GenAI4ELab/papercli-papers huggingface dataset:
PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery
Authors: Jiayue Yuan; Fangting Xie; Guangwen Ouyang; Changhai Ma; Ziyu Wu; Heyu Ding; Quan Wan; Yi Ke; Yuchen Wu; Xiaohui Cai
Venue: AAAI 2026 (main)

Abstract: Multi-person global human mesh recovery (HMR) is crucial for understanding crowd dynamics and interactions. Traditional vision-based HMR methods sometimes face limitations in real-world scenarios due to mutual occlusions, insufficient lighting, and privacy concerns. Human-floor tactile interactions offer an occlusion-free and privacy-friendly alternative for capturing human motion. Existing research indicates that pressure signals acquired from tactile mats can effectively estimate human pose in single-person scenarios. However, when multiple individuals walk randomly on the mat simultaneously, how to distinguish intermingled pressure signals generated by different persons and subsequently acquire individual temporal pressure data remains a pending challenge for extending pressure-based HMR to the multi-person situation. In this paper, we present PressTrack-HMR, a top-down pipeline that recovers multi-person global human meshes solely from pressure signals. This pipeline leverages a tracking-by-detection strategy to first identify and segment each individual's pressure signal from the raw pressure data, and subsequently performs HMR for each extracted individual signal. Furthermore, we build a multi-person interaction pressure dataset MIP, which facilitates further research into pressure-based human motion analysis in multi-person scenarios. Experimental results demonstrate that our method excels in multi-person HMR using pressure data, with 89.2 MPJPE and 112.6  WA-MPJPE, and these showcase the potential of tactile mats for ubiquitous, privacy-preserving multi-person action recognition.
SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World
Authors: Jiaqi Zhang; Chen Gao; Liyuan Zhang; Quoc Viet Hung Nguyen; Hongzhi Yin
Venue: AAAI 2026 (main)

Abstract: Recent advances in embodied agents with multimodal perception and reasoning capabilities based on large vision-language models (LVLMs), excel in autonomously interacting either real or cyber worlds, helping people make intelligent decisions in complex environments. However, the current works are normally optimized by golden action trajectories or ideal task-oriented solutions toward a definitive goal. This paradigm considers limited user-oriented factors, which could be the reason for their performance reduction in a wide range of personal assistant applications. To address this, we propose Chain-of-User-Thought (COUT, a novel embodied reasoning paradigm that takes a chain of thought from basic action thinking to explicit and implicit personalized preference thought to incorporate personalized factors into autonomous agent learning. The main challenges of achieving COUT include: 1) the definition of embodied personalized tasks, 2) the embodied environment epitomizes personalized preference, and 3) the way to model embodied personalized actions. To target COUT, we introduce SmartAgent, an agent framework perceiving cyber environments and reasoning personalized requirements as: 1) interacting with GUI to access an item pool, 2) generating users' explicit requirements implied by previous actions, and 3) recommending items to fulfill users' implicit requirements. To demonstrate SmartAgent's capabilities, we also create a brand-new dataset SmartSpot that offers a full-stage personalized action-involved environment. To our best knowledge, our work is the first to formulate the COUT process, serving as a preliminary attempt towards embodied personalized agent learning. Our extensive experiments on SmartSpot illuminate SmartAgent’s functionality among a series of embodied and personalized sub-tasks.
SEBSFormer: A Spectral-Enhanced Bi-Stream Transformer for Robust EEG Decoding
Authors: Lin Zhang; Shikui Tu; Lei Xu
Venue: AAAI 2026 (main)

Abstract: Electroencephalography (EEG) plays a vital role in clinical and cognitive applications such as epilepsy diagnosis and emotion recognition. However, the low signal-to-noise ratio, inter-subject variability, and inherent non-stationarity of EEG signals present substantial modeling challenges. While recent Transformer-based models offer promising long-range modeling capabilities, their self-attention mechanism behaves as a low-pass filter, suppressing high-frequency neural patterns critical for decoding transient events. In this work, we provide the first formal analysis demonstrating this low-pass behavior in self-attention mechanisms when applied to EEG signals, revealing a fundamental limitation of deep attention-based EEG models. To address this, we propose SEBSFormer, a spectral-enhanced bi-Stream Transformer that jointly models temporal dependencies and spectral structures. SEBSFormer integrates three key modules: a spectral compensation module that restores high-frequency components via residual correction in the Fourier domain; a multi-scale temporal attention module for saliency-guided temporal compression; and a graph-guided dynamic fusion module for adaptive spatial aggregation across electrodes. Extensive experiments on three benchmark datasets—TUAB, TUEV, and SEED—demonstrate that SEBSFormer consistently outperforms existing state-of-the-art models across both clinical and affective tasks. Our findings establish a new paradigm for frequency-aware EEG modeling.
GATCL: An Adaptive Contrastive Learning Framework Based on MHGAT for Spatial Domain Identification in Spatial Transcriptomics
Authors: Shilin Zhang; Weiliang Huo; Qingchen Zhang; Xiulong Liu
Venue: AAAI 2026 (main)

Abstract: Recent advances in spatial transcriptomics have enabled the simultaneous measurement of gene expression profiles and spatial location information, offering a more comprehensive and in-depth view for studying the tissue microenvironment. Spatial domain identification is a crucial step in analyzing spatial transcriptomics. However, current methods have poor accuracy and visualization because they lack self-adaptability to different tissue data, and moreover, they cannot effectively extract spatial location information. To address these issues, we propose an adaptive graph contrastive learning framework based on multi-head graph attention networks (GATCL) for spatial domain identification. Specifically, we design a data augmentation module to mask and shuffle the pre-processed gene expression data to generate more differentiated negative samples. In addition, we construct the multi-head graph attention networks (MHGAT) to encode gene expression profiles and spatial location information. More importantly, we design an adaptive graph contrastive learning model that works both with positive and negative samples from spatial transcriptomics. We introduce the attention pooling mechanism to dynamically and adaptively aggregate the spots' neighborhood information, and to improve the model's generalization ability for different spatial transcriptomics data. Furthermore, we design a discriminator that adds spectral normalization to bilinear functions. Experimental results on DLPFC, breast cancer, and mouse somatosensory cortex datasets demonstrate that the average Adjusted Rand Index (ARI) scores are 0.5746, 0.6182, and 0.6496, respectively, significantly outperforming baseline methods. More importantly, GATCL provides a more detailed visualization of different spatial transcriptomics data.
SAMGTD: Spatial-Aware Masked Graph Transformer-Diffusion Model for Enhanced Cell Type Deconvolution in Spatial Transcriptomics
Authors: Shilin Zhang; Suixue Wang; Qingchen Zhang; Xiulong Liu
Venue: AAAI 2026 (main)

Abstract: Recent advances in spatial transcriptomics have enabled the integration of gene expression profiles with precise spatial coordinates, which have facilitated the exploration of tumor occurrence and development mechanisms, as well as the development of more effective targeted and immunotherapy approaches for tumor treatment. Deciphering cell type represents a critical challenge in spatial transcriptomics research. Existing methods are limited by the pervasive “dropout” events in spatial transcriptomics, hindering their ability to fully capture the relationship between spatial location and gene expression, thereby compromising the performance of cell type deconvolution. To address these limitations, we propose a spatial-aware masked graph transformer-diffusion model (SAMGTD) for enhanced cell type deconvolution in spatial transcriptomics. For spatial transcriptomics, the masked graph transformer model is designed to adaptively capture complex dependencies between spatial locations and gene expression. It employs a masking strategy that guides the model to focus on important local information during training, while the multi-head attention mechanism captures global context. More importantly, the spatial diffusion model is constructed to achieve the dual enhancement of spatial transcriptomics, including denoising and data imputation. It incorporates the multi-head attention mechanism and residual blocks, effectively addressing the “dropout” issue commonly encountered in spatial transcriptomics. For scRNA-seq, we construct a variational autoencoder to reduce noise interference while preserving key gene expression information. Finally, we construct a spatial-aware contrastive learning model to integrate scRNA-seq and spatial transcriptomics for cell type deconvolution. Experiments conducted on three datasets demonstrate that SAMGTD outperforms baseline methods.
NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment
Authors: Wenjiang Zhang; Sifeng Wang; Yuwei Su; Xinyu Li; Chen Zhang; Suyu Zhong
Venue: AAAI 2026 (main)

Abstract: Visual neural decoding seeks to reconstruct or infer perceived visual stimuli from brain activity patterns, providing critical insights into human cognition and enabling transformative applications in brain-computer interfaces and artificial intelligence. Current approaches, however, remain constrained by the scarcity of high-quality stimulus-brain response pairs and the inherent semantic mismatch between neural representations and visual content. Inspired by perceptual variability and co-adaptive strategy of the biological systems, we propose a novel self-supervised architecture, named NeuroBridge, which integrates Cognitive Prior Augmentation (CPA) with Shared Semantic Projector (SSP) to promote effective cross-modality alignment. Specifically, CPA simulates perceptual variability by applying asymmetric, modality-specific transformations to both EEG signals and images, enhancing semantic diversity. Unlike previous approaches, SSP establishes a bidirectional alignment process through a co-adaptive strategy, which mutually aligns features from two modalities into a shared semantic space for effective cross-modal learning. NeuroBridge surpasses previous state-of-the-art methods under both intra-subject and inter-subject settings. In the intra-subject scenario, it achieves the improvements of 12.3% in top-1 accuracy and 10.2% in top-5 accuracy, reaching 63.2% and 89.9% respectively on a 200-way zero-shot retrieval task. Extensive experiments demonstrate the effectiveness, robustness, and scalability of the proposed framework for neural visual decoding.
Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance
Authors: Xun Zhang; Weihao Xia; Yulong Liu; Bo Yang; Alessandro Bozzon; Pan Wang
Venue: AAAI 2026 (main)

Abstract: Understanding the neural basis of three-dimensional (3D) perception is a fundamental objective in cognitive neuroscience. Despite advances in decoding 2D visual stimuli from neural data, reconstructing high-fidelity 3D objects with detailed texture and geometry remains largely unexplored. In this work, we introduce NeuroSculptor3D, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity. NeuroSculptor3D integrates a viewpoint-aware brain embedding module that captures fine-grained spatial variations across visual perspectives, and a hierarchical guidance mechanism that aligns brain-derived features with perceptual, semantic, and structural priors. Together, these components facilitate the generation of consistent multi-view embeddings, which are then decoded via TRELLIS to produce high-quality textured 3D reconstructions. Experiments on the fMRI-Shape dataset demonstrate that NeuroSculptor3D outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency. Code will be released to facilitate further research.
SPARD: Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction
Authors: Yiming Zhang; Baojia Han; Ximing Li; Wei Pang; Fausto Giunchiglia; Xiaoyue Feng; Renchu Guan
Venue: AAAI 2026 (main)

Abstract: The task of stochastic human motion prediction has attracted significant attention in recent years due to its wide-ranging applications in robotics, animation, and human-computer interaction. While diffusion models have demonstrated promising progress in this domain, they remain hindered by two critical limitations: (1) slow inference speeds due to their reliance on iterative sampling, and (2) performance degradation resulting from suboptimal sample allocation during generation. To overcome these challenges, we propose SPARD (Single-step Inference with Adaptive Sampling in Residual Diffusion for Human Motion Prediction), a novel framework that achieves efficient single-step inference while maintaining high predictive accuracy. Furthermore, we introduce a novel adaptive noise predictor module that dynamically samples latent representations based on observed motion sequences, ensuring both accuracy and plausibility in generated motions. Extensive experiments on benchmark datasets demonstrate that SPARD significantly outperforms state-of-the-art methods in both inference efficiency and motion quality, achieving a 15× to 18× speedup in sampling time compared to conventional diffusion-based baselines while preserving generation quality.
Coverage-Constrained Human-AI Cooperation with Multiple Experts
Authors: Zheng Zhang; Cuong C. Nguyen; Kevin Wells; Thanh-Toan Do; David Rosewarne; Gustavo Carneiro
Venue: AAAI 2026 (main)

Abstract: Human-AI cooperative classification (HAI-CC) aims to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts when AI is not confident, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, existing research in both L2D and L2C has not effectively been explored under diverse expert knowledge to improve decision-making, particularly when constrained by the operation cost of human involvement. In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. In particular, CL2DC assesses input data before making final decisions through either AI prediction alone or by deferring to or complementing a specific human expert. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.
EEG Agent: A Unified Framework for Automated EEG Analysis Using Large Language Models
Authors: Sha Zhao; Mingyi Peng; Haiteng Jiang; Tao Li; Shijian Li
Venue: AAAI 2026 (main)

Abstract: Scalable and generalizable analysis of brain activity is essential for advancing both clinical diagnostics and cognitive research. Electroencephalography (EEG), a non-invasive modality with high temporal resolution, has been widely used for brain states analysis. However, most exiting EEG models are usually tailored for single specific tasks, limiting their utility in realistic scenarios where EEG analysis often involves multi-task and continuous reasoning. In this work, we introduce EEG Agent, a general-purpose framework that leverages large language models (LLMs) to schedule and plan multiple tools to automatically complete EEG-related tasks. EEG Agent is capable of performing the key functions: EEG basic information perception, spatiotemporal EEG exploration, EEG event detection, interaction with users, and EEG report generation. To realize the capabilities, we design a toolbox composed of different tools for EEG preprocessing, feature extraction, event detection, etc. These capabilities were evaluated on public datasets, and our EEG Agent can support flexible and interpretable EEG analysis, highlighting its potential for real-world clinical applications.
TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution
Authors: Zhikai Zhao; Chuanbo Hua; Federico Berto; Kanghoon Lee; Zihan Ma; Jiachen Li; Jinkyoo Park
Venue: AAAI 2026 (main)

Abstract: Trajectory prediction is a crucial task in modeling human behavior, especially in safety-critical fields such as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, slow inference speed, lack of explainability, and generalization issues that limit their practical adoption in such environments. In this paper, we introduce TrajEvo, a framework that leverages Large Language Models (LLMs) to automatically design trajectory prediction heuristics. TrajEvo employs an evolutionary algorithm to generate and refine prediction heuristics from past trajectory data. We introduce a Cross-Generation Elite Sampling to promote population diversity and a Statistics Feedback Loop allowing the LLM to analyze alternative predictions. Our evaluations show TrajEvo outperforms previous heuristic methods on various real-world datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to an unseen real-world dataset. TrajEvo represents a first step toward automated design of fast, explainable, and generalizable trajectory prediction heuristics. We make our source code publicly available to foster future research.
S³: Spiking Neurons as an Isolating Segmenter for Brain Signal Decoding
Authors: Qian Zheng; Ming Chen; Sha Zhao; Shi Gu; Peng Lin; De Ma; Huajin Tang; Gang Pan
Venue: AAAI 2026 (main)

Abstract: Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain signals. To address this gap, we propose S3, which leverages spiking neurons as an isolating segmenter for brain signal decoding. S3 segments brain signals adaptively, considering subject- and task-level variability while preserving intrinsic temporal patterns of brain signals. It exploits the unique reset mechanism of spiking neurons to isolate previous irrelevant temporal patterns during the generation of each segmentation point. To optimize S3 for enhancing task performance in the absence of segmentation labels, we develop an optimization method where segmentation pseudo-labels are created with a stochastic-greedy algorithm to optimize them, while circumventing gradient blockade between S3 and task performance. Experiments on 10 downstream tasks across 13 public datasets demonstrate that S3 consistently outperforms existing methods, validating its effectiveness, generalizability and interpretability.
CAT-Net: A Cross-Attention Tone Network for Cross-Subject EEG-EMG Fusion Tone Decoding
Authors: Yifan Zhuang; Calvin Huang; Zepeng Yu; Yongjie Zou; Jiawei Ju
Venue: AAAI 2026 (main)

Abstract: Brain-computer interface (BCI) speech decoding has emerged as a promising tool for assisting individuals with speech impairments. In this context, the integration of electroencephalography (EEG) and electromyography (EMG) signals offers strong potential for enhancing decoding performance. Mandarin tone classification presents particular challenges, as tonal variations convey distinct meanings even when phonemes remain identical. In this study, we propose a novel cross-subject multimodal BCI decoding framework that fuses EEG and EMG signals to classify four Mandarin tones under both audible and silent speech conditions. Inspired by the cooperative mechanisms of neural and muscular systems in speech production, our neural decoding architecture combines spatial-temporal feature extraction branches with a cross-attention fusion mechanism, enabling informative interaction between modalities. We further incorporate domain-adversarial training to improve cross-subject generalization. We collected 4,800 EEG trials and 4,800 EMG trials from 10 participants using only twenty EEG and five EMG channels, demonstrating the feasibility of minimal-channel decoding. Despite employing lightweight modules, our model outperforms state-of-the-art baselines across all conditions, achieving average classification accuracies of 87.83\% for audible speech and 88.08\% for silent speech. In cross-subject evaluations, it still maintains strong performance with accuracies of 83.27\% and 85.10\% for audible and silent speech, respectively. We further conduct ablation studies to validate the effectiveness of each component. Our findings suggest that tone-level decoding with minimal EEG-EMG channels is feasible and potentially generalizable across subjects, contributing to the development of practical BCI applications.
MindSight: A Bio-Inspired Neural Architecture for Visual Restoration via Cortical Electrical Stimulation
Authors: Yongjie Zou; Haonan Niu; Bin Zhao; Guoliang Yi; Mengchuanzhi Yang; Jiawei Ju; Jiapeng Yin; Chengyu T. Li
Venue: AAAI 2026 (main)

Abstract: Visual impairment is a common condition worldwide, and cortical electrical stimulation is one of the approaches to aid in visual restoration. However, existing methods suffer from limited precision, flexibility, and generalization in generating the desired visual perception. In this paper, we propose a novel deep learning-based algorithm for cortical electrical stimulation, named ``MindSight," aimed at enhancing the clarity and accuracy of induced visual perceptions. Our framework introduces three key innovations: (1) A differentiable biophysical model simulating cortical state transitions under electrical stimulation, enabling end-to-end training; (2) A dual-path training architecture combining neural decoding fidelity with phosphene simulation constraints; (3) An attention-guided background gated network for input filtration and, a multi-channel activation constraint to ensure the effectiveness of electrical stimulation. We validated our approach through novel experiments with macaque monkeys, demonstrating superior performance in visual perception tasks. These results highlight the potential of our approach in assisting individuals with visual impairments.