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IJCAI 2026

Instance-Aligned Semantic Reconstruction for Incomplete Multi-View Clustering

Weiqing Yan, Yongteng Du, Peng Song, Chang Tang

Incomplete multi-view clustering (IMVC) aims to exploit complementary information from multiple views with missing observations. Recent diffusion-based approaches have shown promise for view completion; however, they often fail to capture instance-aligned global correlations across views and suffer from inefficient inference and loosely coupled optimization. In this paper, we propose IASR, an Instance-Aligned Semantic Reconstruction framework for IMVC. IASR formulates missing-view recovery as a cross-view token alignment generator, in which noisy targets, observed views, and timestep embeddings are jointly represented as tokens and interact to capture long-range cross-view dependencies throughout the denoising trajectory. To stabilize representation learning under generative noise, we further introduce a stable–active dual-encoder representation architecture with a generative-adaptive contrastive learning strategy that tightly couples view completion and clustering. Extensive experiments on eight benchmark datasets demonstrate that IASR consistently outperforms state-of-the-art IMVC methods, especially under high missing-rate settings, while achieving improved inference efficiency.

IJCAI 2026

Reexamining the Exploration–Exploitation Dilemma from an Entropy-Driven Perspective

Renye Yan, Yaozhong Gan, Jikang Cheng, Yi Sun, Zongwei Wang, Ling Liang, Yimao Cai

Achieving an optimal balance between exploration and exploitation remains a fundamental challenge in reinforcement learning. This work revisits the exploration-exploitation dilemma through the lens of entropy, offering a novel perspective on this enduring problem. It establishes a theoretical connection between policy's entropy and exploratory behavior, using entropy as a rational measure to quantify the exploration-exploitation trade-off. Theoretical analyses demonstrate that a modified Bellman equation, augmented with a novelty-seeking term, ensures appropriate entropy adjustment and guarantees its globally monotonic decay. The derived policy optimization process inherently accommodates all three regimes of the exploration-exploitation spectrum, enabling a principled transition from exploration to exploitation. Building on these theoretical insights, this work introduces AdaZero, an adaptive deep architecture that dynamically balances exploration and exploitation. Extensive empirical evaluations highlight AdaZero's robust performance and validate the theory's feasibility.

IJCAI 2026

Active Diffusion-Based Inference for Ill-Posed Inverse Problems Under Incomplete Priors

Jitao Xu, Nobuo Sato, Yaohang Li

Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is inherently challenging due to nonlinearity, noise, and ill-posedness. In this paper, we propose an active diffusion-based inverse problem solver. A diffusion model is trained to learn the mapping between the parameter space and the observable space. By iteratively detecting and correcting model misspecification through posterior uncertainty, the method discovers and learns the correct region of parameter space, even when initial training bounds exclude the true parameters. This provides a principled, Bayesian justification for adaptive domain augmentation and ensures robust inference for inverse problems under incomplete prior knowledge. We demonstrate the effectiveness of our inverse solver for a toy inverse problem with infinite solutions, and for the parameterization of the quantum correlation functions to event observables in a Quantum Chromodynamics analysis of nucleon structure.

IJCAI 2026

VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation

Changhua Xu, En Yu, Junyu Xuan, Jie Lu

Vision-Language-Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations remains unreliable. While fine-tuned VLA policies often produce semantically plausible trajectories, failures often arise from unresolved geometric ambiguities, where near-miss actions lead to divergent execution outcomes under limited supervision. We study few-shot VLA adaptation from a generation-selection perspective and propose a novel framework, VGAS (Value-Guided Action-chunk Selection). It performs inference-time best-of-N selection to identify action chunks that are both semantically faithful and geometrically precise. Specifically, VGAS employs a finetuned VLA as a high-recall proposal generator and introduces Q-Chunk-Former, a geometrically grounded Transformer critic to resolve fine-grained geometric ambiguities. In addition, we propose Explicit Geometric Regularization (EGR), which shapes a discriminative value landscape to preserve action ranking resolution among near-miss candidates while mitigating value instability under scarce supervision. Experiments and theoretical analysis demonstrate that VGAS consistently improves success rates and robustness under limited demonstrations and distribution shifts. Our code is available at https://github.com/Jyugo-15/VGAS

IJCAI 2026

SplitScaling: Adaptive Scaling for Disaggregated LLM Serving Against Traffic Bursts via DRL

Wei Xiao, Xuefeng Huang, Weijia Shi, Baokang Zhao

The disaggregated Prefill-Decode (PD) architecture has emerged as a prominent paradigm for efficient Large Language Model inference serving. However, resource management remains a critical challenge, particularly under the dual burstiness of real-world scenarios—characterized by volatile fluctuations in both request arrival rates and Prompt-to-Response ratios. Existing rule-based heuristics often fail to accurately identify system bottlenecks, leading to severe resource misallocation and Service Level Objective (SLO) violations. To address this, we propose a Deep Reinforcement Learning-based auto-scaling framework tailored for the PD architecture. By modeling the resource allocation problem as a Markov Decision Process, our framework enables the agent to capture non-linear load dynamics, thereby achieving decoupled and precise scaling for prefill and decode pools. Furthermore, to mitigate Head-of-Line blocking caused by scaling latency, we design an immediate rescheduling mechanism that migrates queued tasks to newly ready nodes in real-time. Experimental results driven by Azure bursty load traces demonstrate that our framework significantly reduces computational costs by 25.2% and 28.3% compared to the Static configuration and HeteroScale, respectively, while strictly adhering to SLOs.

IJCAI 2026

LECDPR:LLM Enhancement and Concept-Document Interactive Modeling for Prerequisite Relation Prediction

Kui Xiao, Lele Zheng, Xiaoxue He, Miao Zhang, Zhifang Huang, Yan Zhang

Accurate prediction of prerequisite relations among concepts is important for course planning and intelligent tutoring systems. Existing text-based methods are frequently contaminated by noise such as redundant phrasing, ambiguous sentences, and domain-specific colloquialisms. Moreover, previous methods based on graph structures may have failed to capture the complex interactions between concepts and documents, an information gap exists between the two approaches. Therefore, we propose an LLM Enhancement and Concept-Document Interactive Modeling for Prerequisite Relation Prediction (LECDPR) model. Concretely, LECDPR elicits an LLM via two-stage prompting to distill concept descriptions and contextual evidence from documents, resulting in semantic space embeddings of concepts. Then, the complex interactive relations between concepts and documents are modeled through three propagation mechanisms to produce relational space embeddings, aligns the two embeddings through random masking and contrastive learning, and adaptively fuses the resulting embeddings. Finally, the fused concept representations are used for more accurate prerequisite relation prediction. Extensive experiments on the public UCD, Lecture Bank, and MOOC datasets show that LECDPR outperforms ten baselines on ACC, AUC, and F1. Our code is available at https://github.com/lecdpr/LECDPR.

IJCAI 2026

Accelerated Distributed Riemannian Optimization Algorithms with Random Shuffling

Wenhan Xian, Heng Huang

Riemannian optimization has attracted increasing attention recently as it is related to a variety of machine learning problems, including principal component analysis, dictionary learning, and mixture modeling. To tackle modern large-scale machine learning tasks, distributed learning is usually considered as an effective solution that trains a global model over multiple worker nodes collaboratively to enhance the computational power. Centralized learning and decentralized learning are two types of distributed learning. Centralized learning employs a central parameter server to coordinate the training process while in decentralized learning, each worker only communicates with its peer neighbors. In this paper, we propose accelerated distributed Riemannian stochastic gradient descent algorithms with random shuffling in the cases of both centralized and decentralized learning. We improve the stochastic first-order oracle complexity of the Riemannian SGD from to where is the size of training data. We conduct an experiment of the leading eigenvector problem under the condition of centralized learning and decentralized learning to validate the performance of our methods.

IJCAI 2026

Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment

Guanmeng Xian, Ning Yang, Philip S. Yu

Adversarial training is effective on balanced datasets, but its robustness degrades under long-tailed class distributions, where tail classes suffer high robust error and unstable decision boundaries. We propose \emph{Manifold-Constrained Adversarial Training (MCAT)}, a unified framework that enforces the semantic validity of adversarial examples by penalizing deviations from class-conditional manifolds in feature space, while promoting balanced geometric separation across classes via an ETF-inspired regularization. We provide theoretical results that link geometric separation to lower bounds on adversarially robust margins, and show that manifold-constrained adversarial risk upper-bounds robust risk on high-density semantic regions. Extensive experiments on standard long-tailed benchmarks demonstrate consistent improvements in overall, balanced, and tail-class adversarial robustness. The codes and appendix are available on https://github.com/yneversky/MCAT.

IJCAI 2026

Progressive Subexpression Reuse in Symbolic Regression: Insights from RL-based Search and a Genetic Programming Realization

Xiangdong Wu, Wenjun Wu, Bingrun Chen, Junle Wang, Zhaoxin Fan, Haoyi Zhou, Pengju Zhang, Rongye Shi

Symbolic regression (SR) aims to recover compact and interpretable mathematical expressions from data. Genetic programming (GP) directly searches over symbolic structures, but its population dynamics can make it difficult to reliably preserve and accumulate useful subexpressions. In contrast, reinforcement learning (RL)-based SR has shown strong empirical performance, suggesting that learned sampling dynamics may capture useful regularities in symbolic search. Motivated by this contrast, we analyze expressions sampled during RL training and identify a recurring pattern, termed progressive subexpression reuse, where useful simple subexpressions emerge early, become increasingly frequent, and support the formation of more complex structures. Based on this observation, we propose Reinforcement Genetic Programming (RGP), a purely GP-based and non-RL framework that explicitly realizes a stage-wise retention--reintroduction loop through a dynamically maintained subexpression pool and pool-guided population initialization. Experiments on standard SR benchmarks show that RGP matches or outperforms strong baselines without RL policy training, suggesting that progressive subexpression reuse is an effective mechanism for SR search. We release our code at https://github.com/wuxiangdong586-max/RGP.

IJCAI 2026

FedEvoQ: Evolutionary Diversity Probing for Data-Quality-Aware Federated Aggregation

Leming Wu, Yaochu Jin, Han Yu, Riquan Zhang

Federated learning (FL) enables collaborative training of deep models while keeping raw data on local devices. A critical factor affecting the global performance is server-side aggregation. The most common strategy weights client updates by the amount of local data, which is often unreliable as the server cannot inspect data. More importantly, it ignores substantial variations in data quality (e.g., label noise) across heterogeneous clients. To address this issue, we propose a data-Quality-aware aggregation framework by introducing an Evolutionary-computation-inspired design into Federated learning (FedEvoQ), with a lightweight dual-branch architecture. Each client trains a main-task branch as usual, while a frozen auxiliary branch (EvoQ) performs evolutionary-style controlled sampling with crossover and mutation at the input and representation level to probe data diversity and reliability. EvoQ outputs a compact quality score that jointly reflects learnability (loss-based signal), geometric consistency (stability under perturbations), representation diversity, and out-of-distribution (OOD) deviation, enabling the server to compute quality-only aggregation weights. Extensive experiments on CIFAR10, CIFAR100, and PathMNIST under noisy and Non-independent and identically distributed (Non-IID) settings demonstrate that FedEvoQ consistently outperforms 10 state-of-the-art (SOTA) federated aggregation methods, improving the average test accuracy of the global model by over 2% with modest overhead.

IJCAI 2026

A Fast and Unified Partial Dependence Plot Algorithm

Ron Wettenstein, Alexander Nadel, Udi Boker

Partial Dependence Plots (PDPs) visualize how changes in a single feature affect the average model prediction. They are widely used in practice to interpret decision tree ensembles and other machine learning models. Joint-PDPs extend this idea to pairs of features, revealing their combined effect. Partial Dependence Interaction Values (PDIVs) measure feature interactions. The Any-Order-PDIVs task computes these interactions for every feature subset across all rows of the dataset. We introduce WOODELF++, a unified and efficient approach for computing all these useful explainability tools on decision tree ensembles, building on WOODELF, an algorithm for efficient SHAP computation. By deriving suitable metrics over pseudo-Boolean functions, WOODELF++ can compute PDPs (exact and approximate), Joint-PDPs, and Any-Order-PDIVs in a unified framework. Our method delivers substantial complexity improvements over the state of the art, including an exponential gain for Any-Order-PDIVs. Additionally, we introduce and efficiently compute Full PDPs, which leverage the model’s split thresholds to faithfully capture its behavior across all possible feature values. WOODELF++ is implemented in pure Python and supports GPU acceleration. On a dataset with 400,000 rows, WOODELF++ computes PDP and Joint-PDP up to 6x faster than the state of the art and up to five orders of magnitude faster than scikit-learn. For Any-Order-PDIVs, the gap is even larger: WOODELF++ computes all interaction values in 5 minutes, while the state of the art is estimated to require over 1,000,000 years.

IJCAI 2026

Learning to Sparsify Stochastic Linear Bandits

Zhengmiao Wang, Ming Chi, Zhi-Wei Liu, Lintao Ye, Carla Fabiana Chiasserini

This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dimensional space subject to a sparsity constraint on the number of nonzero elements in the action vector. The key challenge lies in minimizing cumulative regret while tackling the potential NP-hardness of finding optimal sparse actions due to the inherent combinatorial structure of the problem. We propose an adaptively phased exploration and exploitation algorithmic framework, utilizing ordinary least squares for parameter learning and specialized subroutines for sparse action selection. When the action set is a Euclidean ball, optimal sparse actions can be efficiently computed, enabling us to establish a Õ(d √T) regret, where d is the dimension of the action vector and T is the time horizon length. For general convex and compact action sets where finding optimal sparse actions is intractable, we employ a greedy subroutine. For general strongly convex action sets, we derive a Õ(d √T) α-regret; for general compact sets lacking strong convexity, we establish a Õ(d T^(2/3)) α-regret, where α pertains to the approximation ratio of the greedy algorithm. Finally, we validate the performance of our algorithms using extensive experiments.

IJCAI 2026

Perturbation Matters in Time Series Forecasting: A Wave-attention-aware Transformer Method

Yiming Wang, Yiqing Su, Ximing Li, Changchun Li, Bing Wang

Time series forecasting (TSF) refers to a fundamental task of predicting future sequential data based on historical observations. One representative category of TSF methods is transformer-based approaches, which translate time series into token sequences (i.e., as raw texts) before applying well-established transformers. In this paper, we conducted extensive preliminary experiments to evaluate their stability against various noises, and we empirically observed an interesting phenomenon: applying noises with appropriate levels to training time series can promote the predictive accuracy of transformer-based TSF methods. We analyze this phenomenon from the perspective of token attention and find one basic reason is that applying noises with appropriate levels can lead to token attention weights wavy, which is more consistent to the basic characteristic of time series data. Motivated by this phenomenon and analysis, we apply perturbations and propose a sub-objective wrt, perturbations constraining token attention weights to be wavy. Accordingly, we propose a novel TSF method, namely Wave-Attention-aware TransformER (WATER). We empirically evaluate \baby across the commonly used benchmark datasets, and experimental results indicate that it consistently outperforms the existing TSF methods.

IJCAI 2026

Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization

Yifan Wang, Lifeng Shen, Shuyin Xia, Yi Wang

Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency. Similarity-based methods often suffer from quadratic complexity caused by pairwise distance computations, while deep learning–based approaches typically rely on costly iterative training and a large number of trainable parameters. In this paper, we propose MSRGC-Net, an efficient time-series clustering framework that integrates multiscale reservoir computing, granular-ball-based anchoring graph construction, and consensus learning. MSRGC-Net adopts a training-free reservoir computing paradigm to extract multiscale temporal representations from raw time series without backpropagation, significantly reducing computational overhead. To capture the intrinsic structure of the resulting representations, granular-ball computing is employed to adaptively model data distributions via density-consistent regions, yielding compact and robust anchor graph representations. Furthermore, a consensus-based anchoring graph optimization strategy is introduced to effectively align multiscale reservoir representations and integrate complementary information across temporal scales. Extensive experiments on widely used univariate and multivariate benchmark datasets demonstrate that MSRGC-Net consistently outperforms state-of-the-art methods in clustering performance while maintaining superior computational efficiency.

IJCAI 2026

MarCon: Max-Margin Contrastive Learning for Imbalanced Domain Adaptation Semantic Segmentation

Yibo Wang, Ruikang Xu, Guangcheng Zhu, Cheng Peng, Haobo Wang, Runze Wu, Minmin Lin, Changjie Fan

Unsupervised Domain Adaptation for Semantic Segmentation (UDA-SS) has seen significant progress in recent years. Existing UDA-SS approaches mostly adopt a pseudo-labeling schema to adapt model in the target domain, but they often overlook the inherent long-tailed data distribution in segmentation. We find that such scarce tail samples can lead to representation collapse for tail classes and further hinder the quality of pseudo-labels. To address these challenges, we propose MarCon, a framework that mitigates the long-tailed problem from both feature representation and pseudo-labeling perspectives. Specifically, to explicitly learn a Maximum-Margin Distribution, we derive a reformulated pixel-level contrastive learning objective by modeling feature distributions with the von Mises-Fisher (vMF) distribution. It enforces strict margins to enhance intra-class compactness and inter-class separability, preventing tail classes from being overwhelmed by head classes. Furthermore, to mitigate label noise, we introduce a Reliability-aware Filter (RaF) based on the vMF-derived metrics, which performs adaptive class-wise pixel reliability assessment to identify unreliable pixels and attenuate their contribution to model training, thereby mitigating confirmation bias. Extensive experiments on GTA → Cityscapes and SYNTHIA → Cityscapes demonstrate that MarCon consistently outperforms current leading methods across various transformer-based architectures.

IJCAI 2026

Unlocking More Granular Control of Memory-Efficient LLM Finetuning

Yezhen Wang, Zhouhao Yang, Fanyi Pu, Kenji Kawaguchi

Low-rank gradient projection (LoRP) has recently emerged as a memory-efficient alternative to low-rank adapters (LoRA) for finetuning large language models. Existing LoRP methods, however, implicitly fix the projection unit to a single gradient row, leaving the effect of grouping multiple rows (or subdividing a row) largely unexplored. In this work, we systematically investigate the impact of the projection unit on LoRP methods. Specifically, we extend existing LoRP approaches by introducing an additional degree of freedom, projection granularity, beyond the traditional rank hyperparameter. This enables a framework capable of performing Various-grained Low-Rank Projection of gradients, which we term VLoRP. Using VLoRP, we observe that, under an identical memory budget, fine-grained projections consistently deliver superior performance. Moreover, VLoRP requires no extra computation and minimal code changes, effectively providing a no-cost accuracy boost to LoRP. Finally, we provide convergence analysis on VLoRP with either SGD or an Adam-based memory-efficient optimizer, and extensive experiments are conducted to validate our findings, covering tasks such as Commonsense Reasoning, MMLU, and GSM8K

IJCAI 2026

QiMeng-EvoPartition: Rethinking the Impact of Partitioning for Automated Pipeline Design

Qicheng Wang, Rui Zhang, Shuyao Cheng, Chongxiao Li, Pengwei Jin, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

As a key technique for improving throughput by increasing clock frequency and reducing Cycles per Instruction (CPI), pipeline design increasingly relies on automated methods with the growing scale of modern circuits. However, existing automated pipelining methods often decouple partitioning from CPI optimization, implicitly assuming that partitioning affects only clock frequency. We observe that different partitioning strategies significantly alter the pipeline stage differences between logic gates, thereby impacting the average number of stall cycles and ultimately affecting CPI. Thus, automated pipeline partitioning should jointly reduce CPI and improve clock frequency while ensuring functional correctness, resulting in a multi-objective optimization problem. To address this issue, we propose EvoPartition, an evolutionary partitioning framework for automated pipeline design. Specifically, EvoPartition first inserts virtual nodes to guarantee functional correctness. Then, through iterative evolution for reducing CPI and improving clock frequency, pipeline stage assignments of all nodes are determined. Experimental results on ISCAS85 and EPFL show that EvoPartition achieves a 7.34% CPI optimization by reducing stage differences by 16.23%, and further improves throughput by 7.22% compared to the state-of-the-art.

IJCAI 2026

Beyond Client Clustering: Fine-Grained Preference Alignment in Federated RLHF via Self-Evolving Routing

Ke Wang, Shaojing Fu, Yuchuan Luo, Guilin Deng, Silong Chen, Zheng Yuan, Lin Liu

Federated Reinforcement Learning from Human Feedback (RLHF) enables the collaborative alignment of Large Language Models (LLMs) while preserving privacy, yet it faces critical bottlenecks arising from data heterogeneity. Existing approaches typically rely on rigid client-level clustering, which overlooks intra-client heterogeneity and fails to adapt to the multifaceted needs of individual users. To address this, we propose FedPrism, a novel framework that shifts alignment granularity from the coarse client level to the precise instance level. Similar to an optical prism dispersing mixed light into distinct spectral components, FedPrism decomposes complex, intra-client heterogeneous data streams by dynamically routing individual samples to specialized experts based on semantic features. Crucially, we devise a Posterior-Guided Performance Distillation (PGPD) mechanism that leverages experts' actual training loss as self-supervision to autonomously refine routing policies without explicit labels. Extensive experiments show that FedPrism not only establishes new state-of-the-art results but also effectively mitigates the negative transfer prevalent in non-IID settings, ensuring superior alignment fidelity.

IJCAI 2026

Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering Under Non-IID Data

Jiazhen Wang, Xinyue Chen, Shuaiyu Liu, Zican He, Yazhou Ren, Yi Wang, Shuyin Xia

Federated multi-view clustering (FedMVC) has been widely used to discover latent structures in distributed multi-view data, yet most methods still rely on the restrictive assumption of independent and identically distributed (IID) data. In practice, non-IID distributions with partial and imbalanced categories cause clients to learn biased and local representations, leading to model bias and unstable federated training performance. To address these issues, we propose a FedMVC framework called Bridging Inter-View and Client Heterogeneity: Federated Multi-View Clustering under Non-IID Data (H²-FedMVC), which tackles both inter-view and client heterogeneity in mixed-view settings. We propose a leader-guided learning mechanism to address inter-view heterogeneity by enhancing complementary and discriminative feature learning, and an expert adaptive aggregation strategy to handle client heterogeneity by prioritizing well-matched experts in global updates. Theoretical analysis and experiments demonstrate superior performance in heterogeneous mixed-view FedMVC settings, with code provided at https://github.com/JiazhenWang-0522/H2-FedMVC.

IJCAI 2026

Multi-View Alignment and Denoising via Center-Guided Spectral Diffusion

Jiayuan Wang, Jie Lian, Yongquan Shi, Jielong Lu, Zhiyuan Lai, Shiping Wang

Multi-view learning aims to enhance performance by integrating information from multiple sources. While different views offer complementary perspectives, extracting consistent and discriminative representations remains a significant challenge due to discrepancies in representation and presence of noise. Existing methods typically separate the denoising and alignment processes, mapping the denoised heterogeneous views to a shared subspace for alignment. This means that noise may propagate or even amplify during the alignment stage, ultimately leading to suboptimal solutions. To address this issue, we propose center-guided spectral diffusion, which replaces traditional alignment with generative modeling. This method avoids noise propagation in multi-view alignment process and prevents multi-view features be aligned to the noise subspace during fusion process. Specifically, we first performs unconditional diffusion in both the feature space and a low-rank spectral space to learn a stable consensus centre anchor. This anchor is then used to condition a guided generative diffusion process, enabling the model to generate more consistent and realistic sample representations. By combining conditional and unconditional diffusion, the proposed method alleviates the noise amplification problem commonly found in traditional alignment methods. Experimental results show that the proposed method outperforms state-of-the-art methods on several datasets.

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