🔄 Self-Supervised Learning¶

🤖 AAAI2026 · 16 paper notes

📌 Same area in other venues: 📷 CVPR2026 (92) · 🔬 ICLR2026 (81) · 💬 ACL2026 (1) · 🧪 ICML2026 (28) · 🧠 NeurIPS2025 (35) · 📹 ICCV2025 (13)

🔥 Top topics: Adversarial Robustness ×5 · Alignment/RLHF ×3 · Continual Learning ×3

Spikingformer: A Key Foundation Model for Spiking Neural Networks: This paper proposes Spikingformer, which integrates MS Residual with Self-Attention in a spike-driven manner to address the non-spike computation introduced by SEW Residual in Spikformer, while preserving global modeling capability.
From Pretrain to Pain: Adversarial Vulnerability of Video Foundation Models without Finetuning: This paper proposes Transferable Video Attack (TVA), which generates adversarial perturbations solely by exploiting the embedding space of open-source Video Foundation Models (VFMs), without any knowledge of downstream tasks, and effectively attacks downstream models and multimodal LLMs across 24 video tasks.
Improving Region Representation Learning from Urban Imagery with Noisy Long-Caption Supervision: This paper proposes UrbanLN, a framework that improves urban region representation learning from LLM-generated captions via a long-caption-aware positional encoding interpolation strategy and a dual-level (data and model) noise suppression mechanism.
FineXtrol: Controllable Motion Generation via Fine-Grained Text: This paper proposes FineXtrol, a framework that leverages temporally annotated, fine-grained body-part text descriptions as control signals. By combining a dual-branch ControlNet architecture with hierarchical contrastive learning to enhance the discriminability of the text encoder, FineXtrol achieves efficient, user-friendly, and precise controllable human motion generation, significantly outperforming existing methods on multi-body-part control benchmarks on HumanML3D.
Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space: This paper proposes L-HAKT, a framework that for the first time integrates LLM dual-agent design with hyperbolic geometry for knowledge tracing. A Teacher Agent parses exercise semantics and constructs a hierarchical knowledge graph, while a Student Agent simulates individual learning behaviors to generate synthetic interaction data. Hyperbolic contrastive learning is employed to calibrate the distributional gap between synthetic and real data. L-HAKT achieves an AUC of up to 80.29% across four educational datasets, with an AUC improvement of 13.03% over the GKT baseline on EdNet.
GOAL: Geometrically Optimal Alignment for Continual Generalized Category Discovery: Grounded in Neural Collapse theory, this paper replaces dynamic classifiers with a fixed Equiangular Tight Frame (ETF) classifier and achieves continual generalized category discovery via supervised alignment and confidence-guided unsupervised alignment, reducing forgetting by 16.1% and improving novel category discovery by 3.2% across four benchmarks.
Robust Tabular Foundation Models: This paper proposes RTFM — a model-agnostic adversarial training framework that performs min-max optimization over the parameter space of a synthetic data generator, maximizing the "optimality gap" between a tabular foundation model (TFM) and classical tree-based models. Using fewer than 100,000 additional synthetic datasets, RTFM significantly improves TabPFN V2 across multiple tabular benchmarks.
BCE3S: Binary Cross-Entropy Based Tripartite Synergistic Learning for Long-tailed Recognition: BCE3S is proposed, a binary cross-entropy (BCE)-based tripartite synergistic learning framework that integrates BCE-based joint learning, BCE-based contrastive learning, and BCE-based classifier uniformity learning. By decoupling per-class logits via Sigmoid, it suppresses the imbalance effects inherent to long-tailed distributions, achieving state-of-the-art performance on CIFAR10/100-LT, ImageNet-LT, and iNaturalist2018.
CATFormer: When Continual Learning Meets Spiking Transformers With Dynamic Thresholds: This paper proposes CATFormer, a data-replay-free continual learning framework built upon a spiking Vision Transformer, which achieves task-specific neuronal excitability modulation via context-adaptive dynamic firing thresholds. Over sequences of up to 100 tasks, the model not only avoids forgetting but actually improves in accuracy — a phenomenon the authors term "reverse forgetting."
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning: This paper proposes EPA-GRL (Explanation-Preserving Augmentation for Graph Representation Learning), which employs a GNN explainer trained with a small number of labels to identify semantic subgraphs (explanation subgraphs). During augmentation, only the non-semantic portions (marginal subgraphs) are perturbed, achieving semantics-preserving graph augmentation. EPA-GRL significantly outperforms semantics-agnostic random augmentation methods across 6 benchmarks.
HiLoMix: Robust High- and Low-Frequency Graph Learning Framework for Mixing Address Association: This paper proposes HiLoMix, a robust graph learning framework for the mixing address association task. It addresses three core challenges—graph sparsity, label scarcity, and label noise—through a Heterogeneous Attribute Mixing Interaction Graph (HAMIG), frequency-aware graph contrastive learning, and confidence-based label weighting supervision, respectively. HiLoMix surpasses the second-best baseline by 5.69%, 7.34%, and 15.61% on F1, AUC, and MRR.
Expandable and Differentiable Dual Memories with Orthogonal Regularization for Exemplar-free Continual Learning: This paper proposes EDD (Expandable and Differentiable Dual Memory), an exemplar-free continual learning method that decomposes data into reusable sub-features via differentiable shared and task-specific memories, combined with memory expansion-pruning and orthogonal regularization mechanisms. EDD surpasses 14 state-of-the-art methods on CIFAR-10/100 and Tiny-ImageNet, achieving final accuracies of 55.13%, 37.24%, and 30.11%, respectively.
FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Clients: This paper proposes FedGRPO, which reformulates foundation model optimization as a reward-based evaluation process. Through competence-aware expert selection and federated group-relative policy optimization (transmitting only scalar reward signals), FedGRPO achieves privacy-preserving, communication-efficient federated foundation model optimization, approaching or surpassing centralized GRPO on mathematical reasoning and question-answering tasks.
Improving Sustainability of Adversarial Examples in Class-Incremental Learning: This paper proposes the SAE framework to address the degradation of adversarial examples (AEs) caused by domain drift in class-incremental learning (CIL). Through a semantic correction module (jointly guided by CLIP and the CIL model) and a filtering-and-augmentation module (removing semantically confusing samples), SAE maintains attack effectiveness even after a 9× increase in the number of classes, achieving an average attack success rate improvement of 31.28%.
Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignment: This paper proposes SkipAlign, a framework that introduces a third "skip" operation alongside the conventional pull/push operations in contrastive learning. Low-confidence samples are selectively excluded from alignment and subjected only to mild repulsion, allowing in-distribution (ID) classes to form compact "galaxies" while OOD samples naturally disperse into the "interstellar void." The approach achieves an average AUC improvement of +3.1 on unseen OOD detection, with a maximum gain of +7.1.
Self-Supervised Inductive Logic Programming: This paper proposes a new self-supervised inductive logic programming (SS-ILP) setting and the Poker system, which starts from a small number of positive labeled examples and unlabeled examples, automatically generates positive and negative examples, and employs a maximally general second-order normal form (SONF) background theory to learn logic programs with recursion and predicate invention in the absence of negative examples.