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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.

AAAI 2026

Parallelizable Riemannian Alternating Direction Method of Multipliers for Non-convex Pose Graph Optimization

so: non-convex optimization cso: applications cso: constraint optimization rob: learning & optimization for rob

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Articulated object modeling, which represents interconnected rigid bodies with their geometry, part segmentation, articulation tree, and physical properties, is crucial for robotic perception and manipulation. Recently existing methods like SAGCI leverage Interactive Perception (IP) to refine models through robot interaction. However, SAGCI suffers from prior-dependency (requiring initialization), neglects kinematic/dynamic constraints, and generates non-watertight meshes. To overcome these limitations, we propose SIAM, a novel framework for efficient and generalizable Single-Interaction Articulated Modeling. Given an initial point cloud, SIAM first enables minimal robot interaction to trigger object motion. It then precisely segments parts by analyzing point cloud differences pre- and post-interaction. For joint parameter estimation, we introduce an optimization incorporating novel kinematic energy constraints, enhancing physical consistency. Finally, we reconstruct a high-quality, topologically watertight mesh by learning 3D Gaussian Primitives from multi-view RGB-D observations under deformation. Extensive experiments on the PartNet-Mobility benchmark demonstrate state-of-the-art articulation modeling performance. Successful real-world deployment with an xArm robot further validates the framework's practicality and transferability. SIAM achieves accurate, prior-free modeling with significantly reduced interaction cost. Code will be publicly available upon acceptance.

SIAM: Towards Generalizable Articulated Object Modeling via Single Robot-Object Interaction

In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions to answer user queries automatically. Although many traditional approaches have been proposed for investigating this research field, they mostly rely on static approaches and neglect the interaction property. We argue that the conversational way can well distinguish the fine-grained representations of questions and has great potential to improve the performance of question retrieval. In this paper, we propose a related question retrieval model through conversations, called TeCQR, to locate related questions in cQA. Specifically, we build conversations by utilizing tag-enhanced clarifying questions. In addition, we design a noise tolerance model that evaluates the semantic similarity between questions and tags, enabling the model to effectively handle noisy feedback. Moreover, the tag-enhanced two-stage offline training is proposed to fully exploit the mutual relationships among user queries, questions, and tags to learn their fine-grained representations. Based on the learned representations and contextual conversations, TeCQR incorporates conversational feedback by learning to ask tag-enhanced clarifying questions to retrieve related questions more effectively. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines. \textit{The code and dataset are provided in the Supplementary Materials.}

Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering

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 MSO$_1$ expressible problem can be solved in linear FPT time parameterized by cliquewidth, the number of sought solutions, and the number of quantifiers in the formula. That was an important missing piece in the complexity landscape of structural graph parameters and logic. 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.

Finding Diverse Solutions Parameterized by Cliquewidth

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific module may overlook shared traits across multiple roles, hindering commonality modeling. In this paper, we propose a novel \textbf{HyCoRA}: \textbf{Hy}per-\textbf{Co}ntrastive \textbf{R}ole-\textbf{A}daptive learning framework, which efficiently improves multi-character role-playing ability by balancing the learning of distinct and shared traits. Specifically, we propose a Hyper-Half Low-Rank Adaptation structure, where one half is a role-specific module generated by a lightweight hyper-network, and the other half is a trainable role-shared module. The role-specific module is devised to represent distinct persona signatures, while the role-shared module serves to capture common traits. Moreover, to better reflect distinct personalities across different roles, we design a hyper-contrastive learning mechanism to help the hyper-network distinguish their unique characteristics. Extensive experimental results on both English and Chinese available benchmarks demonstrate the superiority of our framework. The further GPT-4 evaluations and visual analyses also verify the capability of HyCoRA to capture role characteristics.

HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing

A suitable choice of the representation of candidate solutions is crucial for the efficiency of evolutionary algorithms and related metaheuristics. We focus on problems in permutation spaces, which are at the core of numerous practical applications of such algorithms, e.g., in scheduling and transportation. Inversion vectors (also called Lehmer codes) are an alternative representation of the permutation space $S_n$ compared to the classical encoding as a vector of $n$ unique entries. In particular, they do not require any constraint handling. Using rigorous mathematical runtime analyses, we compare the efficiency of inversion vector encodings to the classical representation and give theory-guided advice on their choice. Moreover, we link the effect of local changes in the inversion code space to classical measures on permutations like the number of inversions. Finally, through experimental studies on linear ordering and quadratic assignment problems, we demonstrate the practical efficiency of inversion vector encodings.

Theoretical and Empirical Analysis of Lehmer Codes to Search Permutation Spaces with Evolutionary Algorithms

Multiphase flow systems, with their complex dynamics, field discontinuities, and interphase interactions, pose significant computational challenges for traditional numerical solvers. While neural operators offer efficient alternatives, they often struggle to achieve high-resolution numerical accuracy in these systems. This limitation primarily stems from the inherent spatial heterogeneity and the scarcity of high-quality training data in multiphase flows.
In this work, we propose the Interface Information-Aware Neural Operator (IANO), a novel framework that explicitly incorporates interface topology to enhance the prediction accuracy even in low-data regimes. The IANO architecture introduces two key components: 1) An interface-aware multiple function encoding mechanism jointly models multiple physical fields and interface topology, thus capturing the high-frequency physical features at the interface.
2) A geometry-aware positional encoding mechanism further establishes the relationship between interface topology, physical variables, and spatial positions, making it to achieve pointwise super-resolution prediction in a more refined spatial space. Experimental results demonstrate that IANO outperforms baselines by $\sim$10\% in accuracy for multiphase flow simulations while maintaining robustness under data-scarce and noise-perturbed conditions.

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

Recently, Interleaved-modal Chain-of-Thought (ICoT) reasoning has achieved remarkable success by leveraging both multimodal inputs and outputs, attracting increasing attention. While achieving promising performance, current ICoT methods still suffer from two major limitations: (1) Static Visual Thought Positioning, which statically inserts visual information at fixed steps, resulting in inefficient and inflexible reasoning; and (2) Broken Visual Thought Representation, which involves discontinuous and semantically incoherent visual tokens. To address these limitations, we introduce Interleaved-modal Chain-of-Thought reasoning with Dynamic and Precise Visual Thoughts (DaP-ICoT), which incorporates two key components: (1) Dynamic Visual Thought Integration adaptively introduces visual inputs based on reasoning needs, reducing redundancy and improving efficiency. (2) Precise Visual Thought Guidance ensures visual semantically coherent and contextually aligned representations. Experiments across multiple benchmarks and models demonstrate that DaP-ICoT achieves state-of-the-art performance. In addition, DaP-ICoT significantly reduces the number of inserted images, leading to a 72.6% decrease in token consumption, enabling more efficient ICoT reasoning.

Let’s Think with Images Efficiently! An Interleaved-Modal Chain-of-Thought Reasoning Framework with Dynamic and Precise Visual Thoughts

The MOEA/D is the most popular decomposition-based evolutionary algorithm to solve multi-objective optimization problems. However, among the two common decomposition approaches, weighted-sum and Tchebycheff, the existing theoretical research almost exclusively focus on the latter one. In this first complete mathematical runtime analysis for the MOEA/D using the original weighted-sum decomposition, we show that this variant of the algorithm solves the classic ONEMINMAX benchmark considerably faster than both the MOEA/D with Tchebycheff decomposition and many other classic algorithms such as the NSGA-II, NSGA-III, SMS-EMOA, and SPEA2. More precisely, we show that already a logarithmic number of subproblems suffices for the algorithm to be efficient, and then typically $O(n \log^2 n)$ function evaluations suffice to compute the full Pareto front. This beats the other algorithms by a factor of $\Theta(n / \log n)$. For a second benchmark, the ONEJUMPZEROJUMP problem, we show a speed-up by a factor of $\Theta(n)$. Overall, this work shows that a further development of the weighted-sum approach might be fruitful.

Superior Runtime Guarantees for the MOEA/D Multi-Objective Optimizer via Weighted-Sum Decomposition

We propose a multi-agent multi-armed bandit (MA-MAB) framework to ensure fair outcomes across agents while maximizing overall system performance. For example, in a ridesharing setting where a central dispatcher assigns drivers to distinct geographic regions, utilitarian welfare (the sum of driver earnings) can be highly skewed—some drivers may receive no rides. We instead measure fairness by Nash social welfare, i.e., the product of individual rewards. A key challenge in this setting is decision-making under limited information about arm rewards (geographic regions). To address this, we introduce a novel probing mechanism that strategically gathers information about selected arms before assignment. In the offline setting, where reward distributions are known, we exploit submodularity to design a greedy probing algorithm with a constant-factor approximation guarantee. In the online setting, we develop a probing-based algorithm that achieves sublinear regret while preserving Nash social welfare. Extensive experiments on synthetic and real-world datasets demonstrate that our approach outperforms baseline methods in both fairness and efficiency.

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

The powerful generalization capabilities of Vision-Language-Action (VLA) models are bottlenecked by their heavy reliance on massive, redundant, and unevenly valued datasets, hindering their widespread application. Existing model-centric optimization paths, whether through model compression (which often leads to performance degradation) or policy distillation (where the output is model-dependent and lacks versatility), have failed to fundamentally address this data-level challenge. To this end, we propose a fundamentally different, data-centric generative data distillation framework, FT-NCFM. Our framework employs a self-contained FT (Fact-Tracing) engine that combines causal attribution with programmatic contrastive verification to assess the intrinsic value of samples. This evaluation guides an NCFM adversarial game to synthesize a model-agnostic, information-dense, reusable data asset. Experimental results on several mainstream VLA benchmarks show that using just 5% of our distilled coreset, we can train models that achieve over 90% of the task success rate of models trained on the full dataset, while reducing training time by 75.4%. Our work demonstrates that intelligent data distillation is a highly promising new path for building efficient, high-performance VLA models. Code and datasets will be made available upon acceptance.

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