Singapore

With the increasing scale and complexity of graph data, node attributes are also becoming richer and more complex, spanning multi-view/modal features and informative text. Classic GNNs equipped with shallow encoders are no longer sufficient to handle such data independently, making model collaboration across different architectures an inevitable trend. 
Recently, the integration of Large Language Models (LLMs) and GNNs has attracted significant attention. However, the inherent disparity between these models remains a key challenge. 
Promising solutions have considered fine-tuning Small Language Models (SLMs) to bridge the gap between GNNs and frozen LLMs. 
Yet, this introduces another problem: large and small models bring complementary views of knowledge, but how to effectively integrate them and allow mutual refinement remains a significant research gap.
To address these challenges, we introduce COLA, a collaborative large–small model framework that enables seamless cooperation among semantic LLMs, task-specific fine-tuned SLMs, and structure-aware GNNs.
COLA features a unique Consensus–Complement Coordination (CoCo) mechanism, wherein the Mixture-of-Coordinators (MoC) architecturally aligns the LLM and SLM. Built upon MoC, a flexible graph-knowledge infusion strategy encourages the joint alignment and graph knowledge learning of textual representations. Extensive evaluations across nine diverse datasets demonstrate that COLA consistently achieves state-of-the-art performance, validating the effectiveness and generality of our collaborative paradigm.

AAAI 2026

Unifying Multi-View Knowledge for Graph Learning via Model Collaboration

social network analysis & community

ml: classification and regression

dmkm: graph mining

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/)

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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.

Short texts present significant challenges for clustering due to semantic sparsity, limited contextual information, and ambiguous category boundaries. While recent studies incorporating contrastive learning and cluster structure optimization have improved performance, their reliance on augmented samples often introduces noise and weakens the capacity of pretrained language models to capture fine-grained semantics. To address these issues, we propose a Graph-augmented and Over-smoothing-resistant Contrastive Clustering framework (GOCC). Specifically, GOCC constructs sentence-level and cluster-level graphs to capture local semantic similarity and global structural patterns, incorporating these signals into sentence representations to enhance representational quality and clustering suitability. Moreover, we introduce a contrastive mechanism based on intermediate layer representations within graph-augmented contrastive learning to alleviate semantic over-smoothing caused by deep networks. Finally, a target-distribution-driven clustering optimization strategy is employed to leverage high-confidence samples in guiding cluster assignments. Experimental results on several benchmark short text datasets demonstrate that GOCC consistently outperforms state-of-the-art methods in terms of clustering accuracy and normalized mutual information.

Graph-augmented and Over-smoothing-resistant Contrastive Clustering for Short Text

Large language models (LLMs), such as ChatGPT, have achieved remarkable success across a wide range of fields. However, their trustworthiness remains a significant concern, as they are still susceptible to jailbreak attacks aimed at eliciting inappropriate or harmful responses. 
Most existing jailbreak attacks, nevertheless, mainly operate at the natural language level and rely on a single attack strategy, limiting their effectiveness in comprehensively assessing LLM robustness. In this paper, we propose Equacode, a novel multi-strategy jailbreak approach for large language models via equation-solving and code completion. This approach transforms malicious intent into a mathematical problem and then requires the LLM to solve it using code, leveraging the complexity of cross-domain tasks to divert the model's focus toward task completion rather than safety constraints. Experimental results show that Equacode achieves an average success rate of 91.19\% on the GPT series and 97.62\% across 5 state-of-the-art LLMs, all with only a single query. Further, ablation experiments demonstrate that EquaCode outperforms either the mathematical equation module or the code module alone. This suggests a strong synergistic effect, thereby demonstrating that multi-strategy approach yields results greater than the sum of its parts. EquaCode is open-source and available at this repository: \url{https://github.com/lzzzr123/Equacode}.

EquaCode: A Multi-Strategy Jailbreak Approach for Large Language Models via Equation Solving and Code Completion

Recently, 3D Gaussian Splatting (3DGS), an explicit scene representation technique, has shown significant promise for dynamic novel-view synthesis from monocular video input. However, purely data-driven 3DGS often struggles to capture the diverse physics-driven motion patterns in dynamic scenes. To fill this gap, we propose Physics‑Informed Deformable Gaussian Splatting (PIDG), which treats each Gaussian particle as a Lagrangian material point with time-varying constitutive parameters and is supervised by 2D optical flow via motion projection. Specifically, we adopt static-dynamic decoupled 4D decomposed hash encoding to reconstruct geometry and motion efficiently. Subsequently, we impose the Cauchy momentum residual as a physics constraint, enabling independent prediction of each particle’s velocity and constitutive stress via a time-evolving material field. Finally, we further supervise data fitting by matching Lagrangian particle flow to camera-compensated optical flow, which accelerates convergence and improves generalization. Experiments on a custom physics-driven dataset as well as on standard synthetic and real-world datasets demonstrate significant gains in physical consistency and monocular dynamic reconstruction quality.

Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field

With the explosive growth of multimodal data streams on social media, the timely detection of emerging social events has become increasingly important. 
As a result, Multimodal Social Event Detection in open-world settings is receiving growing attention.
However, most existing methods face two major limitations:
(1) They overlook the dynamic nature of open-world social media data and fail to design dedicated incremental learning frameworks.
(2) They ignore the impact of noise in streaming data, leading to performance degradation over long-term detection.
To overcome these limitations, we propose SeInEvent (**S**tructural **E**ntropy Guided **In**cremental Learning for Open-World Multimodal Social **Event** Detection).
Our innovations are as follows:
**First**, considering data dynamics, we design a self-supervised alternating incremental contrastive learning mechanism.
Through knowledge distillation, historical event clusters were reviewed and consolidated, and contrastive learning was combined to absorb knowledge of unknown events, ultimately achieving incremental learning without labels.
**Second**, addressing the impact of noise, we propose a Pointwise Structural Entropy-based noise filter, which quantifies each sample’s informational contribution to the event clustering structure. 
It enables automatic removal of noisy data and supports robust long-term detection.
Extensive experiments on two public datasets demonstrate that SeInEvent achieves superior performance.

Structural Entropy Guided Incremental Learning for Open-World Multimodal Social Event Detection

Open-vocabulary multi-object tracking (OV-MOT) aims to track objects with unseen categories beyond the training set. While existing methods rely on pseudo video sequences synthesized from static images, they struggle to model realistic motion patterns, resulting in limited association performance in real-world scenarios. To alleviate these issues, we propose SAM2-OV, a novel association learning-free OV-MOT method that adopts a detection-only tuning paradigm, eliminating the need for synthetic sequences or spatiotemporal supervision and substantially reducing the overall learnable parameters. The core of our method is a Unified Detection Module (UDM), which effectively provides object-level prompts to enable SAM2 for OV-MOT. Enabled by UDM, SAM2-OV is the first to integrate SAM2 for OV-MOT, fully unleashing its zero-shot cross-frame association ability.To further enhance object association under occlusion and abrupt motion, we introduce a Motion Prior Assistance Module (MPAM) that incorporates motion cues into the mask selection process. In addition, a Semantic Enhancement Adapter (SEA) distilled from CLIP is used to improve classification generalization. A sparse prompting strategy is also adopted to reduce computational redundancy by triggering detection only on selected keyframes. As only the detection module is tuned on static images, the overall training process remains simple and efficient. Experiments on the TAO dataset demonstrate that SAM2-OV achieves state-of-the-art performance under the TETA metric, particularly on novel categories. Evaluations on the KITTI dataset show the strong zero-shot cross-domain transferability of our SAM2-OV.

SAM2-OV: A Novel Detection-Only Tuning Paradigm for Open-Vocabulary Multi-Object Tracking

Real-world fraud detection applications benefit from graph learning techniques that jointly exploit node features—often rich in textual data—and graph structural information. Recently, Graph-Enhanced LLMs have emerged as a promising graph learning approach that converts graph information into prompts, exploiting LLMs' ability to reason over both textual and structural information. Among them, text-only prompting, which converts graph information into prompts consisting solely of text tokens, offers a solution that relies only on LLM tuning without requiring additional graph-specific encoders. However, text-only prompting struggles on heterogeneous fraud-detection graphs: multi-hop relations expand exponentially with each additional hop, leading to rapidly growing neighborhoods associated with dense textual information. These neighborhoods may overwhelm the model with long, irrelevant content in the prompt and suppress key signals from the target node, thereby degrading performance. To address this challenge, we propose Dual Granularity Prompting (DGP), which mitigates information overload by preserving fine-grained textual details for the target node while summarizing neighbor information into coarse-grained text prompts. DGP introduces tailored summarization strategies for different data modalities—bi-level semantic abstraction for textual fields and statistical aggregation for numerical features—enabling effective compression of verbose neighbor content into concise, informative prompts. Experiments across public and industry datasets demonstrate that DGP operates within a manageable token budget while improving fraud detection performance by up to 6.8\% (AUPRC) over state-of-the-art methods, showing the potential of Graph-Enhanced LLMs for fraud detection.

DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs

The generalization capability of deepfake detectors is crucial for real-world applications. Data augmentation to generate synthetic fake faces has served as an effective strategy to enhance generalization. Interestingly, current state-of-the-art (SoTA) methods rely on fixed augmentation strategies, raising a fundamental question: Can a single static augmentation approach suffice, or does the diversity of forgery features necessitate dynamic strategies? We argue that existing methods overlook the evolving complexity of real-world forgery patterns, such as facial warping, expression manipulation, and compression artifacts, which cannot be fully simulated by fixed policies.
To bridge this gap, we propose CRDA (Curriculum Reinforcement-Learning Data Augmentation), a novel framework that guides the detector to progressively master multi-domain forgery features from simple to complex. CRDA synthesizes augmented samples using a configurable pool of forgery operations and dynamically generates adversarial samples tailored to the detector’s current learning state. 
Key to our approach is the integration of reinforcement learning (RL) and causal inference. To efficiently explore the vast augmentation space, an RL agent dynamically selects augmentation actions based on the detector’s performance, ensuring continuous adaptation to increasingly challenging forgeries. Simultaneously, the agent’s output is designed to introduce variations in action spaces, generating heterogeneous forgery patterns. These variations are guided by causal inference theory, which mitigates spurious correlations by suppressing task-irrelevant biases and enforcing the model to focus on causally invariant features. This integration ensures robust generalization by decoupling synthetic augmentation patterns from the model’s learned representations.
Extensive experiments demonstrate that the proposed method significantly improves the generalizability of the detector, achieving superior performance compared to state-of-the-art methods on multiple cross-domain datasets. Code is available at the supplementary material.

Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation

The performance of Offline reinforcement learning is significantly impacted by the issue of state distributional shift, and out-of-distribution (OOD) state correction is a popular approach to address this problem. However, previous methods correct the agent's transition distributions in a supervised way, which significantly degrades the flexibility and robustness. In this paper, we propose a novel method named Density-Aware Safety Perception (DASP) for OOD state correction. Specifically, our method encourages the agent to prioritize actions that lead to outcomes with higher data density, thereby promoting its operation within or the return to in-distribution (safe) regions. To achieve this, we optimize the objective within a variational framework that concurrently considers both the potential outcomes of decision-making and their density, thus providing crucial contextual information for safe decision-making. Finally, we validate the effectiveness and feasibility of our proposed method through extensive experimental evaluations on the offline MuJoCo and AntMaze suites.

Variational OOD State Correction for Offline Reinforcement Learning

Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a text-aligned module for semantic consistency and a motion-aligned module for realism, refining noisy motions at each timestep to balance probability density and alignment. Extensive experiments of both motion generation and retrieval tasks demonstrate that our approach significantly improves text-motion alignment and motion quality compared to existing state-of-the-art methods.

ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment

Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis. Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions. From a probabilistic perspective, these methods effectively model the relationships between latent factors and the low-rank tensor using Dirac delta distributions. However, tensor low-rank decomposition is inherently non-unique, leading to a multimodal distribution over possible solutions. Critically, such prior knowledge is rarely available in practical scenarios, particularly regarding the optimal rank structure and contraction rules. To address this issue, we propose a score-based model that eliminates the need for predefined structural or distributional assumptions, enabling the learning of compatibility between tensors and latent factors. Specifically, a neural network is designed to learn the energy function, which is optimized via score matching to capture the gradient of the joint log-probability of tensor entries and latent factors. Our method allows for modeling structures and distributions beyond the Dirac delta assumption. Moreover, integrating the block coordinate descent (BCD) algorithm with the proposed smooth regularization enables the model to perform both tensor completion and denoising. Experimental results demonstrate significant performance improvements across various tensor types, including sparse and continuous-time tensors, as well as visual data.

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