Singapore

Graph Neural Networks (GNNs) have achieved significant progress in semi-supervised data classification, with an assumption that a complete graph or accurate structure is available. In this paper, a novel GNN architecture, named discrete-structure-augmentation graph convolutional network (DSA-GCN) is proposed, to apply the GCNs in real-world scenarios where the graphs are noisy and incomplete or even not available. Compared with existing methods, DSA-GCN firstly uses a variational Expectation-Maximization (EM) algorithm to jointly learn graph structure, including a discrete probability distribution on the edges of the graph and label dependency, and the parameters of GCN. Second, DSA-GCN applies novel reconstruction loss in learning discrete dependency structure on graph, together with consistency loss. Third, augmentation strategy is used to derive discrete graph structures with varying sparsity. Extensive experiments demonstrate that DSA-GCN significantly outperforms existing methods under varying levels of edge sparsity.

AAAI 2026

Discrete Structure Augmentation for Graph Convolutional Networks

social network analysis & community，ml: deep neural architectures and foundation models

ml: graph-based machine learning，dmkm: graph mining

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

Medical Visual Question Answering (Med-VQA) aims to generate accurate answers for clinical questions grounded in medical images, which has attracted increasing research attention due to its potential to streamline diagnostics and reduce clinical burden.
Recent advances in Large Vision-Language Models (LVLMs) have shown great promise for Med-VQA, but still suffer from two inference-time issues: (1) attention shift, where the LVLM over-relies on textual priors; and (2) attention dispersion, where it fails to focus on critical diagnostic regions. To tackle these issues, we propose Contrastive Mutual Information Decoding (CMID), a training-free inference-time intervention grounded in information theory for Med-VQA. Concretely, CMID first identifies the Principal Focus Area (PFA) from decoder attention maps, then constructs focus-preserving and focus-excluding views to derive dual contrastive signals that simultaneously amplify salient visual cues and suppress background noise. Crucially, these corrective signals are adaptively scaled by a reliability-gated self-correction mechanism, based on the distributional shift induced by the PFA. Extensive experiments on three Med-VQA benchmarks demonstrate the effectiveness of CMID. Further analyses showcase its robust generalizability across diverse medical architectures and tasks.

CMID: Towards Medical Visual Question Answering via Contrastive Mutual Information Decoding

Homeostatic mechanisms play a crucial role in maintaining optimal functionality within the neural circuits of the brain. By regulating physiological and biochemical processes, these mechanisms ensure the stability of an organism’s internal environment, enabling it to better adapt to external changes. Among these mechanisms, the Bienenstock, Cooper, and Munro (BCM) theory has been extensively studied as a key principle for maintaining the balance of synaptic strengths in biological systems. Despite the extensive development of spiking neural networks (SNNs) as a model for bionic neural networks, no prior work in the machine learning community has integrated bio-inspired BCM theory into SNNs to provide homeostasis. In this study, we propose a Dynamic Weight Adaptation Mechanism (DWAM) for SNNs, inspired by the BCM theory. DWAM can be integrated into the host SNN, dynamically adjusting network weights in real time to regulate neuronal activity, providing homeostasis to the host SNN without any fine-tuning. We validated our method through dynamic obstacle avoidance and continuous control tasks under both normal and specifically designed degraded conditions. Experimental results demonstrate that DWAM not only enhances the performance of SNNs without existing homeostatic mechanisms under various degraded conditions but also further improves the performance of SNNs that already incorporate homeostatic mechanisms.

Dynamic Weight Adaptation in Spiking Neural Networks Inspired by Biological Homeostasis

We propose a novel Auto-Regressive (AR) image generation approach that models images as hierarchical compositions of interpretable visual layers. While AR models have achieved transformative success in language modeling, replicating this success in vision tasks has presented unique challenges due to inherent spatial dependencies in images. Addressing the unique challenges of vision tasks, our method (CART) adds image details iteratively via semantically meaningful decompositions. We demonstrate the flexibility and generality of CART by applying it across three distinct decomposition strategies: (i) Base-Detail Decomposition (Mumford-Shah smoothness), (ii) Intrinsic Decomposition (albedo/shading), and (iii) Specularity Decomposition (diffuse/specular). This "next-detail" strategy outperforms traditional "next-token" and "next-scale" approaches, improving controllability, semantic interpretability, and resolution scalability. Experiments show CART generates visually compelling results while enabling structured image manipulation, opening new directions for controllable generative modeling via physically or perceptually motivated image factorization.

CART: Compositional AutoRegressive Transformer for Image Generation

Multimodal data significantly improves the performance of pretrained models, but its practical application is often limited by missing or incomplete data across modalities. There are two key challenges that existing methods of synthesizing missing data face: (1) semantic inaccuracies due to model hallucinations and (2) discrepancies in distribution preferences between generated and original data. To address these challenges, we propose a novel three-stage multimodal data augmentation framework (\textbf{GFR}), which \textbf{G}enerate, \textbf{F}ilter, and \textbf{R}ank missing modality data. Our framework leverages multimodal large models for diverse data generation, designs a scene graph matching-based filtering algorithm to ensure semantic consistency, and constructs a preference-aware ranking model to align the generated data with both the original distribution and task relevance. Our framework not only enhances semantic diversity and consistency in data generation but also effectively captures the implicit characteristics of the original dataset and the target model. We demonstrate the effectiveness of GFR across multiple datasets by testing different missing types and missing ratios.

Generating-Filtering-Ranking: A Three-Stage MultiModal Data Augmentation Framework Under Partial Modality Missing

Large language models (LLMs) face significant deployment challenges due to their massive computational demands. While pruning offers a promising compression solution, existing methods suffer from two critical limitations: (1) They neglect activation distribution shifts between calibration data and test data, resulting in inaccurate error estimations; (2) Overlooking the long-tail distribution characteristics of activations in the attention module. To address these limitations, this paper proposes a novel pruning method, $D^2Prune$. First, we propose a dual Taylor expansion-based method that jointly models weight and activation perturbations for precise error estimation, leading to precise pruning mask selection and weight updating and facilitating error minimization during pruning. Second, we propose an attention-aware dynamic update strategy that preserves the long-tail attention pattern by jointly minimizing the KL divergence of attention distributions and the reconstruction error. Extensive experiments show that $D^2Prune$ consistently outperforms SOTA methods across various LLMs (e.g., OPT-125M, LLaMA2/3, Qwen3). Moreover, the dynamic attention update mechanism also generalizes well to ViT-based vision models like DeiT, achieving superior accuracy on ImageNet-1K. The source code is publicly available at \url{https://anonymous.4open.science/r/D2Prune-FF87}.

D2 Prune: Sparsifying Large Language Models via Dual Taylor Expansion and Attention Distribution Awareness

Zero-Shot Stance Detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with limited labeled data. While large language models (LLMs) offer zero-shot capabilities, prompting-based approaches often fall short in handling complex reasoning and lack robust generalization to novel targets. Meanwhile, LLM-enhanced methods still require substantial labeled data and struggle to move beyond instance-level patterns, limiting their interpretability and adaptability. 
Inspired by cognitive science, we propose the Cognitive Inductive Reasoning Framework (CIRF), a schema-driven method that bridges linguistic inputs and abstract reasoning via automatic induction and application of cognitive reasoning schemas. CIRF abstracts first-order logic patterns from raw text into multi-relational schema graphs in an unsupervised manner, and leverages a schema-enhanced graph kernel model to align input structures with schema templates for robust, interpretable zero-shot inference. Extensive experiments on SemEval-2016, VAST, and COVID-19-Stance benchmarks demonstrate that CIRF not only establishes new state-of-the-art results, but also achieves comparable performance with just 30% of the labeled data, demonstrating its strong generalization and efficiency in low-resource settings. For reproducibility, we will release the code upon acceptance.

Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning

Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adversarial attack frameworks face certain limitations, e.g., impracticality due to the requirement of white-box information or high control authority, and a lack of stealthiness or effectiveness as they often target all agents or specific fixed agents. To address these issues, we propose AdapAM, a novel framework for adversarial attacks on black-box MAS. AdapAM incorporates two key components: (1) Adaptive Selection Policy simultaneously selects the victim and determines the anticipated malicious action (the action would lead to the worst impact on MAS), balancing effectiveness and stealthiness. (2) Proxy-based Perturbation to Induce Malicious Action utilizes generative adversarial imitation learning to approximate the target MAS, allowing AdapAM to generate perturbed observations using white-box information and thus induce victims to execute malicious action in black-box settings. We evaluate AdapAM across eight multi-agent environments and compare it with four state-of-the-art and commonly-used baselines. Results demonstrate that AdapAM achieves the best attack performance in different perturbation rates. Besides, AdapAM-generated perturbations are the least noisy and hardest to detect, emphasizing the stealthiness.

Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single‑cell RNA‑seq and spatial transcriptomic benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene‑expression signatures, further validating the biological relevance of our approach.

Refinement Contrastive Learning of Cell–Gene Associations for Unsupervised Cell Type Identification

Video restoration (VR) aims to recover high-quality videos from degraded ones. Although recent zero-shot VR methods using pre-trained diffusion models (DMs) show good promise, they suffer from approximation errors during reverse diffusion and insufficient temporal consistency. Moreover, dealing with 3D video data, VR is inherently computationally intensive. In this paper, we advocate viewing the reverse process in DMs as a function and present a novel Maximum a Posterior (MAP) framework that directly parameterizes video frames in the seed space of DMs, eliminating approximation errors. We also introduce strategies to promote bilevel temporal consistency: semantic consistency by leveraging clustering structures in the seed space, and pixel-level consistency by progressive warping with optical flow refinements. Extensive experiments on multiple virtual reality tasks demonstrate superior visual quality and temporal consistency achieved by our method compared to the state-of-the-art.

Temporal-Consistent Video Restoration with Pre-trained Diffusion Models

Video question answering (VideoQA), whose goal is to produce answers through the integration of linguistic and visual understanding, has emerged as a significant research focus. 
Although Large Multimodal Models (LMMs) and autonomous agent methods have achieved notable advances in VideoQA, excessive computational overhead and restricted multimodal interaction capabilities limit their ability to facilitate the continuous evolution of the VideoQA system. 
To address the challenge, we introduce DigimonGPT, an evolvable VideoQA agent inspired by cognitive psychology.
Specifically, DigimonGPT integrates a multimodal memory mechanism to achieve the continuous evolution of VideoQA systems. 
An intra-video declarative memory contains fundamental features of the video and semantic contexts extracted from historical QA pairs.
Another inter-task procedural memory encodes task-solving experience for further question answering.
Additionally, we introduce a hierarchical memory replay mechanism for VideoQA that selects appropriate memories by their relevance and question complexity.
Extensive experiments demonstrate that DigimonGPT's accuracy averagely outperforms 13.71\% on NExT-QA datasets and 9.89\% on Intent-QA datasets over LMM and autonomous agents.

Downloads

Next from AAAI 2026

CMID: Towards Medical Visual Question Answering via Contrastive Mutual Information Decoding

Similar lecture

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2026

CMID: Towards Medical Visual Question Answering via Contrastive Mutual Information Decoding

Similar lecture

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

Downloads