GenAI4ELab/papercli-papers huggingface dataset:
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models
Authors: Xueqi Ma; Xingjun Ma; Sarah Monazam Erfani; Danilo Mandic; James Bailey
Venue: AAAI 2026 (main)

Abstract: Developing open-set classification methods capable of classifying in-distribution (ID) data while detecting out-of-distribution (OOD) samples is essential for deploying graph neural networks (GNNs) in open-world scenarios. Existing methods typically treat all OOD samples as a single class, despite real-world applications—especially high-stake settings like fraud detection and medical diagnosis—demanding deeper insights into OOD samples, including their probable labels. This raises a critical question: Can OOD detection be extended to OOD classification without true label information? To answer this question, we introduce a Coarse-to-Fine open-set Classification (CFC) method that leverages large language models (LLMs) for text-attributed graphs. CFC consists of three key components: (1) A coarse classifier that utilizes LLM prompts for OOD detection and outlier label generation; (2) A GNN-based fine classifier trained with OOD samples from (1) for enhanced OOD detection and ID classification; and (3) Refined OOD classification achieved through LLM prompts and post-processed OOD labels. Unlike methods relying on synthetic or auxiliary OOD samples, CFC employs semantic OOD data-instances that are genuinely out-of-distribution based on their inherent meaning, thus improving interpretability and practical utility. CFC enhances OOD detection by 10% compared to state-of-the-art approaches on text-attributed graphs and in the text domain, while achieving up to 70% accuracy in OOD classification on graph datasets.
MTRL-CG: Multi-Task Reinforcement Learning Method with Spectral Clustering-Based Task Grouping
Authors: Wenjia Meng; Teng Zhang; Haoliang Sun; Yilong Yin
Venue: AAAI 2026 (main)

Abstract: Multi-task reinforcement learning (RL) aims to enhance agent performance across multiple tasks by enabling effective knowledge transfer.
However, these methods adopt a fully shared policy across all tasks without explicitly distinguishing between related and conflicting ones, making them suffer from negative interference issue, where updates beneficial to one task adversely affect others and lead to degraded overall performance. In this paper, we propose a multi-task reinforcement learning method with spectral clustering-based task grouping (MTRL-CG),  which leverages spectral clustering to group related tasks and separate conflicting ones, enabling group-wise policy learning to mitigate negative interference. We first quantify inter-task affinity by measuring the influence of task-specific updates on others within a shared model, and construct an affinity matrix to capture these relationships. Spectral clustering is then applied to partition tasks via spectral embedding and k-means clustering. Each task group is trained with a dedicated policy network to promote focused learning. Built upon the Soft Actor-Critic (SAC) algorithm, MTRL-CG can be readily integrated into existing SAC-based multi-task RL methods. Extensive experiments on the Meta-World benchmark demonstrate the effectiveness of the proposed MTRL-CG method.
Targeting in Multi-Criteria Decision Making
Authors: Nicolas Schwind; Patricia Everaere; Sébastien Konieczny; Emmanuel Lonca
Venue: AAAI 2026 (main)

Abstract: In this work, we introduce the notion of targeting for multi-criteria decision making. The problem involves selecting the best alternatives related to one particular alternative, called the target. We use an axiomatic approach to this problem by establishing properties that any targeting method should satisfy. We present a representation theorem and show that satisfying the main properties of targeting requires aggregating the evaluations of the alternatives related to the target. We propose various candidate targeting methods and examine the properties satisfied by each method.
Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis
Authors: Nicholas Tagliapietra; Katharina Ensinger; Christoph Zimmer; Osman Mian
Venue: AAAI 2026 (main)

Abstract: Real world systems evolve in continuous-time according to their underlying causal relationships, yet their dynamics are often unknown. Existing approaches to learning such dynamics typically either discretize time ---leading to poor performance on irregularly sampled data--- or ignore the underlying causality. We propose CADYT, a novel method for causal discovery on dynamical systems addressing both these challenges. In contrast to state-of-the-art causal discovery methods that model the problem using discrete-time Dynamic Bayesian networks, our formulation is grounded in Difference-based causal models, which allow milder assumptions for modeling the continuous nature of the system. CADYT leverages exact Gaussian Process inference for modeling the continuous-time dynamics which is more aligned with the underlying dynamical process. We propose a practical instantiation that identifies the causal structure via a greedy search guided by the Algorithmic Markov Condition and Minimum Description Length principle. Our experiments show that CADYT outperforms state-of-the-art methods on both regularly and irregularly-sampled data, discovering causal networks closer to the true underlying dynamics.
Bayesian Network Structural Consensus via Greedy Min-Cut Analysis
Authors: Pablo Torrijos; Jose M. Puerta; Juan A. Aledo; José A. Gámez
Venue: AAAI 2026 (main)

Abstract: This paper presents the Min-Cut Bayesian Network Consensus (MCBNC) algorithm, a greedy method for structural consensus of Bayesian Networks (BNs), with applications in federated learning and model aggregation. MCBNC prunes weak edges from an initial unrestricted fusion using a structural score based on min-cut analysis, integrated into a modified Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm. The score quantifies edge support across input networks and is computed using max-flow. Unlike methods with fixed treewidth bounds, MCBNC introduces a pruning threshold θ that can be selected post hoc using only structural information. Experiments on real-world BNs show that MCBNC yields sparser, more accurate consensus structures than both canonical fusion and the input networks. The method is scalable, data-agnostic, and well-suited for distributed or federated structural learning of BNs or causal discovery.
Robust Causal Discovery Under Imperfect Structural Constraints
Authors: Zidong Wang; Xi Lin; Chuchao He; Xiaoguang Gao
Venue: AAAI 2026 (main)

Abstract: Robust causal discovery from observational data under imperfect prior knowledge remains a significant and largely unresolved challenge. Existing methods typically presuppose perfect priors or can only handle specific, pre-identified error types. And their performance degrades substantially when confronted with flawed constraints of unknown location and type. This decline arises because most of them rely on inflexible and biased thresholding strategies that may conflict with the data distribution. To overcome these limitations, we propose to harmonizes knowledge and data through prior alignment and conflict resolution. First, we assess the credibility of imperfect structural constraints through a surrogate model, which then guides a sparse penalization term measuring the loss between the learned and constrained adjacency matrices.  We theoretically prove that, under ideal assumption, the knowledge-driven objective aligns with the data-driven objective. Furthermore, to resolve conflicts when this assumption is violated, we introduce a multi-task learning framework optimized via multi-gradient descent, jointly minimizing both objectives. Our proposed method is robust to both linear and nonlinear settings. Extensive experiments, conducted under diverse noise conditions and structural equation model types, demonstrate the effectiveness and efficiency of our method  under imperfect structural constraints.
Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing
Authors: Hong Xie; Haoran Gu; Yanying Huang; Tao Tan; Defu Lian
Venue: AAAI 2026 (main)

Abstract: This paper proposes a variant of multiple-play stochastic bandits tailored to resource allocation problems arising from LLM applications, edge intelligence, etc. The model is composed of finite number of arms and plays. Each arm has a stochastic number of capacities, and each unit of capacity is associated with a reward function. Each play is associated with a priority weight. When multiple plays compete for the arm capacity, the arm capacity is allocated in a larger priority weight first  manner. Instance independent and instance dependent regret lower bounds are proved, revealing the impact of model parameters on the hardness of learning the optimal allocation policy. When model parameters are given, we design an algorithm named MSB-PRS-OffOpt to locate the optimal play allocation policy with a polynomial computational complexity in the number of arms and plays. Utilizing MSB-PRS-OffOpt as a subroutine, an approximate upper confidence bound (UCB) based algorithm is designed, which has instance independent and instance dependent regret upper bounds matching the corresponding lower bound up to acceptable factors. To this end, we address nontrivial technical challenges arising from optimizing and learning under a special nonlinear combinatorial utility function induced by the prioritized resource sharing mechanism.
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
Authors: Haoyang Zheng; Hengrong Du; Ruqi Zhang; Guang Lin
Venue: AAAI 2026 (main)

Abstract: Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration, we introduce the Discrete Replica EXchangE Langevin (DREXEL) sampler and its variant with Adjusted Metropolis (DREAM). These samplers use two GDSs at different temperatures and step sizes: one focuses on local exploitation, while the other explores broader energy landscapes. When energy differences are significant, sample swaps occur, governed by a mechanism tailored for discrete sampling to ensure detailed balance. Theoretically, we prove that the proposed samplers satisfy detailed balance and converge to the target distribution under mild conditions. Experiments across 2d synthetic simulations, sampling from Ising models and restricted Boltzmann machines, and training deep energy-based models further confirm their efficiency in exploring non-convex discrete energy landscapes.
MeshA*: Efficient Path Planning with Motion Primitives
Authors: Marat Agranovskiy; Konstantin Yakovlev
Venue: AAAI 2026 (main)

Abstract: We study a path planning problem where the possible move actions are represented as a finite set of motion primitives aligned with the grid representation of the environment. That is, each primitive corresponds to a short kinodynamically-feasible motion of an agent and is represented as a sequence of the swept cells of a grid. Typically, heuristic search, i.e. A*, is conducted over the lattice induced by these primitives (lattice-based planning) to find a path. However, due to the large branching factor, such search may be inefficient in practice. To this end, we suggest a novel technique rooted in the idea of searching over the grid cells (as in vanilla A*) simultaneously fitting the possible sequences of the motion primitives into these cells. The resultant algorithm, MeshA*, provably preserves the guarantees on completeness and optimality, on the one hand, and is shown to notably outperform conventional lattice-based planning (x1.5-x2 decrease in the runtime), on the other hand.
A Fast Heuristic Search Approach for Energy-Optimal Profile Routing for Electric Vehicles
Authors: Saman Ahmadi; Mahdi Jalili
Venue: AAAI 2026 (main)

Abstract: We study the energy-optimal shortest path problem for electric vehicles (EVs) in large-scale road networks, where recuperated energy along downhill segments introduces negative energy costs. While traditional point-to-point pathfinding algorithms for EVs assume a known initial energy level, many real-world scenarios involving uncertainty in available energy require planning optimal paths for all possible initial energy levels, a task known as energy-optimal profile search. Existing solutions typically rely on specialized profile-merging procedures within a label-correcting framework that results in searching over complex profiles. In this paper, we propose a simple yet effective label-setting approach based on multi-objective A* search, which employs a novel profile dominance rule to avoid generating and handling complex profiles. We develop four variants of our method and evaluate them on real-world road networks enriched with realistic energy consumption data. Experimental results demonstrate that our energy profile A* search achieves performance comparable to energy-optimal A* with a known initial energy level.
An Adaptive Configuration-Aware Simulated Annealing for the Maximally Diverse Grouping Problem
Authors: Baiyu Chen; Canhui Luo; Junwen Ding; Qingyun Zhang; Zhouxing Su; Zhipeng Lü
Venue: AAAI 2026 (main)

Abstract: The maximally diverse grouping problem (MDGP) seeks to partition the vertices of a complete graph into a fixed number of groups under capacity constraints, maximizing the sum of edge weights within each group. MDGP is an NP-hard combinatorial optimization problem and has wide real-world applications. In this paper, we propose an adaptive configuration-aware simulated annealing (ACSA) algorithm to solve MDGP. First, ACSA adopts a relaxation-based insertion strategy, which temporarily relaxes capacity constraints to expand the neighborhood and allow effective exploration of promising regions. Second, a memory-based swap mechanism is introduced to integrate high-potential suboptimal swap moves into the conventional best-swap operation, thereby achieving a better balance between diversification and intensification of the search. Finally, ACSA employs a vertex-wise sequential coordination strategy to dynamically organize the insertion and swap moves, which enhances the search flexibility. Experiments on 500 benchmark instances demonstrate the strong competitiveness of ACSA, as it improves the best results among the state-of-the-art algorithms on 460 instances and matches them on 39 instances.
Parallelizable Riemannian Alternating Direction Method of Multipliers for Non-convex Pose Graph Optimization
Authors: Xin Chen; Chunfeng Cui; Deren Han; Liqun Qi
Venue: AAAI 2026 (main)

Abstract: Pose graph optimization (PGO) is fundamental to robot perception and navigation systems, serving as the mathematical backbone for solving simultaneous localization and mapping (SLAM). Existing solvers suffer from polynomial growth in computational complexity with graph size, hindering real-time deployment in large-scale scenarios. In this paper, by duplicating variables and introducing equality constraints, we reformulate the problem and propose a Parallelizable Riemannian Alternating Direction Method of Multipliers (PRADMM) to solve it efficiently. Compared with the state-of-the-art methods that usually exhibit polynomial time complexity growth with graph size,  PRADMM  enables efficient parallel computation across vertices regardless of graph size. Crucially, all subproblems admit closed-form solutions, ensuring  PRADMM maintains exceptionally stable performance. Furthermore, by carefully exploiting the structures of the coefficient matrices in the constraints, we establish the global convergence of PRADMM under mild conditions, enabling larger relaxation step sizes within the interval (0,2). Extensive empirical validation on two synthetic datasets and multiple real-world 3D SLAM benchmarks confirms the superior computational performance of PRADMM.
Minimum-Cost Network Flow with Dual Predictions
Authors: Zhiyang Chen; Hailong Yao; Xia Yin
Venue: AAAI 2026 (main)

Abstract: Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely ε-relaxation. We provide time complexity bounds in terms of the infinity norm prediction error, which is both consistent and robust. We also prove sample complexity bounds for PAC-learning the prediction. We empirically validate our theoretical results on two applications of minimum-cost flow, i.e., traffic networks and chip escape routing, in which we learn a fixed prediction, and a feature-based neural network model to infer the prediction, respectively. Experimental results illustrate 12.74× and 1.64× average speedup on two applications.
Parametric Pareto Set Learning for Expensive Multi-Objective Optimization
Authors: Ji Cheng; Bo Xue; Qingfu Zhang
Venue: AAAI 2026 (main)

Abstract: Parametric multi-objective optimization (PMO) addresses the challenge of solving an infinite family of multi-objective optimization problems, where optimal solutions must adapt to varying parameters. Traditional methods require re-execution for each parameter configuration, leading to prohibitive costs when objective evaluations are computationally expensive. To address this issue, we propose Parametric Pareto Set Learning with multi-objective Bayesian Optimization (PPSL-MOBO), a novel framework that learns a unified mapping from both preferences and parameters to Pareto-optimal solutions. PPSL-MOBO leverages a hypernetwork with Low-Rank Adaptation (LoRA) to efficiently capture parametric variations, while integrating Gaussian process surrogates and hypervolume-based acquisition to minimize expensive function evaluations. We demonstrate PPSL-MOBO's effectiveness on two challenging applications: multi-objective optimization with shared components, where certain design variables must be identical across solution families due to modular constraints, and dynamic multi-objective optimization, where objectives evolve over time. Unlike existing methods that cannot directly solve PMO problems in a unified manner, PPSL-MOBO learns a single model that generalizes across the entire parameter space. By enabling instant inference of Pareto sets for new parameter values without retraining, PPSL-BO provides an efficient solution for expensive PMO problems.
Massively Parallel Proof-Number Search for Impartial Games and Beyond
Authors: Tomáš Čížek; Martin Balko; Martin Schmid
Venue: AAAI 2026 (main)

Abstract: Proof-Number Search is a best-first search algorithm with many successful applications, especially in game solving. As large-scale computing clusters become increasingly accessible, parallelization is a natural way to accelerate computation. However, existing parallel versions of Proof-Number Search are known to scale poorly on many CPU cores. Using two parallelized levels and shared information among workers, we present the first massively parallel version of Proof-Number Search that scales efficiently even on a large number of CPUs. We apply our solver, enhanced with Grundy numbers for reducing game trees of impartial games, to the Sprouts game, a case study motivated by the long-standing Sprouts Conjecture. Our algorithm achieves 332.9x speedup on 1024 cores, significantly improving previous parallelizations and outperforming the state-of-the-art Sprouts solver GLOP by four orders of magnitude in runtime while generating proofs 1,000x more complex. Despite exponential growth in game tree size, our solver verified the Sprouts Conjecture for 42 new positions, nearly doubling the number of known outcomes.
Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
Authors: Maxim Divilkovskiy; Alexander Gasnikov
Venue: AAAI 2026 (main)

Abstract: We study non-smooth stochastic decentralized optimization problems over time-varying networks, where objective functions are distributed across nodes and network connections may intermittently appear or break. Specifically, we consider two settings: (i) stochastic non-smooth (strongly) convex optimization, and (ii) stochastic non-smooth (strongly) convex–(strongly) concave saddle point optimization. Convex problems of this type commonly arise in deep neural network training, while saddle point problems are central to machine learning tasks such as the training of generative adversarial networks (GANs). Prior works have primarily focused on the smooth setting, or time-invariant network scenarios. We extend the existing theory to the more general non-smooth and stochastic setting over time-varying networks and saddle point problems. Our analysis establishes upper bounds on both the number of stochastic oracle calls and communication rounds, matching lower bounds for both convex and saddle point optimization problems.
Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer
Authors: Benjamin Doerr; Martin S. Krejca; Milan Stanković
Venue: AAAI 2026 (main)

Abstract: Together with the NSGA-II, the SPEA2 is one of the most widely used domination-based multi-objective evolutionary algorithms. For both algorithms, the known runtime guarantees are linear in the population size; for the NSGA-II, matching lower bounds exist. With a careful study of the more complex selection mechanism of the SPEA2, we show that it has very different population dynamics. From these, we prove runtime guarantees for the OneMinMax, LeadingOnesTrailingZeros, and OneJumpZeroJump benchmarks that depend less on the population size. For example, we show that the SPEA2 with parent population size mu >= n - 2k + 3 and offspring population size lambda computes the Pareto front of the OneJumpZeroJump benchmark with gap size k in an expected number of O((lambda+mu)n + n^(k+1)) function evaluations. This shows that the best runtime guarantee of O(n^(k+1)) is not only achieved for mu = Theta(n) and lambda = O(n) but for arbitrary mu, lambda = O(n^k). Thus, choosing suitable parameters - a key challenge in using heuristic algorithms - is much easier for the SPEA2 than the NSGA-II.
Finding Diverse Solutions Parameterized by Cliquewidth
Authors: Karolina Drabik; Tomáš Masařík
Venue: AAAI 2026 (main)

Abstract: Finding a few solutions for a given problem that are diverse, as opposed to finding a single best solution to solve the problem, has recently become a notable topic in theoretical computer science. Recently, Baste, Fellows, Jaffke, Masařík, Oliveira, Philip, and Rosamond showed that under a standard structural parameterization by treewidth, one can find a set of diverse solutions for many problems with only a very small additional cost [Artificial Intelligence 2022]. In this paper, we investigate a much stronger graph parameter, the cliquewidth, which can additionally describe some dense graph classes. Broadly speaking, it describes graphs that can be recursively constructed by a few operations defined on graphs whose vertices are divided into a bounded number of groups, while each such group behaves uniformly with respect to any operation.

We show that for any vertex problem, if we are given a dynamic program solving that problem on cliquewidth decomposition, we can modify it to produce a few solutions that are as diverse as possible with as little overhead as in the above-mentioned treewidth paper. As a consequence, we prove that a diverse version of any MSO1 expressible problem can be solved in linear FPT time parameterized by the cliquewidth, the number of sought solutions, and the number of quantifiers in the formula, which was a natural missing piece in the complexity landscape of structural graph parameters and logic for the diverse problems. We prove our results, allowing for a more general natural collection of diversity functions compared to only two mostly studied diversity functions previously. That might be of independent interest as a larger pool of different diversity functions can highlight various aspects of different solutions to a problem.