Singapore

Despite the rich spatiotemporal patterns contained in trajectory data from multiple Location-Based Social Network (LBSN) platforms, heterogeneous formats, semantic inconsistencies, and unequal user scales across platforms create substantial barriers to reliable identity mapping. Furthermore, GPS drift and sparse sampling result in degraded data quality and distribution imbalance, which render existing trajectory representation methods inadequate for capturing high-order dependencies and dynamic spatiotemporal evolution patterns in heterogeneous multi-relational graphs.
To this end, we propose HANCUA (Hierarchical Attention Network with Correction for User Association), a novel framework that employs a dual-stage correction mechanism to enhance cross-domain trajectory analysis. The approach constructs hierarchical multi-relational graphs comprising location, trajectory, and correction layers to capture fine-grained mobility patterns, behavioral associations, and inter-platform distribution differences. We design relation-aware multi-head graph attention networks to model complex interactions among heterogeneous node types, which enables comprehensive spatial relationship modeling. A spatiotemporal semantic collaborative learning module integrates temporal information with mobility patterns through interaction-aware attention mechanisms, while an ensemble correction decision module incorporates ensemble learning principles to systematically correct user association biases and address distribution imbalance problems.
Extensive experiments on two real-world LBSN cross-domain datasets reveals that HANCUA significantly outperforms state-of-the-art methods in user identity linking accuracy.

AAAI 2026

Hierarchical Attention Network with Correction for Cross-Domain User Association

app: social networks

temporal or spatio-temporal data

dmkm: mining of spatial

ml: graph-based machine learning

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

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-25 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.



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.

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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.

As a knowledge-intensive and challenging task, automatic generation of long-form wiki-style articles has garnered increasing attention from researchers due to its ability to efficiently integrate, organize and present vast amounts of both structured and unstructured knowledge. 
To the best of our knowledge, most of the existing mainstream state-of-the-art methods for automatic wiki-style article generation typically follow a "one-shot generation" paradigm: given a topic, (1) first generating a structured outline, (2) then independently and in parallel generating the content of each outline chapter in a one-shot using the chapter title and references. However, the core limitation of the paradigm lies in its disregards inter-chapter correlation and lacks post-generation revision and refinement, resulting in content redundancy, weak relevance and logical inconsistency. To address these issues, we propose WikiREVIEW, a novel multi-perspective review framework for automatic wiki-style article generation. Specifically, our proposed method introduces multi-perspective experts to review the content of each outline chapter at both chapter and paragraph levels following the initial generation, offering evaluation feedback and continuously refining the numerous deficiencies in the initial long-form article, ultimately achieving high-quality wiki-style article generation. Extensive experimental results on the public English dataset FreshWiki and our own constructed high-quality Chinese dataset ChineseWiki, demonstrate that our proposed WikiREVIEW significantly outperforms existing state-of-the-art automatic wiki-style article generation methods across all automatic evaluation metrics and human evaluation.

WikiREVIEW: A Multi-Perspective Review Framework for Automatic Wiki-Style Article Generation

Audio synthesis has broad applications in multimedia. Recent advancements have made it possible to generate relevant audios from inputs describing an audio scene, such as images or texts. However, the immersiveness and expressiveness of the generation are limited. One possible problem is that existing methods solely rely on the global scene and overlook details of local sounding objects (i.e., sound sources). To address this issue, we propose a Sound Source-Aware Audio (SS2A) generator. SS2A is able to locally perceive multimodal sound sources from a scene with visual detection and cross-modality translation. It then contrastively learns a Cross-Modal Sound Source (CMSS) Manifold to semantically disambiguate each source. Finally, we attentively mix their CMSS semantics into a rich audio representation, from which a pretrained audio generator outputs the sound. To model the CMSS manifold, we curate a novel single-sound-source visual-audio dataset VGGS3 from VGGSound. We also design a Sound Source Matching Score to clearly measure localized audio relevance. With the effectiveness of explicit sound source modeling, SS2A achieves state-of-the-art performance in extensive image-to-audio tasks. We also qualitatively demonstrate SSV2A's ability to achieve intuitive synthesis control by compositing vision, text, and audio conditions. Furthermore, we show that our sound source modeling can achieve competitive video-to-audio performance with a straightforward temporal aggregation mechanism.

Gotta Hear Them All: Towards Sound Source Aware Audio Generation

Memory behavior modeling seeks to predict individual recall performance and understand its underlying cognitive mechanisms. However, the dynamic and heterogeneous nature of memory data poses significant challenges to the generalization ability of models under unseen conditions. To address this challenge, we propose an invariant representation learning framework I-Mem that integrates self-supervised contrastive learning with decorrelation constraints, enabling the adaptive identification and suppression of environment-related factors in sequential behavioral data, thereby mitigating the influence of spurious features and enhancing the modeling of stable cognitive structures. Importantly, the method does not rely on explicit environment partitioning or predefined environment labels, while our theoretical analysis demonstrates that it can effectively resist environmental perturbations and facilitate the extraction of invariant structural representations, thereby ensuring adaptability and generalization. Empirical evaluations on both synthetic and real-world datasets further confirm its superiority over mainstream methods in terms of generalization performance and stable feature identification. Feature attribution analysis reveals that I-Mem extracts invariant features aligned with classical cognitive effects, and reflects short-term behavioral patterns that may indicate latent cognitive mechanisms beyond existing theories, highlighting both interpretability and discovery potential.

Invariant Representation Learning for Memory Behavior Modeling via Adaptive Environment Separation

Neural Radiance Fields (NeRF)-based Visual Simultaneous Localization and Mapping (SLAM) achieve superior scene geometric modeling and robust camera tracking by leveraging neural representations. 
Existing methods typically relied on multi-resolution hash encoding with truncated signed distance fields (TSDF) to achieve high frame rates. However, unavoidable hash collisions can lead to artifacts, and multi-view color inconsistencies in indoor scenes can result in shape-radiance ambiguity, adversely affecting geometric quality and tracking accuracy.
To address these issues, we propose a novel Multi-scale Hybrid Encoding-based Decoupled SLAM (MHED-SLAM). 
First, to mitigate the adverse effects of hash collisions and reduce the number of learnable parameters, we innovatively fuse a coarse-scale hash tri-plane with a fine-scale hash grid within a single latent volume. 
Second, to enable precise geometric reconstruction and camera tracking, we decouple the reconstruction and rendering processes, independently learning a TSDF field for reconstruction and a density field for rendering.
Third, we devise a Symmetric Kullback-Leibler (SKL) strategy based on ray termination distributions to align the probability distributions derived from the TSDF and density fields for their synchronous convergence. 
Extensive experimental evaluations demonstrate that our approach surpasses the state-of-the-art (SOTA) methods by utilizing a faster frame rate of 20 Hz and fewer parameters, while achieving higher tracking and reconstruction accuracy.

MHED-SLAM: Multi-Scale Hybrid Encoding-Based Decoupled SLAM

Graph contrastive learning (GCL) aims to learn representations by bringing semantically similar graphs closer and pushing dissimilar ones farther apart without label supervision. Hard negatives, which refer to graphs that have different labels but similar embeddings to the target graph, play a key role in improving representation discrimination. However, current methods that generate both high-quality positives and hard negatives face two challenges:
(1) Hard negative sample generation often suffers from class imbalance, resulting in unequal attention across classes and reduced discriminative power in the learned representations.
(2) The typical binary positive sample generation approach, which divides the graph into important and unimportant semantic regions, overlooks regions that negatively impact semantics and mislead model predictions. To address these issues, we introduce a novel method named BalanceGCL, which enhance graph contrastive learning with balanced hard negatives and fine-grained semantic-aware positives. BalanceGCL comprises two modules: Balanced Hard Negative graphs generation (BHN) and Fine-grained Semantic-aware Positive graphs generation (FSP). Inspired by the counterfactual mechanism, BHN generates balanced hard negatives that remain structurally similar to the original graph while inducing a controlled semantic shift. To ensure class balance, BHN iteratively constructs one hard negative sample for each class, ensuring an even distribution of negative samples across all alternative categories. FSP leverages the semantic differences between original graphs and balanced hard negatives to identify positively contributing, negatively contributing, and unimportant regions. By enhancing the influence of positive contributors, suppressing negative ones, and perturbing unimportant areas, it generates more reliable and semantically complete positive samples. The proposed method outperforms state-of-the-art GCL techniques across 14 datasets in graph classification and transfer learning tasks, demonstrating its effectiveness in tackling class imbalance and identifying fine-grained semantic-aware regions.

Graph Contrastive Learning with Balanced Hard Negatives and Fine-grained Semantic-aware Positives

Accurate and efficient depth estimation from time-of-flight (ToF) LiDAR is essential for autonomous systems operating in real-world environments. However, traditional histogram-based depth estimation (HBDE) algorithms face fundamental limitations in balancing depth performance and computational cost, and they struggle under signal-induced pile-up distortion. While deep learning has shown promise, existing neural network-based methods rely on large models that are impractical for deployment on edge hardware. To bridge this critical gap, we propose a paradigm shift in histogram-based ToF estimation, reframing depth estimation from signal filtering to lightweight similarity learning. Instead of attempting to correct the distorted signal, our approach learns a specialized metric where the measure of similarity between the distorted histogram and a reference pulse is the temporal shift itself. The resulting 57.61 KB model, over 215.2 $\times$ smaller than state-of-the-art deep learning approaches, achieves real-time performance (106.27 fps) on an FPGA. It delivers superior accuracy across nearly all signal-noise conditions, including 2.21 cm RMSE at severe pile-up scenarios, significantly outperforming conventional methods while remaining practical for on-device deployment—a feat unattainable by prior large-scale deep learning models.

A Paradigm Shift in High-Resolution Depth Estimation Using SPAD-Based LiDAR Histograms: From Signal Filtering to Lightweight Similarity Learning

Infrared small target detection is challenging due to limited target size and low signal-to-noise ratio. Unlike common targets, infrared small targets contain a higher proportion of edge pixels and exhibit blurred boundaries due to diffraction and quantization artifacts, making boundaries uniquely valuable cues for target perception. However, existing methods often emphasize holistic modeling while underutilizing such informative boundary cues. Motivated by this observation, we propose a Dual-Path Edge-Guided Frequency-Aware Network (DEFANet), which enables edge-target collaborative modeling for enhanced feature representation. DEFANet features a dual-path design, consisting of a main branch for holistic target modeling and an edge branch for boundary transition perception. To facilitate interaction and enhance representation in both branches, we introduce two core modules: Frequency-Aware Dual Enhancement Module (FADE) and Edge-Guided Integration Module (EGI). FADE employs a Frequency-Decoupled Attention Enhancement Mechanism to enhance both branches in the frequency domain, strengthening holistic modeling in the main branch and boundary representation in the edge branch. EGI leverages a Dual-Path Group-Wise Guidance Mechanism to integrate enhanced edge features into the main branch, improving boundary perception. Extensive experiments on four public infrared small target datasets, MDvsFA, LAFT, SIRST, and SIATD, demonstrate that DEFANet achieves SOTA performance. Ablation studies further validate the effectiveness of DEFANet and the soundness of its design motivation.

DEFANet: Dual-Path Edge-Target Collaboration with Frequency-Aware Enhancement for Infrared Small Target Detection

Neural solvers have demonstrated remarkable success in combinatorial optimization, often surpassing traditional heuristics in speed, solution quality, and generalization. However, their efficacy deteriorates significantly when confronted with complex constraints that cannot be effectively managed through simple masking mechanisms. To address this limitation, we introduce Universal Constrained Preference Optimization (UCPO), a novel plug-and-play framework that seamlessly integrates preference learning into existing neural solvers via a specially designed loss function, without requiring architectural modifications. UCPO embeds constraint satisfaction directly into a preference-based objective, eliminating the need for meticulous hyperparameter tuning. Leveraging a lightweight warm-start fine-tuning protocol, UCPO enables pre-trained models to consistently produce near-optimal, feasible solutions on challenging constraint-laden tasks, achieving exceptional performance with as little as 1\% of the original training budget.

UCPO: A Universal Constrained Combinatorial Optimization Method via Preference Optimization

Text-to-image (T2I) models have raised increasing safety concerns due to their capacity to generate NSFW and other banned objects. To mitigate these risks, safety filters and concept removal techniques have been introduced to block inappropriate prompts or erase sensitive concepts from the models. However, all the existing defense methods are not well prepared to handle diverse adversarial prompts. In this work, we introduce MacPrompt, a novel black-box and cross-lingual attack that reveals previously overlooked vulnerabilities in T2I safety mechanisms. Unlike existing attacks that rely on synonym substitution or prompt obfuscation, MacPrompt constructs macaronic adversarial prompts by performing cross-lingual character-level recombination of harmful terms, enabling fine-grained control over both semantics and appearance. By leveraging this design, MacPrompt crafts prompts with high semantic similarity to the original harmful inputs (up to 0.96) while bypassing major safety filters (up to 100\%). More critically, it achieves attack success rates as high as 92\% for sex-related content and 90\% for violence, effectively breaking even state-of-the-art concept removal defenses. These results underscore the pressing need to reassess the robustness of existing T2I safety mechanisms against linguistically diverse and fine-grained adversarial strategies.
\textbf{Warning: Content Warning: This paper includes sensitive examples (e.g., adult, violent, or illegal content). Unsafe images are masked but may still be disturbing.}

MacPrompt: Maraconic-Guided Jailbreak Against Text-to-Image Models

Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are prone to errors in generating structured queries.
To improve the structured data QA ability of small-scale LLMs, we propose a self-correction distillation (SCD) method. In SCD, an error prompt mechanism (EPM) is designed to detect errors and provide customized error messages during inference, and a two-stage distillation strategy is designed to transfer large-scale LLMs' query-generation and error-correction capabilities to small-scale LLM.
Experiments across 5 benchmarks with 3 structured data types demonstrate that our SCD achieves the best performance and superior generalization on small-scale LLM (8B) compared to other distillation methods, and closely approaches the performance of GPT4 on some datasets. Furthermore, large-scale LLMs equipped with EPM surpass the state-of-the-art results on most datasets.

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