities as functionally homogeneous, ignoring the diverse and evolving structural roles of nodes. Role-based embedding methods model global structure by bridging same-role nodes, but their reliance on a unified mechanism for aggregation and evolution means they fail to distinguish the underlying logic of distinct interactions governed by structural roles. The limitations motivate us to develop a customized role-based strategy. It can also adapt to evolving RPT dynamics, thereby forming a continuous regulatory process to combat illegal activities. In this paper, we propose an innovative Role Perceptual Augmented Temporal Graph Network (RPATGN) for proactive RPT detection. We analyze the structural roles of nodes and employ a role-based message passing mechanism that adapts its aggregation strategy based on the roles of interacting nodes. We integrate a variational graph recurrent neural network, enhanced by temporal contextual attention, to explicitly model the dynamics of the roles and the overall network evolution. Extensive experiments on real-world financial datasets demonstrate the effectiveness of our approach for RPT detection. It holds practical significance for fostering robust financial systems and promoting healthy, transparent financial markets.

Role Perceptual Augmented Temporal Graph Network for Related-party Transaction Detection

The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address this limitation, we establish the FemaleSaanenGoat dataset containing synchronized eight-view RGBD videos of 55 female Saanen goats (6-18 months). Using multi-view DynamicFusion, we fuse noisy, non-rigid point cloud sequences into high-fidelity 3D scans, overcoming challenges from irregular surfaces and rapid movement. Based on these scans, we develop SaanenGoat, a parametric 3D shape model specifically designed for female Saanen goats. This model features a refined template with 41 skeletal joints and enhanced udder representation, registered with our scan data. A comprehensive shape space constructed from 48 goats enables precise representation of diverse individual variations. With the help of SaanenGoat model, we get high-precision 3D reconstruction from single-view RGBD input, and achieve automated measurement of six critical body dimensions: body length, height, chest width, chest girth, hip width, and hip height. Experimental results demonstrate the superior accuracy of our method in both 3D reconstruction and body measurement, presenting a novel paradigm for large-scale 3D vision applications in precision livestock farming.All code, data, and supplementary materials are publicly available at https://github.com/bojin-nwafu/Female-Saanen-Goats.

Monocular Mesh Recovery and Body Measurement of Female Saanen Goats

With the rapid rise of the gig economy, crowdsourced delivery platforms (e.g., Uber Eats, DoorDash, Deliveroo) have become a key component of urban logistics. However, their competitive order-grabbing mechanisms incentivize riders to pursue individual reward maximization, leading to strategic homogenization, severe efficiency degradation, and growing income inequality. This paper proposes \textbf{ARDE}, a novel dual-layer reinforcement learning framework that aligns individual rationality with collective welfare. The outer agent adaptively generates governance signals based on system-level indicators (strategy entropy, Gini coefficient, completion rate), while the inner agents optimize tactical decisions via Diffusion Q-Learning to encourage cooperative behavior and avoid premature convergence. A language model-driven reward shaping module dynamically incorporates fairness and diversity considerations, providing interpretable and adaptive policy guidance. This paper build a high-fidelity multi-agent simulation environment using the LaDe-D dataset to replicate realistic order allocation dynamics. In contrast, ARDE achieves a balanced and sustainable outcome, improving entropy stability ($0.997 \pm 0.184$), reducing inequality (Gini change $-1.3\%$), and maintaining high system efficiency, outperforming both rule-based and reward-shaping baselines. These results provide the first empirical evidence that algorithmic governance can overcome the “individual rationality $\rightarrow$ collective suboptimality” dilemma in crowdsourced delivery platforms. ARDE offers a scalable, low-complexity solution for intelligent governance in gig economies and other large-scale socio-technical systems.

Adaptive Regulation via Dual-Layer Evolution (ARDE): A Multi-Agent Approach to Balancing Efficiency, Fairness, and Diversity in Crowdsourced Platforms

Creating a well-structured lesson plan is essential for improving classroom efficiency, yet it is often a labor-intensive process. Recently, many studies have leveraged large language models (LLMs) to generate lesson plans automatically. However, existing methods heavily rely on LLMs that are pre-trained on large-scale universal corpora, which often lack critical educational theory and textbook-specific information. This can lead to inconsistencies and misalignment with textbook content. To address these challenges, we propose CE-LessonPlan, a novel compress-expand framework to generate lesson plans by effectively combining external lesson plan references and textbook information. The framework consists of two key components: a compressor, which synthesizes multiple retrieved references into a cohesive document, and an expander, which integrates textbook-specific information with the parametric knowledge of LLMs to produce another enriched lesson plan. The outputs of the compressor and expander are then seamlessly integrated to create a comprehensive golden context, further enhancing the lesson plan generation process with LLMs. We conduct extensive experiments to demonstrate that CE-LessonPlan outperforms existing methods for generating lesson plans. To encourage reproducible research, we make our data and code publicly available at https://anonymous.4open.science/r/CE-LessonPlan-43E9.

A Compress-Expand Framework for Automatic Lesson Plan Generation

Early warning systems for disease outbreaks play a crucial role in public health for management and contingency planning. However, most predictive modeling works focus on flat models that incorporate exogenous inputs (e.g. climate, demographics) to predict future outbreaks at different locations, but do not jointly model multiple spatial aggregation levels. 
In this paper, we introduce \emph{HierarNet}, a unique \emph{independent-interactive hierarchical} forecasting framework that aims to predict disease outbreaks at different levels of spatial resolution, such as provinces, regions, and nations. HierarNet consists of two main phases. In the \emph{local} phase, we train \emph{independent} forecasting models for all locations at all levels. In the \emph{global} phase, all models \emph{iteratively interact} with others across different levels via their hierarchical relationships under an \emph{ensemble} fashion to maximize their agreements. This \emph{global local hierarchical interactive} scheme makes HierarNet a highly effective and \emph{flexible} method (i.e. it can work with an arbitrary base prediction model and available exogenous data for each location independently). Extensive experiments are conducted on various disease datasets (e.g., Dengue fever, flu, diarrhea, and Bluetongue) in different countries (e.g., France, Vietnam, and USA) to show the performance of HierarNet compared to 19 state-of-the-art (SOTA) methods such as MinT, DYCHEM, WITRAN, SegRNN, TSMixer, PatchTST, or iTransformer. We also illustrate the \emph{generability} of HierarNet in other domains, e.g., web traffic forecasting.

HierarNet: Independent Interactive Hierarchical Disease Outbreak Forecasting

Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment. Furthermore, conventional Neural Network-based risk estimators typically generate point estimates without conveying model uncertainty, limiting their utility in critical decision-making. To address these shortcomings, we introduce a novel geospatial deep learning framework that leverages satellite imagery as a comprehensive spatial input. This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks. Rather than producing a single deterministic output, our model estimates a full Beta probability distribution over fatal crash risk, yielding accurate and uncertainty-aware predictions--a critical feature for trustworthy AI in safety-critical applications. Our model outperforms baselines by achieving a 17-23% improvement in recall, a key metric for flagging potential dangers, while delivering superior calibration. By providing reliable and interpretable risk assessments from satellite imagery alone, our method enables safer autonomous navigation and offers a highly scalable tool for urban planners and policymakers to enhance roadway safety equitably and cost-effectively.

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Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network

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