Singapore

Capturing accurate dynamic information of moving organs is essential for functional assessment using non-invasive imaging modalities. Achieving high temporal resolution visualization of physiological processes remains a critical challenge in dynamic magnetic resonance imaging (MRI) when reconstructing from extremely limited acquisitions. We introduce an unsupervised zero-shot reconstruction framework combining Implicit Neural Representation (INR) with manifold learning, capable of reconstructing dynamic MRI data at unprecedented temporal resolutions (less than 10 ms per frame for 2D imaging, less than 400 ms per frame for 3D imaging). 
The framework employs learnable low-dimensional manifold vectors to autonomously capture motion in real time directly from undersampled data, and dynamically condition coordinate-based spatial representations to generate high-fidelity image sequences.
Through a novel spatiotemporal coarse-to-fine (C2F) optimization strategy, our method outperforms current state-of-the-art (SOTA) techniques across multiple imaging scenarios, including cardiac, speech and dynamic-contrast-enhanced (DCE) abdominal MRI, demonstrating robust performance under challenging motion patterns and contrast dynamics.
The learned manifolds additionally provide intuitive visualization of motion and contrast evolution during imaging.
These advances indicate strong clinical potential for applications requiring extreme temporal resolution while maintaining both anatomical and temporal fidelity.

AAAI 2026

Zero-shot Implicit Neural Manifold Representation (INMR) for Ultra-high Temporal Resolution Dynamic MRI

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.

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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.

Feature attribution has gained prominence as a tool for explaining model decisions, yet evaluating explanation quality remains challenging due to the absence of ground-truth explanations. To circumvent this, explanation-guided input manipulation has emerged as an indirect evaluation strategy, measuring explanation effectiveness through the impact of input modifications on model outcomes during inference. Despite the widespread use, a major concern with inference-based schemes is the distribution shift caused by such manipulations, which undermines the reliability of their assessments. The retraining-based scheme ROAR overcomes this issue by adapting the model to the altered data distribution. However, its evaluation results often contradict the theoretical foundations of widely accepted explainers. 
This work investigates this misalignment between empirical observations and theoretical expectations. In particular, we identify the Sign issue as a key factor responsible for residual information that ultimately distorts retraining-based evaluation. Based on the analysis, we show that a straightforward reframing of the evaluation process can effectively resolve the identified issue. Building on the existing framework, we further propose novel variants that jointly structure a comprehensive perspective on explanation evaluation. These variants largely improve evaluation efficiency over the standard retraining protocol, thereby enhancing practical applicability for explainer selection and benchmarking. Following our proposed schemes, empirical results across various data scales provide deeper insights into the performance of carefully selected explainers, revealing open challenges and future directions in explainability research.

Rethinking Explanation Evaluation Under the Retraining Scheme

Retrieval-Augmented Generation (RAG) is an effective solution to overcome the limitations of Large Language Models (LLMs) in terms of specific-domain knowledge and timely information updates. However, current RAG methods typically respond to queries based on isolated segments, lacking the ability to integrate information within the same document. This undermines performance in real-world tasks requiring coherent understanding across an entire document. Notably, the human brain naturally integrates and summarizes prior knowledge upon reading a given text, progressively formulating a comprehensive understanding. Motivated by this cognitive process, we propose the Hierarchical Two-Stage Summarization-based Information Retrieval (HTSIR) method, which preprocesses the corpus prior to retrieval, summarizes continuous texts to obtain integrated information, and constructs a retrieval tree with varying summary granularities. The retrieved information is then processed by a Reranker based on the current question to serve as a context for LLMs. Additionally, as single-step summarization is often imprecise in query-based summarization tasks, we further apply a Refinement module, allowing LLMs to reflect and revise their output to achieve the final result. By combining HTSIR with GPT-4o mini, we achieve state-of-the-art results on complex question tasks across four long-text datasets (NarrativeQA, QASPER, QuALITY, and QMSum), achieving a notable improvement of 6 points on Question Answering (QA) task in QuALITY-HRAD.

Improving Long-Context Summarization with Multi-Granularity Retrieval Optimization

Amid growing demands for data privacy and advances in computational infrastructure, federated learning (FL) has emerged as a prominent distributed learning paradigm. Nevertheless, differences in data distribution (such as covariate and semantic shifts) severely affect its reliability in real-world deployments. To address this issue, we propose FedSDWC, a causal inference method that integrates both invariant and variant features. FedSDWC infers causal semantic representations by modeling the weak causal influence between invariant and variant features, effectively overcoming the limitations of existing invariant learning methods in accurately capturing invariant features and directly constructing causal representations. This approach significantly enhances FL's ability to generalize and detect OOD data. Theoretically, we derive FedSDWC's generalization error bound under specific conditions and, for the first time, establish its relationship with client prior distributions. Moreover, extensive experiments conducted on multiple benchmark datasets validate the superior performance of FedSDWC in handling covariate and semantic shifts. For example, FedSDWC outperforms FedICON, the next best baseline, by an average of 3.04% on CIFAR-10 and 8.11% on CIFAR-100.

FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD

Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption.

Spiking Heterogeneous Graph Attention Networks

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.

MTRL-CG: Multi-Task Reinforcement Learning Method with Spectral Clustering-Based Task Grouping

Streaming video question answering (Streaming Video QA) poses distinct challenges for multimodal large language models (MLLMs), as video frames arrive sequentially and user queries can be issued at arbitrary timepoints. Existing solutions relying on fixed-size memory or naive compression often suffer from context loss or memory overflow, limiting their effectiveness in long-form, real-time scenarios.We present Vista, a novel framework for scene-aware streaming video QA that enables efficient and scalable reasoning over continuous video streams. The innovation of Vista can be summarized in three aspects: (1) Scene-aware segmentation. Vista dynamically clusters incoming frames into temporally and visually coherent scene units. (2) Scene-aware compression. Each scene is compressed into a compact token representation and stored in GPU memory for efficient index-based retrieval, while the full-resolution frames are offloaded to CPU memory. (3) Scene-aware recall. Upon receiving a question, relevant scenes are selectively recalled and reintegrated into the model’s input space, enabling both efficiency and completeness. Vista is model-agnostic and integrates seamlessly with a variety of vision-language backbones, enabling long-context reasoning without compromising latency or memory efficiency. Extensive experiments on StreamingBench demonstrate that Vista achieves state-of-the-art performance, establishing a strong baseline for real-world streaming video understanding.

Vista: Scene-Aware Optimization for Streaming Video Question Answering Under Post-Hoc Queries

Continual learning methods used to force neural networks to process sequential tasks in isolation, preventing them from leveraging useful inter-task relationships and causing them to repeatedly relearn similar features or overly differentiate them. To address this problem, we propose a fully differentiable, exemplar-free expandable method composed of two complementary memories: One learns common features that can be used across all tasks, and the other combines the shared features to learn discriminative characteristics unique to each sample. Both memories are differentiable so that the network can autonomously learn latent representations for each sample. For each task, the memory adjustment module adaptively prunes critical slots and minimally expands capacity to accommodate new concepts, and orthogonal regularization enforces geometric separation between preserved and newly learned memory components to prevent interference. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet show that the proposed method outperforms 14 state-of-the-art methods for class-incremental learning, achieving final accuracies of 55.13\%, 37.24\%, and 30.11\%, respectively. Additional analysis confirms that, through effective integration and utilization of knowledge, the proposed method can increase average performance across sequential tasks, and it produces feature extraction results closest to the upper bound, thus establishing a new milestone in continual learning.

Expandable and Differentiable Dual Memories with Orthogonal Regularization for Exemplar-free Continual Learning

Personalized Federated Learning (PFL) customizes models for each client to mitigate challenges from non-IID data, wherein a dominant strategy is model decoupling that partitions models into shared and personalized parts based on architectural priors (e.g., backbone vs. head). However, we reveal a critical flaw in this strategy: it induces "intrinsic drift," a performance degradation often more severe than the well-known client drift, which limits final accuracy. We trace this drift to a steep cliff of high loss emerging from the naive stitching of shared and personalized parts. To address this, we shift from architectural partitioning to a parameter behavior-driven paradigm. We introduce PPFL, an approach that employs a novel soft-fusion strategy guided by parameter-wise behavioral perception. PPFL dynamically infers each parameter's functional role—whether it behaves more like a 'personalist' or a 'generalist' in the current context—by synthesizing its multifaceted behavior observed during local training. Extensive experiments on image, text, and multimodal classification benchmarks show that PPFL outperforms eight state-of-the-art baselines by up to 5.3\%. Moreover, it can function as a plug-in module, boosting the accuracy of vanilla FedAvg with a 16.82\% absolute gain.

PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning

Graph-based vertical federated learning (GVFL) enables collaboration by incorporating scattered attributes and adjacency relations from aligned nodes, and allows each party to contribute its personalized input embedding to joint training and inference. The injection of adversarial inputs can mislead the inference towards attacker’s will, forcing other benign parties to make negligible contributions and losing rewards regarding the importance of their contributions. However, most attacks require server model architectures, queries, or labeled auxiliary graphs from the training domain. These extra requirements are not practical for real-world GVFL applications. In this paper, we propose SGAC, a novel attack framework for crafting adversarial inputs to dominate joint inference without relying on such above requirements. SGAC advances prior attacks by requiring only access to auxiliary graphs from non-training domains. SGAC learns generalized label-indicative embeddings and estimates class-transferable probabilities across domains to generate a surrogate model that closely approximates the server model. SGAC then emphasizes salient node attributes and edges in the auxiliary graph, creating a diverse shadow input set that resembles influential test inputs. With surrogate fidelity and input diversity, SGAC crafts transferable adversarial inputs. Evaluation on diverse model architectures confirms the effectiveness of SGAC.

Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning

Task decomposition has shown promise in complex cooperative multi-agent reinforcement learning (MARL) tasks, which enables efficient hierarchical learning for long-horizon tasks in dynamic and uncertain environments. However, learning dynamic task decomposition from scratch generally requires a large number of training samples, especially exploring the large joint action space under partial observability. In this paper, we present the Conditional Diffusion Model for Dynamic Task Decomposition (C$\text{D}^\text{3}$T), a novel two-level hierarchical MARL framework designed to automatically infer subtask and coordination patterns. The high-level policy learns subtask representation to generate a subtask selection strategy based on subtask effects. To capture the effects of subtasks on the environment, C$\text{D}^\text{3}$T predicts the next observation and reward using a conditional diffusion model. At the low level, agents collaboratively learn and share specialized skills within their assigned subtasks. Moreover, the learned subtask representation is also used as additional semantic information in a multi-head attention mixing network to enhance value decomposition and provide an efficient reasoning bridge between individual and joint value functions. Experimental results on various benchmarks demonstrate that C$\text{D}^\text{3}$T achieves better performance than existing baselines.

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