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IJCAI 2026

Stability and Generalization for Decentralized Markov SGD

Jiahuan Wang, Ziqing Wen, Ping Luo, Dongsheng Li, Tao Sun

Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. In many practical applications, data are generated by Markov chains and learning is performed in a decentralized manner, which introduces significant analytical challenges. In this work, we investigate the stability and generalization of decentralized stochastic gradient descent (SGD) and stochastic gradient descent ascent (SGDA) under Markov chain sampling. Leveraging a stability-based framework, we characterize how Markovian dependence and decentralized communication jointly influence generalization behavior. Our analysis captures the effects of network topology, Markov chain mixing properties, and primal–dual dynamics. We establish non-asymptotic generalization bounds for both algorithms, extending existing results on Markov stochastic gradient methods to decentralized and minimax settings.

IJCAI 2026

Salient-Residual Decoupled Multi-View Learning for Clustering

Gaokai Wang, Yazhou Ren, Fengyu Zhang, Jie Xu, Chaoning Zhang, Zhen Long, Ce Zhu

Multi-view clustering aims to utilize information from multiple feature representations to uncover underlying data structures. Most existing methods emphasize learning a consensus representation by enforcing consistency across views. However, those structures that cannot be directly incorporated into the clustering space receive little attention, and are often discarded as noise in practice. In this paper, we argue that such information can be informative signals for the clustering process and should be explicitly modeled rather than suppressed or ignored. Specifically, we propose salient-residual decoupled multi-view learning for clustering, SRDMVC, introducing a novel decomposition-fusion iterative optimization, which separates the feature space into a salient space and a residual subspace effectively and fuses them using a novel attention mechanism. The residual subspace can capture the deep-level structure of view information, making positive contributions to the clustering process, under proper constraints. Without assuming stronger view alignment or complementarity, SRDMVC enhances cluster discrimination, avoids spurious consensus, and alleviates representation degradation. Extensive experiments on benchmark datasets demonstrate the superior performance of the method we propose.

IJCAI 2026

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

Alessandro Trapasso, Luca Iocchi, Fabio Patrizi

Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties. Markovian Reinforcement Learning (RL) can be inadequate for such tasks, while modeling them as non-Markovian reward decision processes (NMRDPs) enables agents to handle temporal dependencies. Existing approaches offer limited formal guarantees on both (near-)optimality and sample efficiency. We address both issues with QR-max, a novel model-based algorithm for NMRDPs with discrete actions that factorizes Markovian transition learning from non-Markovian reward handling via reward machines. To our knowledge, this is the first model-based RL algorithm for discrete-action NMRDPs that leverages this factorization to obtain PAC convergence to ε-optimal policies with polynomial sample complexity. We then extend QR-max to continuous state spaces with Bucket-QR-max, a SimHash-based discretizer that preserves the same factorized structure and achieves fast and stable learning without manual gridding or function approximation. We experimentally compare our method with state-of-the-art model-based RL approaches on environments of increasing complexity, showing substantially improved sample efficiency and greater robustness in finding optimal policies.

IJCAI 2026

Sharp-Wave Ripples Learning: A Bio-Inspired Incremental Learning Method

Yi Sun, Xiaochang Hu, Wenzhuo Zhang, Zhiwei Wang, Jiting Li, Jian Li, Xu Xin, jia jun

The primary challenge in incremental learning for deep neural networks (DNNs) lies in balancing memory stability with learning plasticity as they incrementally acquire new knowledge. Existing incremental learning methods typically require storing portions of old datasets or relying on specialized network architectures to achieve learning without forgetting. Recent neuroscience findings reveal that hippocampal sharp-wave ripples play critical roles in consolidating neocortex memory by internally triggering the reactivation of spontaneous neural circuits without external stimulus. Inspired by this biological mechanism, we propose an incremental learning method termed Sharp-Wave Ripples Learning (SWRL), a novel incremental learning framework composed of two functionally complementary modules: a neocortex-like network for long-term memory and a hippocampus-like model for rapid new-knowledge encoding. SWRL continuously integrates newly acquired knowledge from the hippocampus-like module into the neocortex-like network via a meta-weighted memory integration mechanism guided by internally generated SWR-like signals. Our framework is task-agnostic, architecture-agnostic, and exemplar-free. Experimental results on multiple public benchmarks demonstrate that SWRL achieves superior incremental learning performance under various scenarios across diverse DNN architectures, requiring no access to previous data or modifications to the base architecture. The code and appendix are both released at https://github.com/SYVAE/SWRL.

IJCAI 2026

Label Distribution Imputation and Bias-Corrective Representation Learning for Incomplete and Imbalanced Label Distribution

Xiangcheng Sun, Miaogen Ling, Han Qin, Guohua Lv, Yongbiao Gao

Label Distribution Learning (LDL) represents each instance with a label distribution, but these distributions are often incomplete in practice due to high annotation costs and annotators’ cognitive burden. Recent Incomplete Label Distribution Learning (InLDL) methods leverage global and local correlations to impute missing values, imputation errors remain systematic and non-uniform across labels. However, in the more challenging scenario of imbalanced label distributions, such non-uniform errors disproportionately harm low-degree labels, skewing learning toward dominant labels. To address the more complex challenge, we propose Label Distribution Imputation and Bias-Corrective Representations (LDIBR) for incomplete and imbalanced label distribution learning. LDIBR performs instance-adaptive imputation conditioned on instance features and the binary observation mask. It learns a prior, a reliability-gated correction, and entry-wise fusion weights to produce a normalized imputed distribution. Additionally, LDIBR decomposes the imputed distributions into a shared low-rank core and an instance-specific residual. The core captures stable global degree patterns, while the residual isolates sample-specific deviations and imputation noise. Comprehensive experiments across multiple benchmark datasets with a 50% missing ratio validate both the effectiveness and robustness of the LDIBR approach. The code is available at https://github.com/wenhuihji/LDCBR.

IJCAI 2026

Bridging the Biophysical Gap: Holistic Environmental Awareness for 3D Linker Design

Mengwei Sun, Chengwei Ai, Xiaoyi Liu, Shiqiang Ma, Qiaozhen Meng, Fei Guo

3D molecular linker design is a critical task in structure-based drug discovery, which requires the precise synthesis of chemical bridges to connect fragments within the constrained environment of a protein binding pocket. Existing methods often suffer from environmental blindness, treating the inter-fragment space as a vacuum and yielding candidates with poor binding affinity or severe steric clashes. To address this, we propose LinkerBridge, an equivariant framework that unifies biochemical semantics with physical constraints through two innovations: a Contextual Interaction-Aware Representation module that internalizes pre-existing biochemical semantics, and a Differentiable Physical Guidance mechanism derived from Van der Waals potentials to steer generation away from collision zones. Extensive evaluations on ZINC, GEOM, and BindingMOAD benchmarks, as well as Hsp90 and JNK3 case studies, demonstrate that LinkerBridge significantly outperforms state-of-the-art methods in real-world drug discovery.

IJCAI 2026

Learning Local Feature Masks with Variational Information Bottleneck

Lu Sun, Jun Sakuma

Instance-wise feature selection (IWFS) identifies informative features for each instance, improving generalization by discarding irrelevant information and enhancing interpretability through personalized explanations. Most IWFS methods adopt a selector--predictor architecture, where a selector generates instance-specific masks to guide prediction. This often leads to co-adaptation, in which the selector encodes label information into the mask, resulting in spurious correlations and unfaithful explanations. Existing methods also struggle to capture diverse local patterns, which is critical for IWFS under heterogeneous sparsity. We propose VIBMask, a unified IWFS framework by the variational information bottleneck. VIBMask mitigates co-adaptation by penalizing mutual information between unselected features and the label, and improves expressivity via an ensemble of diverse selectors that capture heterogeneous sparse patterns. We further derive a novel variational lower bound for discrete masks, enabling efficient end-to-end training through reparameterization. Experiments on synthetic and real datasets show that VIBMask consistently outperforms state-of-the-art IWFS methods in both predictive accuracy and informative feature discovery.

IJCAI 2026

Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data

Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada

Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data. When statistical heterogeneity across clients is severe, Clustered Federated Learning (CFL) can improve performance by grouping similar clients and training cluster-wise models. However, most CFL approaches rely on multiple communication rounds for cluster estimation and model updates, which limits their practicality under tight constraints on communication rounds. We propose Data Collaboration-based Clustered Federated Learning (DC-CFL), a single-round framework that completes both client clustering and cluster-wise learning, using only the information shared in DC analysis. DC-CFL quantifies inter-client similarity via total variation distance between label distributions, estimates clusters using hierarchical clustering, and performs cluster-wise learning via DC analysis. Experiments on multiple open datasets under representative non-IID conditions show that DC-CFL achieves accuracy comparable to multi-round baselines while requiring only one communication round. These results indicate that DC-CFL is a practical alternative for collaborative AI model development when multiple communication rounds are impractical. Our source code is publicly available at https://github.com/soutasuga/DC-CFL.

IJCAI 2026

ITBoost: Information-Theoretic Trust for Robust Boosting

Ye Su, Longlong Zhao, Diego García-Gil, Jipeng Guo, Gangchun Zhang, Jinxin Chen, Jinsong Chen

Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy. This behavior is largely related to the way boosting algorithms emphasize samples with large gradients, without explicitly accounting for whether such errors originate from informative hard cases or from unreliable labels. We address this issue by reconsidering how sample reliability is evaluated during boosting. Instead of relying on instantaneous error, we examine the evolution of each sample’s residuals across iterations. Based on this insight, we propose Information-Theoretic Trust Boosting (ITBoost), which uses the Minimum Description Length principle to measure the complexity of residual trajectories. Samples whose residual patterns fluctuate in an irregular manner are treated as less trustworthy and are down-weighted during learning. Theoretically, we derive a tighter generalization bound for ITBoost under label noise. Empirical results on various tabular benchmarks indicate that ITBoost provides improved robustness in noisy environments over leading boosting and deep tabular models, while retaining best average performance on clean data.

IJCAI 2026

Survival Fully-Collapsed Copula Mixed Membership Blockmodel

Yanxing Song, Richard Yi Da Xu

Survival models on networks describe the time-evolving dynamics of complex systems, but existing frameworks suffer from two key limitations: First, many past approaches implicitly assume independent censoring, which can introduce substantial bias. Second, point-process-based models often emphasize only sending behavior and overlook shared latent structure between interacting nodes. Recent work has incorporated copulas into deep survival models to mitigate censoring bias, but these methods do not capture topological role structure. Another line of research combines survival analysis with the Mixed-Membership Stochastic Blockmodel (MMSB) to model heterogeneity via role-specific hazard rates; however, it typically assumes structural independence and therefore misses important correlations between roles. To bridge these gaps, we apply an improved Copula–MMSB mechanism and propose the first Survival Fully-Collapsed Copula Mixed Membership Blockmodel. Our framework jointly models time-to-event dynamics and complex dependencies among latent roles. On the Enron benchmark, it surpasses state-of-the-art survival blockmodels in both predictive likelihood and structural interpretability. Additionally, the model admits an exact closed-form discrete distribution over role pairs, which enables a fully collapsed Gibbs sampler and avoids the slow mixing and poor convergence common in non-conjugate inference. In simulations, this yields a 100× improvement in sampling efficiency over semi-collapsed baselines.

IJCAI 2026

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies

Jingze Song, Zihao Chen, Wenqing Chen, Zibin Zheng

Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters more than data quantity. Accordingly, prior studies have introduced various multi-dimensional heuristics to evaluate and filter instruction data. However,most existing methods rely on static task-agnostic and model-agnostic weighting schemes, which overlook the varying requirements of specific downstream tasks and the differing pre-existing capabilities of models. In this paper, we propose a framework for learning multi-indicator weights that jointly adapts data selection to both the downstream task and the specific model. Our method identifies optimal weight configurations without full-scale fine-tuning by utilizing in-context learning (ICL) signals on compact tiny-validation sets. These signals serve as efficient performance proxies that ensure high-fidelity evaluation at minimal computational cost. Experiments across multiple benchmarks and model families, including Mistral, Qwen, and Llama, show that the approach achieves performance comparable to or exceeding full-dataset tuning while using only 30% of the training samples on GSM8K. Furthermore, our analysis reveals a trade-off between semantic diversity and logical complexity in reasoning tasks, highlighting the necessity of joint task-model adaptation.

IJCAI 2026

Causal Manifold Transport for Identifiable Causal Generation in Diffusion Models

Junghyo Sohn, Wootaek Jeong, Sujeong Song, Jee Seok Yoon, Heung-Il Suk

Identifying meaningful latent representations within diffusion models remains a challenging problem for causal approaches. We propose Causal Manifold Transport Diffusion Model (CMT-Diff), a framework that operationalizes causal actions as geometric transformations. By adopting the perspective of backtracking counterfactuals, we formulate the generative process as a composite diffeomorphism that couples the Probability Flow ODE with a Continuous Normalizing Flow. This mapping constructs an exogenous manifold where causal factors align with coordinate variations. Within this geometry, we derive Causal Manifold Transport (CMT) to realize interventions as linear vector translations along factor-aligned directions. We establish theoretical identifiability guarantees and demonstrate that our approach facilitates controllable generation by capturing the underlying causal manifold.

IJCAI 2026

Learning Minimally Rigid Graphs with High Realization Counts

Oleksandr Slyvka, Jan Rubeš, Rodrigo Alves, Jan Legerský

For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptionally many realizations is an extremal problem in rigidity theory, but exhaustive search quickly becomes infeasible due to the super-exponential growth of the number of candidate graphs and the high cost of realization-count evaluation. We propose a reinforcement-learning approach that constructs minimally rigid graphs via 0- and 1-extensions, also known as Henneberg moves. We optimize realization-count invariants using the Deep Cross-Entropy Method with a policy parameterized by a Graph Isomorphism Network encoder and a permutation-equivariant extension-level action head. Empirically, our method matches the known optima for planar realization counts and improves the best known bounds for spherical realization counts, yielding new record graphs.

IJCAI 2026

SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators

Pengqing Shi, Jie Yin, Stephen Tierney, Junbin Gao

Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations. However, owing to the reliance on frequency truncation to maintain learning efficiency of FNOs, empirical studies suggest that FNOs exhibit spectral bias toward low-frequency information, which may hinder the learning capability especially for certain PDEs with strong high-frequency oscillations. To address this limitation, we propose SirenFNO, a novel framework that leverages sinusoidal representation networks (SIRENs) to learn implicit neural representations and performs mode-wise kernel parameterization. Our SIREN parameterization learns a full-grid spectrum with a constant and discretization-independent parameter count, thereby eliminating the need for frequency truncation. We further extend SirenFNO with functional tensor decompositions to enhance parameter and learning efficiency. Empirical results show that our SirenFNO consistently outperforms FNO with approximately 4 to 15 times parameter reductions with preserved discretization invariance, and our functional decomposition variants obtain performance improvements with a maximum of 73 times fewer parameters across multiple PDE benchmarks.

IJCAI 2026

URFPert: Unrolled Regulatory Flow Networks for Out-of-Distribution Single-Cell Perturbation Prediction

Xiaoqi Sheng, Jiawen Liu, Yutong Li, Sankar Mondal, Jiaming Liang, Tinghe Zhang, Hongmin Cai

Single-cell perturbation screens enable systematic discovery of gene regulatory mechanisms, yet the exponential expansion of perturbation space makes comprehensive experimentation impractical. Although in silico predictors have been increasingly proposed to address this challenge, most existing methods either assume static regulatory priors or learn unconstrained data-driven representations. As a result, they often suffer from poor adaptation to state-dependent shifts and weak out-of-distribution generalization. In response, we propose URFPert, an unrolled regulatory flow network tailored for single-cell perturbation prediction. Specifically, URFPert is composed of three fundamental modules: (i) Regulatory Structure Unrolling module, which employs an iterative unrolling optimization algorithm to infer state-dependent gene regulatory networks (GRNs); (ii) Heterogeneous Graph Flow Network, which incorporates the inferred GRNs into a conditional velocity field to yield state-consistent and perturbation-specific trajectories; (iii)Discriminative Optimization Strategy, which combines Mean Squared Error~(MSE) with Maximal Coding Rate Reduction~(MCR²) to promote discriminative perturbation responses and mitigate training confounders. Comprehensive evaluations on five single-cell perturbation datasets demonstrate that URFPert outperforms state-of-the-art methods in unseen perturbation regimes, providing a powerful tool for interpreting regulatory mechanisms. The project code is public via https://github.com/Jiniretttt/URFPert.

IJCAI 2026

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

Zhikang Shen, Jianrong Lu, Haiyuan Wan, Jianhai Chen

Federated learning (FL) has emerged as a promising paradigm for adapting large language models (LLMs) to distributed data. To mitigate the high communication and memory overhead, parameter efficient techniques such as Low Rank Adaptation (LoRA) are widely adopted. However, practical deployments often exhibit rank and data heterogeneity, making direct aggregation of LoRA updates biased and unstable. Existing approaches enforce a unified rank or align heterogeneous updates into a single shared subspace, which undermines personalization and accuracy. Moreover, under differential privacy (DP), adding noise to such mixed updates could perturb client specific directions that should remain local, resulting in utility loss. To address these issues, we propose Selective Decoupled Federated LoRA (SDFLoRA), a structure aware LoRA framework that decouples each client update into a shared component for updating and a private component that preserves client specific semantics. Subspace alignment and aggregation are applied selectively to the shared module, while private modules remain local. We further design low rank re-compression to maintain a fixed rank budget for the shared module. This structure supports privacy aware optimization by injecting DP noise exclusively into the shared module. Experiments on multiple benchmarks demonstrate that SDFLoRA beats federated LoRA baselines and exhibits strong robustness under privacy constraints.

IJCAI 2026

Distributed Learning with Adversarial Gradient Perturbations

Nawapon Sangsiri, Yufei Tao

Privacy concerns in distributed learning often lead clients to return intentionally altered gradient information. We consider the problem of learning convex and L-smooth functions under adversarial gradient perturbation, where a client's gradient reply to a server query can deviate arbitrarily from the true gradient subject to a distance bound. Our study focuses on two fundamental questions: (i) what is the smallest achievable sub-optimality gap (i.e., excess error in optimization) under such responses, and (ii) how many queries are sufficient to guarantee a given sub-optimality gap? We establish tight feasibility thresholds on the sub-optimality gap and provide algorithms that achieve these thresholds with provable query complexity guarantees.

IJCAI 2026

How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?

Tatsuya Sagawa, Ryosuke Kojima

Chemical Language Models (CLMs) pre-trained on large-scale molecular data are widely used for molecular property prediction. However, the common belief that increasing training resources, such as model size, dataset size, and training compute, improves both pre-training loss and downstream task performance has not been systematically validated in the chemical domain. In this work, we evaluate this assumption by pre-training CLMs while scaling training resources and measuring transfer performance across diverse molecular property prediction (MPP) tasks. We find that while pre-training loss consistently decreases with increased training resources, downstream task performance shows limited improvement. Moreover, alternative metrics based on the Hessian or loss landscape also fail to estimate downstream performance in CLMs. We further identify conditions under which downstream performance saturates or degrades despite continued improvements in pre-training metrics, and analyze the underlying task-dependent failure modes through parameter space visualizations. These results expose a gap between pre-training-based evaluation and downstream performance, and emphasize the need for model selection and evaluation strategies that explicitly account for downstream task characteristics. The code is available at https://github.com/sagawatatsuya/MolScaleTransfer.

IJCAI 2026

Can Stationary Distributions of Scale-Invariant Neural Networks Be Described by the Thermodynamics of an Ideal Gas?

Ildus Sadrtdinov, Ekaterina Lobacheva, Ivan Klimov, Mikhail Burtsev, Mikhail I. Katsnelson, Dmitry Vetrov

Understanding the training dynamics of deep neural networks remains a major open problem, with physics-inspired approaches offering promising insights. Building on this perspective, we develop a thermodynamic framework to describe the stationary distributions of stochastic gradient descent (SGD) with weight decay for scale-invariant neural networks, a setting that both reflects practical architectures with normalization layers and permits theoretical analysis. We establish analogies between training hyperparameters (e.g., learning rate, weight decay) and thermodynamic variables such as temperature, pressure, and volume. Starting with a simplified isotropic noise model, we uncover a close correspondence between SGD dynamics and ideal gas behavior, validated through theory and simulation. Extending to training of neural networks, we show that key predictions of the framework, including the behavior of stationary entropy, align closely with experimental observations. This framework provides a principled foundation for interpreting training dynamics and may guide future work on hyperparameter tuning and the design of learning rate schedulers.

IJCAI 2026

ProCURE: Addressing the Programming Concept Understanding Gap for Code Generation in LLMs via Concept-Aware Consistency Learning

Xiaoning Ren, Qiang Hu, Wei Ma, Chongyang Liu, Yan Li, Yao Zhang, Lingxiao Jiang, Yongqiang Lyu, Yinxing Xue

Although Large Language Models (LLMs) excel at code generation, recent research reveals that they exhibit an insufficient grasp of core programming concepts, such as data flow and control flow. This limitation undermines their robustness when encountering variations in these concepts in practice; however, effective solutions that explicitly target this gap remain limited. To address this challenge, we propose ProCURE, a concept-aware consistency learning framework designed to enhance LLMs’ understanding of programming concepts. Specifically, ProCURE first performs automated concept-oriented code augmentation to construct a concept-aligned dataset covering representative programming concepts. It then conducts concept-aware fine-tuning, encouraging the model to capture fine-grained concept variations and learn appropriate generation behaviors under such variations via a novel concept-sensitive consistency loss. To quantify programming concept understanding, we introduce the Concept Consistency Score (CCScore), defined as the proportion of correct generations preserved under concept variations. A higher CCScore indicates a more profound understanding of programming concepts. We evaluate ProCURE on four open-source LLMs across three widely used code generation benchmarks. Experimental results show that ProCURE improves CCScore by an average of 17.9 points, demonstrating its effectiveness in addressing the programming concept understanding gap.

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