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IJCAI 2026

Towards Scalable Metaverse Systems with Social-Aware VR Displays

Shaolong Guo, Yuntao Wang, Qinnan Hu, Zhou su, Tom H. Luan

The Metaverse is envisioned to support immersive, large-scale social interactions via virtual reality (VR) displays. However, scalability remains a major bottleneck: as the number of concurrent users grows, tracking and updating display content incurs quadratic overhead, often limiting a shared virtual space to only a few dozen participants. Our key observation is that user attention in social VR is highly selective, with users primarily focusing on socially relevant peers rather than all visible users. Motivated by this observation, we propose SAGE, a social-aware graph-based VR display framework that enables personalized displays based on inferred social relevance. SAGE introduces a dual-graph learning architecture to jointly model long-term social structures and short-term spatiotemporal co-presence patterns, generating complementary interest scores for display prioritization. Based on these scores, we formulate scalable VR display support as a multi-dimensional resource allocation problem and design a lightweight coordination mechanism with provable guarantees, including incentive compatibility and individual rationality. Experiments on Metaverse datasets show that SAGE improves interaction-relevance prediction by 11.64% and increases social welfare by up to 2.4× compared to state-of-the-art schemes. It scales to support up to 1,000 concurrent users and remains robust against strategic manipulation.

IJCAI 2026

MORL-CA: Dynamic Multi-Objective Reinforcement Learning for Chlor-Alkali Process Optimization Under Time-Varying Conditions

Derun Gan, Renhao Yin, Guangzhi Qu, Feng Zhang

Chlor-alkali production is a large-scale industrial process whose operating conditions and equipment states evolve over time. Its process optimization requires ongoing trade-offs among conflicting objectives such as product yield, energy consumption, and equipment life. Existing optimization approaches are typically static and must be re-optimized after environmental changes, limiting their real-world applicability. In this work, we model the problem as a dynamic multi-objective sequential decision-making problem that continuously tracks a time-varying Pareto set under changing conditions. We propose MORL-CA, a multi-objective reinforcement learning framework that integrates offline pretraining on historical data with constrained online policy refinement. MORL-CA introduces a state-aware adaptive objective weighting mechanism within a multi-critic actor-critic architecture, enabling localized Pareto-improving policy updates while satisfying operational and safety constraints. Extensive experiments in an environment conducted from real chlor-alkali data demonstrate that MORL-CA achieves superior Pareto solution quality and smoother adaptation to dynamics compared with state-of-the-art multi-objective optimizers and MORL baselines.

IJCAI 2026

A Durable Machine Unlearning Framework to Nullify Recall of Sensitive Data on Incremental Training

Qingqing Cao, LIANG HU, Dora D. Liu, Jiaxing Miao, Jian Cao, Zhongyuan Lai, Wei Cao

The advancement of data privacy regulations has spurred the development of Machine Unlearning (MU), which is designed to remove the influence of sensitive data from a trained model and results in an unlearned model (ULM). Despite rapid progress in MU techniques, their vulnerabilities remain underexplored, which poses risks due to potential leakage of unlearned information. In realistic scenarios, ULMs always need to be incrementally trained with the newly collected data samples, which can lead to the consequences of recalling sensitive information if the new dataset contains similar or even the same unlearned samples. To address this issue, we devise a Durable Unlearning Enhancement (DUE) framework to avoid restoring unwanted sensitive information from incremental training data samples. The DUE framework has three key components that identify sensitive samples and suppress their gradients to update ULMs. Extensive experiments on state-of-the-art MU methods across multiple real-world datasets show that the proposed DUE framework can effectively nullify the recall of sensitive information after MU, and even improve the performance of ULMs. Consequently, our work establishes a new fundamental research direction in safe training against MU vulnerabilities.

IJCAI 2026

Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design

Elias Berger, Muhammad Usama, Jan Mehlstäubl, Bernhard Saske, Kristin Paetzold-Byhain

Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead of attempting to implicitly learn physical laws from data, we propose a Hybrid Agentic-Physical Architecture that embeds validated knowledge-based engineering tools directly into the decision-making loop of autonomous AI agents. In this framework, engineering design is formulated as a closed-loop, sequential decision-making process guided by explicit physical verification. Based on a load case, dedicated agents iteratively plan, generate, evaluate, and revise engineering designs using knowledge-based tools as a feedback signal. We introduce a benchmark dataset and metrics for assessing functional validity in generative CAD. Our system generates more complex and physically verified designs, with a 4.2x increase in structural complexity and improving compile rate by 3.5% compared to similar agentic methods. The codebase, prompts and dataset will be made publicly available to support reproducibility and future research.

IJCAI 2026

DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling

Carlo Malapad Acosta, Herath Mudiyanselage Viraj Vidura Herath, Jia Yu Lim, Abhishek Saha, Sanka Rasnayaka, Lucy Marshall

Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and accuracy while having the ability to process unstructured spatial domains. Given its flexible input and architecture, GNNs can be leveraged alongside physics-informed techniques with ease, significantly improving interpretability and generalizability. We introduce a novel flood GNN architecture, DUALFloodGNN, which embeds physical constraints at both global and local scales through explicit loss terms. The model jointly predicts water volume at nodes and flow along edges through a shared message-passing framework. To improve performance for autoregressive inference, model training is conducted with a multi-step loss enhanced with dynamic curriculum learning. Compared with standard GNN architectures and state-of-the-art GNN flood models, DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables (e.g., water volume, flow, and depth) while maintaining high computational efficiency. The model is open sourced at https://github.com/acostacos/dual_flood_gnn. The dataset is open sourced at https://doi.org/10.25910/9xav-0s86.

IJCAI 2026

Mining Statistically Likely k-Reachable States in Probabilistic Programs

Arnab Ray, Nitesh Trivedi, Ansuman Banerjee, Sourav Chakraborty, Arijit Ghosh, Subhajit Roy

We propose the notion of statistically likely k-step reachable set in probabilistic programs, a statistically robust notion for high-probability k-step reachable program states. We design an inductive algorithm to capture this set as a symbolic representation in propositional logic for Boolean probabilistic programs. Our methodology iteratively learns a symbolic formula for the statistically likely k-step reachable set that involves (a) learning an initial symbolic candidate via decision tree learning, (b) collecting positive and negative counterexamples via forward and backward verification checks, and (c) refining the current candidate via a sequence of prune and split moves on the decision tree. We demonstrate that the statistically likely k-step reachable set can reveal interesting properties about programs by studying probabilistic programs from the literature.

IJCAI 2026

LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy

Hamed Karimi, Vaishali Meyappan, Reza Samavi

LLMs' overconfidence, particularly when hallucinating, poses a significant challenge for the deployment of the models in safety-critical settings and makes a reliable estimation of uncertainty necessary. Existing approaches for uncertainty quantification typically prioritize lexical or probabilistic measures; however, these techniques often ignore the semantic variance of different responses with similar meaning. In this paper, we propose Adaptive Conformal Semantic Entropy (ACSE), a method for estimating prompt-level uncertainty by adaptively measuring semantic dispersion in LLMs outputs. Our uncertainty scoring function is based on clustering semantic entropy of multiple diverse responses to the same prompt. The function adaptively adjusts the uncertainty score based on semantic features of each cluster. To ensure statistical reliability of our score, we use conformal calibration to apply a decision rule to accept/abstain the prompts, providing a finite-sample, distribution-free guarantee such that the error rate among the accepted responses remains bounded by a user-specified tolerance. Our extensive experimental evaluations using different LLMs and datasets, demonstrate that our approach consistently outperforms state-of-the-art uncertainty quantification baselines using discriminative performance, acceptance rate, conformal guarantees, and probabilistic calibration indicators. As a highlight, for TriviaQA dataset, AUROC of our approach is 0.88 compared to 0.65 produced by the token entropy approach.

IJCAI 2026

Causal Newton Optimization: Online Calibration with Iterative Local Linear Modeling and Newton Updates

Daigo Fujiwara, Tomonori Izumitani, Shohei Shimizu

Optimization in industrial systems often involves calibrating from a semi-optimized state, where global exploration methods like Reinforcement Learning (RL) or Bayesian Optimization (BO) are inefficient or unsafe. We propose Causal Newton Optimization (CNO), an online algorithm that iteratively calibrates inputs under a known causal graph but unknown structural equations. CNO estimates local linear causal effects via additive interventions and employs a log-linear variance regression to robustly guide Newton-based updates. Evaluations on synthetic systems and a chemical plant simulator demonstrate that CNO achieves the best balance between objective improvement and robustness. While traditional PID control suits standard dynamical systems, CNO significantly outperforms RL and BO in complex structural causal models, providing the robust stability vital for safety-critical real-world applications.

IJCAI 2026

A Parallel Framework for the Maximum Common Induced Subgraph Problem

Jieyu Wu, Quan Zhang, Yiyuan Wang, Shiwei Pan, Jian Gao

Finding the maximum common induced subgraph (MCIS) between two graphs is a well-known NP-hard problem. While sequential MCIS algorithms have been extensively studied, parallel computing has emerged as an important direction for further performance enhancement with the advancement of computing resources. In this paper, we propose a parallel MCIS framework integrating a dynamic task decomposition method guided by search information and a novel pruning strategy based on shared information. The experimental results demonstrate that the algorithm enhanced by our framework achieves superior performance over both state-of-the-art sequential and existing parallel algorithms. Extensive results further demonstrate the wide scalability and generality of our framework, and the effectiveness of our strategies.

IJCAI 2026

Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling

Tingyang Wei, Jiao Liu, Abhishek Gupta, Chin Chun Ooi, Puay Siew Tan, Yew Soon Ong

Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as parametric expensive multi-objective optimization problems (P-EMOPs) where each task parameter defines a distinct optimization instance. Current multi-objective Bayesian optimization methods have been widely used for finding finite sets of Pareto optimal solutions for each task. However, P-EMOPs present a fundamental challenge: the continuous task parameter space can contain infinite distinct problems, each requiring separate expensive evaluations. To address this, we propose learning an inverse model to amortize the multi-objective optimization cost across the continuous task-preference space, enabling direct solution prediction for any query without the need for expensive re-evaluation. This paper introduces a novel parametric multi-objective Bayesian optimizer that learns this inverse model by alternating between (1) generative solution sampling via conditional generative models and (2) acquisition-driven search leveraging inter-task synergies. This approach enables effective optimization across multiple tasks and finally achieves direct solution prediction for unseen parameterized EMOPs without re-evaluations. We theoretically justify the faster convergence by leveraging inter-task synergies through task-aware Gaussian processes. Based on that, empirical studies in synthetic and real-world benchmarks further verify the effectiveness of the proposed parametric optimizer.

IJCAI 2026

Theoretical Analysis of Multi-Objective Evolutionary Algorithms on Integer Spaces with Local Optima

Yuetong Sun, Zeqiong Lv, Shengjie Ren, Zimin Liang, Miqing Li, Chao Qian

Multi-objective evolutionary algorithms (MOEAs) are popular tools for multi-objective optimization (MOO), and have been successfully applied to many real-world MOO problems. However, the theoretical study has lagged behind their practical success and remains largely confined to synthetic pseudo-Boolean functions. To close this gap, this paper--drawing inspiration from a class of popular continuous problems with real-world relevance--introduces a multi-objective benchmark defined on an integer space, featuring an analyzable landscape and the presence of local optima. We conduct a running time analysis on the proposed benchmark and derive several theoretical results. Specifically, we prove that a widely-studied MOEA, GSEMO, using unit-step mutation can be trapped in local optimal regions and fail to identify the Pareto front. Fortunately, we find that this difficulty can be overcome either by incorporating an ageing mechanism or using heavy-tailed mutations that allow multi-valued changes along each dimension of an individual. In addition, we demonstrate the extendability of the proposed benchmark to more complex landscapes with numerous local optima, resembling well-established problems in the field (e.g., those from the ZDT and DTLZ suites). We hope this work is a step forward for the theoretical study of MOEAs on problems that are closely related to those commonly investigated in empirical research.

IJCAI 2026

A First Runtime Analysis of Parallel Tempering in Quadratic Optimization

Tiago Paixão, Jorge Pérez Heredia, Dirk Sudholt, Andrew M. Sutton

Parallel tempering, or the replica exchange method, is a Markov Chain Monte Carlo (MCMC) sampling technique for finding low-energy states in complex landscapes by executing multiple processes at different temperatures and allowing for states to migrate between parallel processes based on the Metropolis criterion. Despite a growing interest in the technique as a randomized search heuristic, it is not clear when and why parallel tempering is effective. We conduct a first runtime analysis of the parallel tempering algorithm on a simple quadratic unconstrained binary optimization problem that induces a rugged energy landscape with tunable parameters. We prove that a single-temperature Metropolis process fails to efficiently optimize the problem at any temperature. In sharp contrast, a two-state parallel tempering approach using a low and a high temperature typically solves the problem in O(n^2 log n) iterations. The low-temperature process ensures stability, as it is unlikely to accept worsenings, whereas the high-temperature process facilitates exploration by traversing energy barriers. Improvements are discovered through exploration in the high-temperature process and transferred to the low-temperature process, where they are permanently retained. This effective interplay between processes demonstrates the power of parallel tempering and lends credence to its design.

IJCAI 2026

On the Impact of Crossover in Many-Objective Optimization: A Runtime Analysis of NSGA-III

Andre Opris

In recent years, theoretical understanding has rapidly advanced regarding how popular multi-objective evolutionary algorithms (MOEAs) can optimize many-objective problems. However, the benefits of using crossover in many-objective optimization are theoretically not understood, except for specifically designed benchmark functions tuned to particular crossover operators, and still lag significantly behind its practical use. In this paper, we build upon this line of research and present a theoretical runtime analysis of the widely used NSGA-III algorithm on the classical m-objective m-OneJumpZeroJump function (m-OJZJ for short). Our results demonstrate that NSGA-III with crossover optimizes m-OJZJ asymptotically faster than NSGA-III without crossover for any number m of objectives for huge parameter regimes. We complement our analysis by providing a lower runtime bound on 4-OJZJ when crossover is turned off.

IJCAI 2026

Finding Simple Shortest-Paths via Centroids

Carlos Linares Lopez, Ian Herman

Finding an arbitrary number of shortest paths in a graph is a fundamental problem in Graph Theory and Artificial Intelligence with numerous applications to real-world problems. While some algorithms produce paths which might contain loops, the most interesting and challenging problem variant consists of finding simple (or loopless) paths. This problem has been addressed with algorithms which always conduct an arbitrary number of Dijkstra searches. While the current state-of-the-art leverages the notion of sidetrack edge, a new development upon sidetrack edges has been recently introduced which can be used to produce non-simple paths very efficiently, centroids. In this paper, centroids are used to compute an arbitrary number of simple paths with some important benefits: the expansion of a single centroid delivers an arbitrary number of paths; only a single Dijkstra search is required to complete the task; the same algorithm can be easily coupled with heuristics that improve search efficiency. Experimental results across various domains show substantial improvements over the current state of the art, both in runtime and memory usage.

IJCAI 2026

Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity

Shishen Lin, Yixin Chen

NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for control tasks. However, standard offline optimisation searches for connection strengths directly, which can scale poorly in high-dimensional weight spaces and more difficult continuous control problems. Hybrid methods that combine neuroevolution with online learning can address this challenge, but their theoretical properties remain underexplored. This paper gives the first regret analysis for a general NeuroEvolutionary Online Learning (NEOL) framework, which decouples learning into two timescales: an outer loop for architecture search and an inner loop for online weight adaptation via reward-modulated plasticity. Under mild conditions, we prove that NEOL achieves sublinear regret. Empirically, under fixed interaction budgets on four standard control benchmarks, a NEAT-based NEOL implementation achieves higher final fitness and lower variance than pure NEAT, and is competitive with strong reinforcement learning (RL) baselines on several tasks. The results are supported by Wilcoxon rank-sum tests and ablation studies. Overall, the findings show that online plasticity can improve the sample efficiency and robustness of two-timescale neuroevolution. Code is available at https://github.com/boobaa2001/NeuroEvolution_Online_Learning_NEOL

IJCAI 2026

First Mathematical Runtime Analyses of Multi-Objective Evolutionary Algorithms for Multi-Valued Decision Variables

Mingfeng Li, Zheng Cheng, Weijie Zheng, Benjamin Doerr

Problems defined on binary decision spaces have been intensively studied in the theory of multi-objective evolutionary algorithms (MOEAs). In contrast, no mathematical runtime analyses exist so far for MOEAs dealing with decision variables that take a finite number r > 2 of values, despite the prevalence of such problems in practice. In this work, we begin to fill this research gap. We analyze how the classic SEMO algorithm with unit-strength local mutation computes the Pareto front of an r-valued counterpart of the classic ONEMINMAX benchmark. For the expected number of function evaluations until the Pareto front is covered by the population of this MOEA, we prove an upper bound of O(n²r² log n) and a near-tight lower bound of Ω(n²r(r + log n)). We can close the small remaining gap between these two bounds by considering a variant of the algorithm that accepts only strictly better solutions; for this variant, we show an upper bound of O(n²r(r + log n)), matching our lower bound, which also holds for this variant. Our results suggest that classic MOEAs encounter no significant additional difficulties when dealing with multi-valued decision variables. However, significantly more advanced tools may be required to obtain tight bounds for algorithms with more complex population dynamics.

IJCAI 2026

Taming Treewidth DP with Modulators: A General Booster for Graph Heuristics

Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen, Mingyu Guo

Treewidth is a fundamental graph invariant that quantifies how tree-like a given graph is. It is extensively used with dynamic programming to design fixed-parameter tractable algorithms for many NP-hard graph combinatorial optimization problems. However, despite broad theoretical applicability, treewidth dynamic programming (TDP) does not scale in practice beyond graphs with very small treewidth. Rather than applying TDP as a standalone technique, we demonstrate that TDP can serve as a broadly applicable enhancer for a wide range of graph combinatorial optimization algorithms. Our framework leverages the concept of treewidth modulators, which refer to vertex sets whose removal significantly reduces the treewidth. We further propose an empirically efficient procedure for generating such treewidth modulators. To enhance an algorithm ?, we use ? to heuristically make decisions on the modulators vertices, after which the remaining decisions outside the treewidth modulators become scalable for TDP. To demonstrate the general applicability of our proposed framework. We experimented with three classic graph combinatorial optimization models: Maximum Independent Set, Minimum Vertex Cover, and Max Cut. We apply TDP to enhance algorithms across diverse paradigms, including evolutionary search, greedy heuristics, and graph-neural-network-based heuristics. For all combinations of optimization models and base algorithms, TDP significantly improves performance over the original methods. In many settings, TDP-enhanced greedy heuristics are competitive with, and sometimes clearly outperform, state-of-the-art commercial solvers.

IJCAI 2026

Extending Weighted Heuristic Search to Bi-Objective Search Problems

Hans Kühn Leiva, Jorge A. Baier, Carlos Hernandez Ulloa, Oren Salzman, Ariel Felner, Sven Koenig

In heuristic search, a well-known technique to speed up search while providing a suboptimality guarantee is to multiply the heuristic function by a weight w > 1. In this paper, we study the theoretical and practical implications of using such a technique in bi-objective heuristic search, a natural academic exercise that has remained unexplored. We introduce Weighted BOA∗ϵ (WBOA∗ ϵ ), a weighted version of the BOA* algorithm, which uses two real parameters: a weight w for the heuristic and an approximation factor ϵ. Higher values of w and ϵ allow for faster computation of approximate Pareto-optimal solution sets. We prove that WBOA∗ ϵ returns a representative solution set containing (w − 1, ϵ)-approximate solutions. We empirically compare it to A*pex, the state-of-the-art approximate bi-objective search algorithm. We find that WBOA∗ ϵ is competitive with A*pex: when using perfect heuristic functions, in road maps WBOA∗ϵ is faster for higher approximation factors, while in grid maps WBOA∗ϵ dominates A*pex. With imperfect heuristic functions, WBOA∗ϵ performs better than A*pex for lower approximation factors, while the opposite is true for larger approximation factors.

IJCAI 2026

One for Exploration and Another for Exploitation: A Dual-Population MOEA Framework with Provable Benefits

Chenglin Jiang, Shengjie Ren, Zimin Liang, Miqing Li, Chao Qian

Evolutionary Algorithms (EAs) are currently the most popular tool for solving multi-objective optimization problems. Balancing exploration and exploitation is fundamental to the performance of Multi-Objective EAs (MOEAs). Achieving this requires maintaining a set of high-quality solutions for effective exploitation while simultaneously enabling exploration of diverse regions of the search space. In MOEAs, these two roles are typically fulfilled by a single evolutionary population. However, high-quality solutions that are promising for exploitation may lack the potential to guide the search toward different regions of the search space. Conversely, solutions with high exploratory potential that can lead the search toward more promising regions may themselves be of low quality. To address this issue, this paper proposes a dual-population MOEA with Exploration and Exploitation Decoupled (MOEA/EED): the exploration population uses a simple aging mechanism, while the exploitation population preserves the currently optimal solutions. We theoretically prove the benefits of MOEA/EED, i.e., it achieves the upper bounds, O(n^(k+1) · min{1, (e ln(3e) / k)^k}) and O((5e ln(3e))^(n/5)), on the expected running time for solving two commonly studied bi-objective problems OneJumpZeroJump and bi-objective RealRoyalRoad, which are significantly better (even exponentially smaller) than that of the popular SMS-EMOA, NSGA-II, and their non-elitist variants. Lastly, we empirically show its effectiveness on a wide range of practical optimization problems.

IJCAI 2026

Speeding Up the NSGA-II via Dynamic Population Sizes

Benjamin Doerr, Martin S. Krejca, Simon Wietheger

Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision. In this work, we aim to overcome these difficulties by proposing the dynamic NSGA-II, a variant of the well-known NSGA-II that starts with a small initial population and doubles it after a user-specified number ? of function evaluations, up to a maximum size of ?ₘₐₓ. We prove that the dynamic NSGA-II with optimal parameters computes the Pareto front of the OneMinMax benchmark of size ? with high probability in O(? log² ?) function evaluations, which is considerably faster than the Θ(?² log ?) runtime of the static NSGA-II with optimal parameters. For the OneJumpZeroJump benchmark with gap size ?, we show a runtime of O(?^? log² ?), improving upon the known runtime of Θ(?^(? + 1)). We also propose a variant that uses the initial population size for a longer period and achieves slightly better performance. Finally, we show that a simple concurrent-run strategy turns our dynamic NSGA-II variants into parameter-less algorithms that exceed the above runtimes only by a logarithmic factor and hence still outperform the static NSGA-II by a factor of ͠Ω(?).

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