GenAI4ELab/papercli-papers huggingface dataset:
Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
Authors: Qiantao Yang; Liquan Chen; Mingfu Xue; Songze Li
Venue: AAAI 2026 (main)

Abstract: Federated learning has drawn widespread interest from researchers, yet the data heterogeneity across edge clients remains a key challenge, often degrading model performance. Existing methods enhance model compatibility with data heterogeneity by splitting models and knowledge distillation. However, they neglect the insufficient communication bandwidth and computing power on the client, failing to strike an effective balance between addressing data heterogeneity and accommodating limited client resources. To tackle this limitation, we propose a personalized federated learning method based on cosine sparsification parameter packing and dual-weighted aggregation (FedCSPACK), which effectively leverages the limited client resources and reduces the impact of data heterogeneity on model performance. In FedCSPACK, the client packages model parameters and selects the most contributing parameter packages for sharing based on cosine similarity, effectively reducing bandwidth requirements. The client then generates a mask matrix anchored to the shared parameter package to improve the alignment and aggregation efficiency of sparse updates on the server. Furthermore, directional and distribution distance weights are embedded in the mask to implement a weighted-guided aggregation mechanism, enhancing the robustness and generalization performance of the global model. Extensive experiments across four datasets using ten state-of-the-art methods demonstrate that FedCSPACK effectively improves communication and computational efficiency while maintaining high model accuracy.
TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling
Authors: Qirong Yang; Yucheng Guo; Zicheng Liu; Yujie Yang; Qijin Yin; Siyuan Li; Shaomin Ji; Linlin Chao; Xiaoming Zhang
Venue: AAAI 2026 (main)

Abstract: The modeling of genomic sequences presents unique challenges due to their long length and structural complexity. Traditional sequence models struggle to capture long-range dependencies and biological features inherent in DNA. In this work, we propose TrinityDNA, a novel DNA foundational model designed to address these challenges. The model integrates biologically informed components, including Groove Fusion for capturing DNA's structural features and Gated Reverse Complement (GRC) to handle the inherent symmetry of DNA sequences. Additionally, we introduce a multi-scale attention mechanism that allows the model to attend to varying levels of sequence dependencies, and an evolutionary training strategy that progressively adapts the model to both prokaryotic and eukaryotic genomes. TrinityDNA provides a more accurate and efficient approach to genomic sequence modeling, offering significant improvements in gene function prediction, regulatory mechanism discovery, and other genomics applications. Our model bridges the gap between machine learning techniques and biological insights, paving the way for more effective analysis of genomic data. Additionally, we introduced a new DNA long-sequence CDS annotation benchmark to make evaluations more comprehensive and oriented toward practical applications.
Debiased Cognitive Diagnosis: A Contrastive Counterfactual Modeling Method via Variational Autoencoder
Authors: Shangshang Yang; Xuewen Duan; Xiaoshan Yu; Ziwen Wang; Haiping Ma; Xingyi Zhang
Venue: AAAI 2026 (main)

Abstract: Cognitive diagnosis (CD), inferring student knowledge mastery based on historical response records, is crucial for personalized educational services such as adaptive practice and learning path planning. Existing CD models were built based on the assumption that student's response data is integral, overlooking the nonrandom missingness of data caused by student answering exercises selectively. 
This missingness generally leads to biased and incomplete observations, where confounders, such as selection bias and exposure bias, significantly undermine the accuracy of student knowledge modeling. 
To address missingness, we propose a Debiased Cognitive Diagnosis (DBCD) framework through the perspective of counterfactual modeling to remove exogenous confounders from the response data.
Specifically,  the proposed DBCD achieves debiasing for CD by applying the idea of contrastive learning to constrain the model's prediction distributions on both factual and counterfactual data.
For a student,  the factual data is his/her original response records, while the counterfactual data is generated by sampling the same number of exercises from all exercises of each concept through a similarity-based counterfactual sampling strategy.
Considering the difficulty of directly removing the exogenous confounders for student, we devise a β-Variational Autoencoder to model their exogenous confounders within the latent representations of knowledge proficiency by leveraging exercise priors and student response patterns. 
Then, the learned representations are further combined with the vanilla student's ability embedding via a gating mechanism-based fusion for final diagnosis prediction of the model.
Extensive experiments on real-world educational datasets demonstrate that the proposed DBCD  effectively mitigates confounders and even outperforms existing methods, thereby validating the feasibility and effectiveness of the DBCD framework.
HiNCoT: Hierarchical Nonlinear Continuous Transform-based Tensor Representation for Multi-Dimensional Data Recovery
Authors: Tao Yang; Weihao Wu; Tingzhu Huang
Venue: AAAI 2026 (main)

Abstract: Recently, continuous transform-based tensor representation has emerged as a promising tool for multi-dimensional data recovery. 
However, the existing continuous transforms are essentially single-layer linear mappings, which limits their ability to capture the complex relationships inherent in multi-dimensional data.
To overcome this limitation, we propose a Hierarchical Nonlinear Continuous Transform-based Tensor Representation (HiNCoT) for multi-dimensional data recovery. By leveraging the hierarchical nonlinear continuous transform, HiNCoT constructs the recovered tensor from a latent tensor, which is generated by the deep representation module with a low-rank core tensor as input. Compared with the existing continuous transform-based methods, HiNCoT can more effectively capture the complex nonlinear relationships inherent in multi-dimensional data along the third dimension.
To evaluate the effectiveness of the proposed HiNCoT, we suggest an HiNCoT-based multi-dimensional data recovery model. Extensive experiments on diverse degeneration scenarios demonstrate the superiority of our hierarchical nonlinear transform-based method over existing single-layer linear transform-based methods.
Know Your Neighbors: Subgraph Importance Sampling for Heterophilic Graph Active Learning
Authors: Wenjie Yang; Shengzhong Zhang; Chen Ye; Jiaxing Guo; Tongshan Xu; Zengfeng Huang
Venue: AAAI 2026 (main)

Abstract: Graph neural networks (GNNs) have demonstrated strong performance in various graph mining tasks but rely heavily on extensively labeled nodes. To improve training efficiency, graph active learning (GAL) has emerged as a solution for selecting the most informative nodes for labeling. However, existing GAL methods are primarily designed for homophilic graphs, where nodes with the same labels are more likely to be connected. In this work, we systematically study active learning on heterophilic graphs, a setting that has received limited attention. Surprisingly, we observe that existing GAL methods fail to consistently outperform random sampling on heterophilic graphs. Through an in-depth investigation, we reveal that these methods implicitly assume homophily even on heterophilic graphs, leading to suboptimal performance. To address this issue, we introduce the principle of "Know Your Neighbors" and propose an active learning algorithm KyN specifically for heterophilic graphs. The core idea of KyN is to provide GNNs with accurate estimations of homophily distribution by labeling nodes together with their neighbors. We implement KyN based on subgraph sampling with probabilities proportional to l1 Lewis weights, which is supported by solid theoretical guarantees. Extensive experiments on diverse real-world datasets, including a large heterophilic graph with over 2 million nodes, demonstrate the effectiveness and scalability of KyN.
Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training
Authors: Xi Yang; Han Zhang; Zhijian Lin; Yibiao Hu; Hong Han
Venue: AAAI 2026 (main)

Abstract: Out-of-context misinformation (OOC) is a low-cost form of misinformation in news reports, which refers to place authentic images into out-of-context or fabricated image-text pairings. This problem has attracted significant attention from researchers in recent years. Current methods focus on assessing image-text consistency or generating explanations. However, these approaches assume that the training and test data are drawn from the same distribution. When encountering novel news domains, models tend to perform poorly due to the lack of prior knowledge. To address this challenge, we propose Variational Domain-Invariant Learning with Test-Time Training (VDT) framework to enhance the domain adaptation capability for OOC misinformation detection. Domain-Invariant Variational Align module is employed to jointly encodes source and target domain data to learn a separable distributional space and domain-invariant features. For preserving semantic integrity, we utilize domain consistency constraint module to reconstruct the source and target domain latent distribution. During testing phase, we adopt the test-time training strategy and confidence-variance filtering module to dynamically updating the VAE encoder and classifier, facilitating the model's adaptation to the target domain distribution. Extensive experiments conducted on the benchmark dataset NewsCLIPpings demonstrate that our method outperforms state-of-the-art baselines under most domain adaptation settings.
BidMatch: Boosting Semi-Supervised Learning by Bi-Dimensional Sample Weight Guidance
Authors: Xianling Yang; Zhiwen Yu; Sun Song; Kaixiang Yang
Venue: AAAI 2026 (main)

Abstract: Semi-supervised learning (SSL) based on pseudo-label and consistency has achieved significant success. The core idea behind these methods is to assign sample weights based on pseudo-label probabilities, thereby guiding the model toward biased learning. However, existing research still faces two major challenges in guiding learning: (1) how to evaluate learning states across different classes in the absence of labels, and (2) how to construct an effective sample weight space that provides precise guidance throughout training. To address these challenges, we propose the Bi-Dimensional Sample Weight Guidance algorithm, BidMatch. BidMatch introduces Class Information Entropy (CIE), which captures the learning relationships between classes and reflects the model’s learning state for each class. Additionally, Pseudo-label Probability Redistribution (PPR) is proposed to maintain distribution invariance and sparsity during training, thereby emphasizing differences in sample importance. By leveraging CIE and PPR, BidMatch generates sample weights that account for both class and instance dimensions, effectively guiding the model toward balanced and efficient learning across classes. BidMatch has demonstrated state-of-the-art performance on various SSL datasets. Notably, it achieved a 6.45% error rate on CIFAR-10 with only one label per class, significantly outperforming baseline methods.
Step Back to Leap Forward: Self-Backtracking for Symbolic Reasoning and Planning in Language Models
Authors: Xiao-Wen Yang; Xuan-Yi Zhu; Ding-Chu Zhang; Wen-Da Wei; Jie-Jing Shao; Zhi Zhou; Lan-Zhe Guo; Yu-Feng Li
Venue: AAAI 2026 (main)

Abstract: Although autoregressive language models demonstrated remarkable performance across various tasks, their effectiveness in symbolic reasoning and decision-making scenarios remains constrained.  Recent research indicates that training language models to emulate symbolic search algorithms (e.g. depth-first search or A* algorithm) can yield strong improvements in their symbolic reasoning and planning capabilities. However, existing methods only achieve superficial imitation of symbolic search trajectories, as their generation processes lack explicit backtracking mechanisms.  This limitation prevents models from truly mastering symbolic search, often resulting in rigid and redundant outputs with poor solution quality. To address this issue, we propose a self-backtracking mechanism that enables LLMs to autonomously determine when to backtrack through specialized training, effectively utilizing this capability to scale during inference.  By introducing a self-improvement strategy, the model can further refine its search process into optimal solution generation, improving problem-solving efficiency. Empirical evaluations demonstrate that our method boosts LLMs' reasoning on the Countdown task by 40% over optimal-path supervised fine-tuning (SFT) and improves both performance and efficiency on the Maze Navigation task.
Flow-Based Knowledge Transfer for Efficient Large Model Distillation
Authors: Xinye Yang; Junhao Wang; RuiLi; Haosen Sun; Xuesheng Zhang; Zebang Liu; Gaochao Xu; Yiwei Chen
Venue: AAAI 2026 (main)

Abstract: Traditional knowledge distillation relies on simple MSE or KL divergence losses that fail to capture the complex distributional relationships between teacher and student model representations. We propose FlowDistill, a novel distillation framework that employs normalizing flows to model and transfer the intricate knowledge distributions from teacher to student models. Our approach introduces three key innovations: (1) Invertible Knowledge Mapping using continuous normalizing flows (CNFs) to learn bijective transformations between teacher and student representation spaces, enabling precise knowledge transfer without information loss, (2) Flow-Guided Progressive Distillation that gradually increases the complexity of knowledge transfer by learning hierarchical flow transformations from simple to complex distributions, and (3) Conditional Flow Networks that adapt knowledge transfer based on input context and task requirements. Unlike previous diffusion-based distillation methods such as DiffKD that suffer from computational overhead due to iterative denoising processes and information loss during noise addition, our flow-based approach provides exact invertible transformations with significantly reduced computational cost. Extensive experiments on ImageNet classification, COCO object detection, and Cityscapes semantic segmentation demonstrate that FlowDistill achieves superior performance with 2.1% accuracy improvement over DiffKD on ResNet-34 to ResNet-18 distillation while reducing inference time by 3.5×. Our method establishes new state-of-the-art results across multiple distillation benchmarks and provides theoretical guarantees for lossless knowledge transfer through invertible flow transformations.
Self-Indexing KVCache: Predicting Sparse Attention from Compressed Keys
Authors: Xu Yang; Jiapeng Zhang; Dongyang Zhao; Guo Chen; Zhuo Tang
Venue: AAAI 2026 (main)

Abstract: The KV cache in self-attention has emerged as a major bottleneck in long-context and large-batch inference for LLMs. Existing approaches often treat sparsity prediction and compression as separate modules—relying on auxiliary index structures to select relevant tokens, and on complex quantization schemes to reduce memory usage. This fragmented design introduces redundant overhead and limits scalability.

In this paper, we propose a novel paradigm: treating the compressed key representation not merely as storage, but as a self-indexing structure that directly enables efficient sparse attention. By designing a sign-based 1-bit vector quantization (VQ) scheme, our method unifies compression and retrieval in a single, hardware-friendly format. This approach eliminates the need for external indices or learning-based predictors, offering a lightweight yet robust solution for memory-constrained inference. 

All components are designed to be hardware-efficient and easy to implement. By implementing  custom CUDA kernels, our method integrates seamlessly with FlashAttention, minimizing additional runtime and memory overhead. Experimental results demonstrate that our approach delivers both effectiveness and efficiency.
Diffusion-calibrated Continual Test-time Adaptation
Authors: Xu Yang; Moqi Li; Kun Wei
Venue: AAAI 2026 (main)

Abstract: Continual Test-Time Domain Adaptation (CTTA) aims to adapt a pre-trained source model to a dynamically evolving target domain without requiring additional data collection or labeling efforts. A key challenge in this setting is to achieve rapid performance improvement in the current domain using unlabeled data, while avoiding impairing generalization to future domains in complex scenarios. To enhance the discriminative capability of the inference models, we propose a novel framework that integrates an external auxiliary generative model with a test-time adaptive method, leveraging cross-validation to identify reliable supervisory signals. Specifically, for each test instance, we utilize a diffusion module to generate a calibrated instance under the textual description of its predicted category. Based on the generated one, we design a learning strategy with the following components: (1) the calibrated instance and its category are used to form a supervisory signal; (2) the predicted category of the calibrated instance is compared with the test instance for selecting reliable signals. For these generated and selected instances, adaptive weighting is applied during optimization to stabilize the category distribution and preserve prediction diversity. Finally, based on the inverse process of diffusion, we construct the negative instance of the generated instance and introduce a robust contrastive learning to further calibrate model optimization. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple benchmarks. Ablation studies further validate the effectiveness of each proposed component.
Bayes-Optimal Fair Classification with Multiple Sensitive Features
Authors: Yi Yang; Yinghui Huang; Xiangyu Chang
Venue: AAAI 2026 (main)

Abstract: Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive features. In this paper, we characterize the Bayes-optimal fair classifier for multiple sensitive features under general approximate fairness measures, including *mean difference* (MD) and *mean ratio* (MR). We show that these approximate measures for existing group fairness notions, including Demographic Parity, Equal Opportunity, Predictive Equality, and Accuracy Parity, are linear transformations of selection rates for specific groups defined by both labels and sensitive features. We then characterize that Bayes-optimal fair classifiers for multiple sensitive features under both MD and MR become instance-dependent thresholding rules that rely on a weighted sum of these group membership probabilities. Our framework applies to both attribute-aware and attribute-blind settings and can accommodate composite fairness notions like Equalized Odds. Building on this, we propose two practical algorithms for Bayes-optimal fair classification via in-processing and post-processing. We show empirically that our methods compare favorably to existing methods.
Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach
Authors: Yiyuan Yang; Guodong Long; Qinghua Lu; Liming Zhu; Jing Jiang
Venue: AAAI 2026 (main)

Abstract: Federated foundation models represent a new paradigm to jointly fine-tune pre-trained foundation models across clients. It is still a challenge to fine-tune foundation models for a small group of new users or specialized scenarios, which typically involve limited data compared to the large-scale data used in pre-training. In this context, the trade-off between personalization and federation becomes more sensitive. To tackle these, we proposed a bi-level personalization framework for federated fine-tuning on foundation models. Specifically, we conduct personalized fine-tuning on the client-level using its private data, and then conduct a personalized aggregation on the server-level using similar users measured by client-specific task vectors. Given the personalization information gained from client-level fine-tuning, the server-level personalized aggregation can gain group-wise personalization information while mitigating the disturbance of irrelevant or interest-conflict clients with non-IID data. The effectiveness of the proposed algorithm has been demonstrated by extensive experimental analysis in benchmark datasets.
DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks
Authors: Yunfei Yang; Xiaojun Chen; Yuexin Xuan; Zhendong Zhao; Xin Zhao; He Li
Venue: AAAI 2026 (main)

Abstract: Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific input-output pairs. However, existing watermarks are easily removed when facing model stealing attacks, and make it difficult for model owners to effectively verify the copyright of stolen models. In this paper, we analyze the root cause of the failure of current watermarking methods under model stealing scenarios and then explore potential solutions. Specifically, we introduce a robust watermarking framework, DeepTracer, which leverages a novel watermark samples construction method and a same-class coupling loss constraint. DeepTracer can incur a high-coupling model between watermark task and primary task that makes adversaries inevitably learn the hidden watermark task when stealing the primary task functionality. Furthermore, we propose an effective watermark samples filtering mechanism that elaborately select watermark key samples used in model ownership verification to enhance the reliability of watermarks. Extensive experiments across multiple datasets and models demonstrate that our method surpasses existing approaches in defending against various model stealing attacks, as well as watermark attacks, and achieves new state-of-the-art effectiveness and robustness.
Streaming Generated Gaussian Process Experts for Online Learning and Control
Authors: Zewen Yang; Dongfa Zhang; Xiaobing Dai; Fengyi Yu; Chi Zhang; Bingkun Huang; Hamid Sadeghian; Sami Haddadin
Venue: AAAI 2026 (main)

Abstract: Gaussian Processes (GPs), as a nonparametric learning method, offer flexible modeling capabilities and calibrated uncertainty quantification for function approximations. Additionally, GPs support online learning by efficiently incorporating new data with polynomial-time computation, making them well-suited for safety-critical dynamical systems that require rapid adaptation. However, the inference and online updates of exact GPs, when processing streaming data, incur cubic computation time and quadratic storage memory complexity, limiting their scalability to large datasets in real-time settings. In this paper, we propose a streaming kernel-induced progressively generated expert framework of Gaussian processes (SkyGP) that addresses both computational and memory constraints by maintaining a bounded set of experts, while inheriting the learning performance guarantees from exact Gaussian processes. Furthermore, two SkyGP variants are introduced, each tailored to a specific objective, either maximizing prediction accuracy (SkyGP-Dense) or improving computational efficiency (SkyGP-Fast). The effectiveness of SkyGP is validated through extensive benchmarks and real-time control experiments demonstrating its superior performance compared to state-of-the-art approaches.