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IJCAI 2026

Dual Branch Mutual Teaching for Long-Tailed Partial Label Learning

Xiangyu Ren, Mingxuan Xia, Guangcheng Zhu, Gengyu Lyu, Haobo Wang, Peng Lu

In Partial Label Learning (PLL), each instance is associated with a candidate label set, with exactly one label being true. While most studies implicitly assume balanced class distributions, real-world data often exhibit severe class imbalance distributions, leading to the Long-Tailed Partial Label Learning (LT-PLL) problem. In response, prevailing strategies typically manifest the label space to retrieve more tail samples for improved pseudo-label accuracy. However, hard-to-distinguish tail samples can significantly hinder representation learning and, in turn, affect the quality of pseudo-labels. To address this, we propose a novel Distribution-aware Dual-branch Contrastive Learning framework, DEACON, that decouples representation of head and tail labels via a dual-branch mutual-teaching design, enabling disambiguation across different shot-level groups with tailored representations. DEACON comprises two modules: (i)-a instance-balanced branch trained with a vanilla contrastive objective; (ii)-a label-balanced branch trained with a novel reweighted loss that adjusts the contrastive strength through the conditioned von-Mises Fisher density. The two branches mutually teach each other using their pseudo-labels, and we ensemble their predictions to improve overall performance. Extensive experiments on various benchmarks show that our method consistently outperforms state-of-the-art baselines.Code and appendix can be found in https://github.com/YukiNozzzz/DEACON.

IJCAI 2026

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

Hong Qian, Xiang Shu, Xiang Xia, Xuhui Liu, Yangde Fu, Bei Liang, Huibin Wang, Liang Dou

Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional subspace. However, determining the effective dimension of a task in advance remains a significant challenge, which influences the selection of the subspace dimensionality and the optimization performance. Traditional methods use fixed subspace dimensions provided by experts or rely on trial and error to estimate subspace dimensions with resources consumed. To this end, this paper proposes an automated random embedding for high-dimensional Bayesian optimization with unknown effective dimension, called Dynamic Shared Embedding Bayesian Optimization (DSEBO). DSEBO starts with a low dimension and switches to a higher subspace if the solutions in the current subspace show preliminary convergence. DSEBO dynamically determines the dimension of the next subspace based on the quality of the solutions in different subspaces and shares the queried solutions with the new subspace for a better initialization. Theoretically, we derive a regret bound for DSEBO and demonstrate that DSEBO can better balance approximation and optimization errors. Extensive experiments on functions with dimensionality of varying magnitudes and real-world tasks with unknown effective dimensions reveal that, compared with state-of-the-art methods, alternating optimization across different subspaces results in significant improvements in high-dimensional optimization, both in terms of optimization regret and time.

IJCAI 2026

TabKD: Tabular Knowledge Distillation Through Interaction Diversity of Learned Feature Bins

Shovon Niverd Pereira, Krishna Khadka, Yu Lei

Data-free knowledge distillation enables model distillation without original training data, which is critical for privacy-sensitive tabular domains. However, existing methods do not perform well on tabular data because they do not explicitly address feature interactions, which are critical for encoding predictive knowledge. We identify interaction diversity, systematic coverage of feature combinations, as an important factor for effective tabular model distillation. To operationalize this insight, we propose TabKD, which learns adaptive feature bins aligned with teacher decision boundaries, then generates synthetic queries that ensure uniform pairwise interaction coverage. Across 4 benchmark datasets and 4 teacher architectures, TabKD achieves the highest student-teacher agreement in 14 of 16 configurations, outperforming 5 state-of-the-art baselines. We further show that interaction coverage strongly correlates with distillation quality, validating our core hypothesis. Our work establishes interaction-focused exploration as a principled framework for tabular model distillation.

IJCAI 2026

Generating High-Diversity Synthetic Tabular Data via Less-Constrained Prior

Sanghun Park, Jaesung Lim, Jong-June Jeon, Seunghwan An

Generating high-quality synthetic tabular data, regarding fidelity, diversity, and utility, is crucial for many practical purposes. Recent tabular data synthesis methods, including two-stage generative modeling approaches, have achieved this with nearly perfect fidelity. However, we observe that there remains room for improving diversity, and we show that this limitation arises from an additional constraint imposed on the latent support. Our main contribution is that we effectively eliminate this redundant constraint by directly deriving the objective function from the KL-divergence between the ground-truth density and the generative model used for synthetic sample generation. We empirically demonstrate that our model, which relies on a single prior distribution, significantly improves the quality of the synthetic data, especially in terms of diversity. Our implementation code is available at https://anonymous.4open.science/r/SPT-1EE2/.

IJCAI 2026

Re-Weighting Cross-Modal Pairs via Rank Consistency for Noise-Robust Retrieval

Weiran Pan, Wei Wei

Image-text alignment relies on large-scale, well-aligned image-text pairs, yet web-crawled datasets inevitably introduce noisy correspondence. Existing methods typically re-weight training pairs by estimating semantic matching degrees from the model's own similarity predictions, which are inherently unreliable under noise and often overestimate partially aligned pairs. To overcome this limitation, we introduce a novel semantic matching degree estimation method based on ranking consistency. Our basic idea is that for a well-aligned image-text pair (I, T), the image I and text T should occupy semantically consistent positions in the embedding space. Thus, using I and T as queries to retrieve other texts (images) should yield two highly consistent rankings. Therefore, we reweight the training paris by measuring the Normalized Discounted Cumulative Gain (NDCG) between its cross-modal and intra-modal retrieval results. This signal is derived from the global data manifold structure rather than point-wise predictions, making it more robust to noise. Experiments on multiple cross-modal retrieval benchmarks validate the effectiveness of our method. Code is available at https://github.com/Aliinton/RCR.

IJCAI 2026

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

Hongyi Pan, Emadeldeen Hamdan, Xin Zhu, Ahmet Enis Cetin, Ulas Bagci

Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initialization remains challenging and computationally expensive. In this paper, we propose two complementary methods that leverage the Discrete Cosine Transform (DCT) to enhance the efficiency and performance of Vision Transformers. First, we address the initialization problem by introducing a simple yet effective DCT-based initialization strategy for self-attention, where projection weights are initialized using DCT coefficients. This structure-preserving approach consistently improves classification accuracy on the CIFAR-10 and ImageNet-1K benchmarks. Second, we propose a DCT-based attention compression technique that exploits the decorrelation properties of the frequency domain. By observing that high-frequency DCT coefficients typically correspond to noise, we truncate high-frequency components of the input patches, thereby reducing the dimensionality of the query, key, and value projections without sacrificing accuracy. Experiments on Swin Transformer models demonstrate that the proposed compression method achieves a substantial reduction in computational overhead while maintaining comparable performance. Code: https://github.com/NUBagciLab/DCT-Transformer.

IJCAI 2026

LoCo: Low-Rank Compositional Rotation Fine-Tuning

An Nguyen, Jaesik Choi, Anh Tong

Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language processing and computer vision. While existing methods such as low-rank adaptations achieve parameter efficiency via low-rank weight updates, they are limited in their ability to preserve the geometric structure of pretrained representations. We introduce Low-rank Compositional Orthogonal fine-tuning (LoCO), a novel PEFT method that constructs orthogonal transformations through low-rank skew-symmetric matrices and compositional rotation chains. We propose an approximation scheme that enables fully parallel computation of compositional rotations, making the approach practical for high-dimensional feature spaces. Our method maintains low computational complexity while maintaining orthogonality with controlled approximation error. We validate LoCO across diverse domains, including diffusion transformer fine-tuning, vision transformer adaptation, and language model adaptation. Our approach demonstrates superior or competitive performance compared to both existing orthogonal and non-orthogonal baselines.

IJCAI 2026

FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters

Feihong Nan, Zhengyi Zhong, pan wang, Weidong Bao, Xiongtao Zhang, Quan Wen, Ji Wang

Federated unlearning (FU) is critical for complying with legal mandates like the right to be forgotten in decentralized systems, yet current methods face a persistent dilemma between non-target knowledge loss and high request latency. To resolve these issues, we propose FedUP, a one-shot federated unlearning framework utilizing lightweight pluggable filters that act as a "knowledge funnel" to screen out target data while preserving original model performance. By freezing original model parameters and training filters at the server side using differentially private (DP)-protected class centroid samples, FedUP bypasses the need for multi-round client-server communication and complex retraining, reducing unlearning latency from minutes to mere seconds. Additionally, the framework's pluggable architecture ensures inherent reversibility, enabling the seamless restoration of forgotten knowledge by simply removing the filters. Extensive experiments on diverse image and text tasks demonstrate that FedUP effectively reduces non-target knowledge loss and achieves superior unlearning precision and efficiency across various scenarios. Code is available at: https://github.com/suows/FedUP-code.

IJCAI 2026

Deep Identification of Propagation Trees in Graph Diffusion

Zeeshan Memon, Chen Ling, Ruochen Kong, Vishwanath Seshagiri, Andreas Züfle, Liang Zhao

Understanding how information or influence propagates through a network, such as during an epidemic outbreak or the spread of misinformation, is a fundamental yet challenging problem. While prior works have focused on cascade prediction (forecasting future infected nodes), network inference (recovering latent global diffusion graphs), or source localization (identifying diffusion's origin), these approaches do not recover the actual "who-infected-whom" propagation tree for a specific diffusion instance. We introduce DIPT (Deep Identification of Propagation Trees), a probabilistic framework that infers propagation trees from final observed node diffusion states, without knowledge of the underlying diffusion mechanism. DIPT models local influence strengths between nodes and uses a discrete-continuous alternating optimization strategy to jointly learn the diffusion mechanism and infer the propagation structure. Empirical results across eight real-world datasets demonstrate that DIPT consistently outperforms existing approaches in reconstructing propagation trees.

IJCAI 2026

Can Quantum Federated Learning Withstand Circuit-Level Backdoors?

Aakar Mathur, Ruknuddin Mohammed, Ashish Gupta

Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit training and measurement-driven gradients. This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks by exploiting quantum-aware mechanisms, including Grover, Pauli, Bit-flip, and Sign-flip. By enabling malicious clients on both in-training and post-training surfaces, these attacks can critically undermine the learning process. We establish a rigorous theoretical foundation to demonstrate attack stealthiness under standard smoothness assumptions. Experiments on the MNIST and CIFAR-10 datasets with non-IID splits and varying fractions of malicious clients show that even a single malicious client can induce severe accuracy degradation under FedAvg aggregation. While popular defenses, including Krum, Multi-Krum, FoolsGold, FLGuardian, and Mud-HoG, reduce degradation in many regimes, they fail to eliminate worst-case failure cases, where accuracy drops up to 50%. The experimental analysis further reveals that under the CULT model, malicious updates effectively mask their presence by staying close to benign norms, thereby helping attackers evade detection.

IJCAI 2026

The Computational Complexity of Almost Stable Clustering with Penalties

Farnam Mansouri, Sandra Zilles, Kamyar Khodamoradi

We investigate the complexity of stable (or perturbation-resilient) instances of k-Means and k-Median clustering problems in metrics with small doubling dimension. While these problems have been extensively studied under multiplicative perturbation resilience in low-dimensional Euclidean spaces, we adopt a more general notion of stability, termed "almost stable", known from the literature as (alpha,epsilon)-perturbation resilience. Additionally, we extend our results to k-Means/k-Median with penalties, where each data point is either assigned to a cluster centre or incurs a penalty. We show that certain special cases of almost stable k-Means/k-Median (with penalties) are solvable in polynomial time. To complement this, we also examine the hardness of almost stable instances and (1 + 1/poly(n))-stable instances of k-Means/k-Median (with penalties), proving super-polynomial lower bounds on the runtime of any exact algorithm under the widely believed Exponential Time Hypothesis (ETH).

IJCAI 2026

Cooperative Multi-View Graph Learning via High-Rank Tensor Specificity

Ling Ma, Qiyu Zhong, Songxuan Shi, Hao Wei, Shunjie Yang, Junbo Lian, Qiuru Hai, Lianjin Yu, Xiangning Zeng, Yi Shan, Zhen Yang, Gengyu Lyu

Graph-based multi-view clustering, with its ability to mine potential associations between samples, has attracted extensive attention. To capture high-order correlations, tensor-based frameworks have been introduced to model multiple graphs jointly. Although these methods have achieved promising performance, existing methods mainly focus on stacking consistency graphs with low-rank constraints, while overlooking high-order specificity within diversity graphs, and the exploration of fine-grained diversity information among different samples across views remains insufficient. To address these issues, we propose High-Rank Tensor Specificity Induced Cooperative Multi-ViewGraph Learning (HTS-CMGL). Specifically, we first obtain the consistency graphs and diversity graphs from multi-view data, which are further reconstructed into the consistency tensor and the diversity tensor, respectively. Subsequently, a novel Enhanced Tensor Rank (ETR) is imposed on the consistency tensor, which is a tighter approximation of the tensor rank and is more noisy-robust to explore the high-order consistency. Meanwhile, we design a new Tensor High-Rank Logarithmic Norm (THLN) on the diversity tensor. By leveraging a unique high-rank constraint mechanism, THLN not only actively preserves high-rank and informative features, but also simultaneously captures view-level and sample-level diversity information. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed method on clustering multi-view data.

IJCAI 2026

SCD-MVC: Stable Conditional Diffusion for Multi-view Clustering

Jinli Ma, Chenkai Guo, Renda Han, Renxiang Guan, Siwei Wang, KE LIANG, Xiaoyu Cui, Dayu Hu

Multi-view clustering has garnered significant attention for its ability to integrate heterogeneous data and uncover underlying categorical structures. However, prevailing autoencoder-based methods often yield indiscriminative embeddings, rendering them prone to trivial solutions. While diffusion models have shown promise in modeling complex data distributions, their generative processes remain inherently unstable. More critically, the resulting latent representations often lack sufficient intra-class compactness and inter-class separability, leading to suboptimal clustering performance. In this paper, we propose SCD-MVC, a Stable Conditional Diffusion-driven framework for Multi-View Clustering. Specifically, it integrates conditional diffusion generation into the latent encoder to generate a target view conditioned on the remaining views, thereby facilitating robust joint distribution modeling. To align the generative process with clustering objectives, we introduce a clustering-guided semantic alignment module. This module regulates the diffusion path to enforce the learning of representations that are both view-consistent and discriminative. Furthermore, we introduce a temporal consistency regularization that enforces alignment between representations of adjacent diffusion steps. This stabilizes training and guides the optimization toward clustering-friendly representations. Extensive experiments on nine benchmark datasets demonstrate that SCD-MVC consistently outperforms eight state-of-the-art methods, achieving performance gains ranging from 6.68% to 13.09%. The source code is available at https://github.com/Knighttt0011/SCD-MVC.

IJCAI 2026

TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning

Dabiao Ma, Ziming Dai, Zhimin Xin, Shu Wang, Jian Yang, Haojun Fei

Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing a pervasive token-level redundancy in the fine-tuning of large models (LMs). We propose TS-PEFT, a theoretical framework utilizing proximal optimization that acts as a dynamic probe to identify token-level redundancy during the fine-tuning process. Extensive experiments demonstrate that indiscriminately updating all tokens is not only computationally superfluous but often introduces optimization noise. Surprisingly, by discarding 30%-70% of token updates, TS-PEFT consistently matches or exceeds the performance of dense baselines such as LoRA, DoRA. Our in-depth analysis shows that the learned token-level sparsity is a superior indicator of module importance compared to traditional weight criteria, providing a novel data-driven perspective on the intrinsic adaptation mechanism of LMs.

IJCAI 2026

Adversarial Optimization Scheme for Threat Detection Based on Hierarchical Oracle Supervision

Yiqing Luo, Mingshu He, Xiaojuan Wang

Conventional threat detection methodologies encounter substantial limitations in real-world complex network settings, owing to their dependence on single-dimensional analysis, vulnerability to adversarial perturbations, and propensity for overfitting. This study introduces an adversarial training optimization framework that incorporates hierarchical label encoding and prompt learning, designed to enhance model robustness and generalization in threat detection. The framework first establishes a hierarchical structure of attack scenarios and types, leveraging a graph attention network to encode semantic and structural dependencies among labels. Subsequently, the classification task is reformulated as a masked language modeling problem through prompt learning, enabling effective semantic alignment. Furthermore, a novel adversarial training mechanism is proposed, which utilizes local hierarchical information as a potent regularization signal. Within a game-theoretic architecture comprising a generator, an encoder, and a discriminator, the encoder is steered to integrate authentic hierarchical priors and produce high-fidelity oracle representations. This process encourages the generator to implicitly assimilate sample-specific hierarchical knowledge during adversarial learning, thereby improving the model's resilience to noisy inputs and its capacity to detect infrequent attacks. Evaluation results show that this method achieves an accuracy of 0.9972–1.0000 in complex mixed scenarios for scenario detection and 0.9987–0.9999 for attack category detection, while exhibiting strong robustness against interference.

IJCAI 2026

Attention as Selection: Semantic-Guided Time Series Forecasting

Xueyu Luo, Qiang Lu*, Sangui Jian, Yangxue Hu, Zhengyu Ying, Ye Yu, Wenxing Lu, Yan Qiao

Recent advances in time series forecasting (TSF) leverage large language models (LLMs) to provide semantic priors, enabling more robust forecasting under limited training data. However, directly fusing semantic signals into temporal features often induces modality entanglement and obscures local temporal structures when cross-modal correlations are weak, noisy, or inconsistent. To address these issues, we propose Attention as Selection (AAS), a dual-branch framework that decouples temporal and semantic representations. Specifically, we define cross-modal attention as a selection process, where semantic prompts are employed to induce a sparse temporal attribution distribution over temporal positions. This guides the model to focus on critical time steps without interfering with the construction of temporal representations. Furthermore, low-entropy regularization is employed alongside global cross-modal consistency constraints to regulate the selection behavior, ensuring that semantic guidance remains sparse, stable, and aligned with temporal dynamics. Extensive experiments on six real-world datasets demonstrate that AAS outperforms existing methods across various forecasting scenarios. Code is available at https://github.com/VIMLab-hfut/Attention-as-Selection.

IJCAI 2026

GRASP: Hard-Label Black-Box Malware Evasion with Higher Success, Fewer Queries, and Smaller Perturbations

Yutong Liu, Jianting Ning, Qi Feng, Yanjun Zhang, Yujin Huang, Leo Yu Zhang

Machine learning (ML)-based malware detectors are widely deployed but remain vulnerable to adversarial attacks. However, under hard-label black-box access, existing adversarial attacks on Windows Portable Executable (PE) malware are often query-inefficient and incur large file-size inflation. A common paradigm is to predefine a set of semantics-preserving atomic perturbations and search for evasive combinations under only binary feedback. Among these atomic perturbations, (i) those with highly combinatorial search spaces are difficult to explore effectively under hard-label feedback, leaving their potential untapped, and (ii) those relying on transplanting benign fragments are often laden with evasion-irrelevant bytes and exhibit highly variable adversarial utility. We propose Gradient-seeded Reinforcement Learning And Stealthy Pruning (GRASP), a three-stage framework that tackles these challenges. First, we decouple perturbations with highly combinatorial search spaces from the query-based search and instead apply a gradient-seeded warm-up that uses Gumbel-Softmax relaxation to enable gradient-based updates over the discrete space. This yields a strong warm start that improves evasion and reduces queries in later stages. Second, Reinforcement Learning (RL)-based refinement is accelerated by a perturbation library that filters, caches, and reuses compact high-utility patterns, reducing wasted queries on low-utility benign fragments. Third, a perturbation minimization stage removes redundant bytes while preserving evasion, reducing size inflation and feeding compact patterns back to the library. Experiments show that GRASP outperforms baselines, achieving higher attack success with fewer queries and smaller file-size inflation. We additionally demonstrate its practical effectiveness against commercial Antivirus engines.

IJCAI 2026

Adversarial Attack Framework Against Vision-Language Model Unlearning

Yimin Liu, Peng Jiang, Yajie Wang

Large Vision--Language Models (VLMs) unlearning tends to eliminate the influence of ``to-be-forgotten'' content in the training corpora, algorithmically by suppressing the likelihood of faithfully generating responses on forget-target inputs. The injection of adversarial inputs can manipulate the unlearned VLM's generation towards the attacker’s will, forcing the reproduction of the supposedly forgotten content and undermining the reliability of expected forgetting behavior. However, most attacks assume access to the unlearned VLM’s architecture or parameters, or to output logits via queries. In this paper, we propose SISA, a novel attack framework for crafting adversarial inputs to manipulate generation towards the forgotten target, which only requires access to a surrogate, pre-trained VLM. SISA advances prior attacks by exploiting the persistence of visual sink tokens after unlearning as a stable structural anchor for semantic alignment. SISA induces a sink regime on a candidate visual token to build the structural anchor that influences generation, and then semantically aligns model output to the target while conditioning on attention through the induced sink token, reinforcing the anchor for desired elicitation. With sink persistence and sink-conditioned semantic anchoring, SISA crafts transferable adversarial inputs. Evaluation on diverse unlearned VLM settings confirms the effectiveness of SISA, increasing the outputs’ semantic agreement with forgotten targets by up to 6.9X relative to clean inputs.

IJCAI 2026

Focus-LIME: Surgical Interpretation of Long-Context Large Language Models via Proxy-Based Neighborhood Selection

Junhao Liu, Haonan Yu, Zhenyu Yan, Xin Zhang

As Large Language Models (LLMs) scale to handle massive context windows, achieving surgical feature-level interpretation is essential for high-stakes tasks like legal auditing and code debugging. However, existing local model-agnostic explanation methods face a critical dilemma in these scenarios: feature-based methods suffer from attribution dilution due to high feature dimensionality, which prevents them from providing faithful explanations. In this paper, we propose Focus-LIME, a coarse-to-fine framework designed to restore the tractability of surgical interpretation. Focus-LIME utilizes a proxy model to curate the perturbation neighborhood, allowing the target model to perform fine-grained attribution exclusively within the optimized context. Empirical evaluations on long-context benchmarks demonstrate that our method makes surgical explanations practical and provides faithful explanations to users.

IJCAI 2026

BetaEdit: Null-Space Constrained Sequential Model Editing

Bingqing Liu, Wei Liu, Yuhua Li

Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge representation, thereby preserving the model's original behavior. However, in practice these methods rely on an approximate null space—leading to knowledge leakage—and further suffer from severe performance degradation during sequential editing. Recent work shows that history-aware editing strategies can empirically mitigate this decline, yet the underlying reason remains unclear. In this paper, we first expose the knowledge leakage inherent in existing null-space approaches and then analyze why history-aware updates effectively preserve both editing performance and general capabilities during long-horizon editing. Building on these insights, we propose BetaEdit, a refined framework that effectively controls the knowledge leakage and integrates history-aware updates into the null-space paradigm. Extensive experiments on three large language models across two standard benchmarks show that BetaEdit consistently outperforms prior methods in the challenging regime of massive-scale sequential editing. Code is available at: https://github.com/lbq8942/BetaEdit.

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