Singapore

While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs&#39; over-reliance on linguistic priors and insufficient visual feature integration, proposing heuristic decoding calibration strategies to mitigate them. However, the non-trainable nature of these strategies inherently limits their optimization potential. To this end, we propose an adversarial parametric editing framework for Hallucination mitigation in VLMs, which follows an Activate-Locate-Edit Adversarially paradigm. Specifically, we first construct an activation dataset that comprises grounded responses (positive samples attentively anchored in visual features) and hallucinatory responses (negative samples reflecting LLM prior bias and internal knowledge artifacts). Next, we identify critical hallucination-prone parameter clusters by analyzing differential hidden states of response pairs. Then, these clusters are fine-tuned using prompts injected with adversarial prefixes optimized via prompt tuning to maximize visual neglect, thereby forcing the model to prioritize visual evidence over inherent parametric biases. Evaluations on both generative and discriminative VLM tasks demonstrate the significant effectiveness of ALEAHallu in alleviating hallucinations. Our code is available at https://anonymous.4open.science/r/knowledge_editing-C890/

AAAI 2026

Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs

While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs' over-reliance on linguistic priors and insufficient visual feature integration, proposing heuristic decoding calibration strategies to mitigate them. However, the non-trainable nature of these strategies inherently limits their optimization potential. To this end, we propose an adversarial parametric editing framework for Hallucination mitigation in VLMs, which follows an Activate-Locate-Edit Adversarially paradigm. Specifically, we first construct an activation dataset that comprises grounded responses (positive samples attentively anchored in visual features) and hallucinatory responses (negative samples reflecting LLM prior bias and internal knowledge artifacts). Next, we identify critical hallucination-prone parameter clusters by analyzing differential hidden states of response pairs. Then, these clusters are fine-tuned using prompts injected with adversarial prefixes optimized via prompt tuning to maximize visual neglect, thereby forcing the model to prioritize visual evidence over inherent parametric biases. Evaluations on both generative and discriminative VLM tasks demonstrate the significant effectiveness of ALEAHallu in alleviating hallucinations. Our code is available at https://anonymous.4open.science/r/knowledge_editing-C890/

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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.

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.

Swarm UAV autonomous flight for Long-Horizon (LH) tasks is crucial for advancing the low-altitude economy. However, existing methods focus only on specific basic tasks due to dataset limitations, failing in real-world deployment for LH tasks. LH tasks are not mere concatenations of basic tasks, requiring handling long-term dependencies, maintaining persistent states, and adapting to dynamic goal shifts. This paper presents U2UData-2, the first large-scale swarm UAV autonomous flight dataset for LH tasks and the first scalable swarm UAV data online collection and algorithm closed-loop verification platform. The dataset is captured by 15 UAVs in autonomous collaborative flights for LH tasks, comprising 12 scenes, 720 traces, 120 hours, 600 seconds per trajectory, 4.32M LiDAR frames, and 12.96M RGB frames. This dataset also includes brightness, temperature, humidity, smoke, and airflow values covering all flight routes. The platform supports the customization of simulators, UAVs, sensors, flight algorithms, formation modes, and LH tasks. Through a visual control window, this platform allows users to collect customized datasets through one-click deployment online and to verify algorithms by closed-loop simulation. U2UData-2 also introduces an LH task for wildlife conservation and provides comprehensive benchmarks with 9 SOTA models.

U2UData+: A Scalable Swarm UAVs Autonomous Flight Dataset for Embodied Long-horizon Tasks

Split inference (SI) enables users to access deep learning (DL) services without directly transmitting raw data. However, recent studies reveal that data reconstruction attacks (DRAs) can recover the original inputs from the smashed data sent from the client to the server, leading to significant privacy leakage. While various defenses have been proposed, they often result in substantial utility degradation, particularly when the client-side model is shallow. We identify a key cause of this trade-off: existing defenses apply excessive perturbation to redundant information in the smashed data. To address this issue in computer vision tasks, we propose InfoDecom, a defense framework that first decomposes and removes redundant information and then injects noise calibrated to provide theoretically guaranteed privacy. Experiments demonstrate that InfoDecom achieves a superior utility-privacy trade-off compared to existing baselines.

InfoDecom: Decomposing Information for Defending Against Privacy Leakage in Split Inference

Zero-shot coordination(ZSC) has recently become a hot topic in reinforcement learning research recently. It focuses on the generalization ability of agents, requiring them to coordinate well with collaborators that are not seen before without any fine-tuning. Population-based training has been proven to provide good zero-shot coordination performance; nevertheless, existing methods are limited by computational resources, mainly focusing on optimizing diversity in small populations while neglecting the potential performance gains from scaling population size. To address this issue, this paper proposes the Scalable Population Training (ScaPT), an efficient training framework comprising two key components: a meta-agent that efficiently realizes a population by selectively sharing parameters across agents, and a mutual information regularizer that guarantees population diversity. To empirically validate the effectiveness of ScaPT, this paper evaluates it along with representational frameworks in Hanabi and confirms its superiority.

Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games

One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communication. However, most existing methods struggle to achieve robust performance on real-world domains such as medical imaging, or are inefficient when handling non-IID (Independent and Identically Distributed) data. To address these limitations, we introduce FALCON, a novel framework that enhances the effectiveness of OSFL over non-IID image data. The core idea of FALCON is to leverage the feature-aware hierarchical token sequences generation and knowledge distillation into OSFL. First, each client leverages a pretrained visual encoder with hierarchical scale encoding to compress images into hierarchical token sequences, which capture multi-scale semantics. Second, a multi-scale autoregressive transformer generator is used to model the distribution of these token sequences and generate the synthetic sequences. Third, clients upload the synthetic sequences along with the local classifier trained on the real token sequences to the server. Finally, the server incorporates knowledge distillation into global training to reduce reliance on precise distribution modeling. Experiments on medical and natural image datasets validate the effectiveness of FALCON in diverse non-IID scenarios, outperforming the best OSFL baselines by 9.58\% in average accuracy.

Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences

Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimizing hybrid discrete-continuous action space remains a fundamental challenge, mainly due to limited policy expressiveness and poor scalability in high-dimensional settings. 
To address this challenge, we view the hybrid action space problem as a fully-cooperative game and propose a \textbf{Cooperative Hybrid Diffusion Policies (CHDP)} framework to solve it.
CHDP employs two cooperative agents that leverage a discrete and a continuous diffusion policy, respectively.
The continuous policy is conditioned on the discrete action's representation, explicitly modeling the dependency between them.
This cooperative design allows the diffusion policies to leverage their expressiveness to capture complex distributions in their respective action spaces.
To mitigate the update conflicts arising from simultaneous policy updates in this cooperative setting, we employ a sequential update scheme that fosters co-adaptation.
Moreover, to improve scalability when learning in high-dimensional discrete action space, we construct a codebook that embeds the action space into a low-dimensional latent space. 
This mapping enables the discrete policy to learn in a compact, structured space. 
Finally, we design a Q-function-based guidance mechanism to align the codebook's embeddings with the discrete policy's representation during training.
On challenging hybrid action benchmarks, CHDP outperforms state-of-the-art method by up to $19.3\%$ in success rate.

CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space

In real-world time-series modelling, graph structures are widely adopted because they explicitly encode node topology and capture complex network dynamics. In practice, however, a complete graph is often partitioned across multiple parties; each party can access only its local sub-graph and, owing to privacy regulations, cannot share topology or data, creating pervasive data silos. Federated Graph Learning (FGL) offers a privacy-preserving collaborative-learning paradigm, yet current methods still face two key challenges: (1) they implicitly capture inter-edge information, making it difficult to accurately reconstruct the global structure and consequently degrading model performance; (2) explicitly exchanging inter-edge information may leak graph-topology privacy. To overcome these obstacles, we propose FedSkeleton, a privacy-preserving framework for time-series prediction that comprises a Skeleton Construction Module and a Dual-stream Forecasting Module, enabling global dependency capture without revealing the topology. Extensive experiments show that FedSkeleton consistently outperforms existing baselines and even surpasses centralised models with full-graph access. In addition, we conduct comprehensive security analysis, communication-cost evaluation and scalability experiments, demonstrating that FedSkeleton effectively resists common attacks, keeps communication overhead manageable and remains robust with respect to key hyper-parameters and the number of participating parties.

FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting

Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low-latency speech processing in real-time applications. In this paper, we propose SepPrune, the first structured pruning framework specifically designed to compress deep speech separation models and reduce their computational cost. SepPrune begins by analyzing the computational structure of a given model to identify layers with the highest computational burden. It then introduces a differentiable masking strategy to enable gradient-driven channel selection. Based on the learned masks, SepPrune prunes redundant channels and fine-tunes the remaining parameters to recover performance. Extensive experiments demonstrate that this learnable pruning paradigm yields substantial advantages for channel pruning in speech separation models, outperforming existing methods. Notably, a model pruned with SepPrune can recover 85% of the performance of a pre-trained model (trained over hundreds of epochs) with only one epoch of fine-tuning, and achieves convergence 36x faster than training from scratch.

SepPrune: Structured Pruning for Efficient Deep Speech Separation

The Mixture-of-Experts (MoE) architecture has emerged as a promising paradigm for scaling large language models (LLMs) by activating only a sparse subset of experts per input. However, its massive parameter size remains a major obstacle to efficient deployment. Existing pruning methods often ignore two key aspects: the intricate structural dependencies among experts and the heterogeneous importance of different layers. To tackle these issues, we propose C-GNN-PRUNE, a unified and structure-aware compression framework tailored for MoE models. Our method introduces an EntropyGuided Allocation Module that dynamically assigns pruning budgets by leveraging expert activation entropy, enabling adaptive handling of inter-layer heterogeneity. To preserve structural collaboration patterns, we construct an expert interaction graph that fuses functional similarity and routing behavior, and employ a GNN-Based Embedding Module to learn structure-aware expert representations. These embeddings, along with co-activation patterns, are fed into a Community Detection Module to identify expert clusters for structured pruning. Finally, an Activation-Aware Selection Module retains the most critical experts in each community, balancing sparsity and expressiveness. Experiments on multiple open-source MoE models demonstrate that C-GNN-PRUNE consistently outperforms prior methods under various pruning ratios, achieving better trade-offs between compression and accuracy. This framework provides a modular and effective solution for structure-preserving compression of large-scale MoE models.

C-GNN-PRUNE: A Unified Graph-Based Framework for Structure-Aware Pruning of Mixture-of-Experts Models

Tabular data synthesis is a key technique for protecting data privacy and addressing class imbalance, yet existing generative models struggle to capture the complex intrinsic structure of the data. To overcome this limitation, we propose TabGeoFlow, a novel geometric flow matching model for tabular data synthesis. The core innovation of TabGeoFlow is the injection of an explicit geometric inductive bias into the conditional flow matching framework. We decompose the learned vector field into local tangent and normal components of the data manifold. By dynamically suppressing the predicted normal component via a controlling loss function, we constrain the generative path to follow the data's intrinsic structure. Implemented with a shared backbone for parameter efficiency, TabGeoFlow outperforms existing baseline methods across multiple benchmark datasets, demonstrating superior data quality in terms of both statistical similarity and downstream machine learning utility.

TabGeoFlow: A Geometric Flow Matching Model for Tabular Data Synthesis

Antibody design is critically important in biomedical and therapeutic contexts but remains extremely challenging due to the complexity of antibody sequence–structure relationships and stringent antigen specificity requirements. Traditional computational approaches rely on multi-stage pipelines and often overlook all-atom details (e.g., side-chain conformations) as well as fine-grained geometric features, resulting in limited effectiveness. To overcome these limitations, we propose Dynamic Geometric Equivariant Network (DGENet), an end-to-end all-atom antibody design model that integrates a geometric-kinematic equivariant dynamic optimization module (GK-EDO) with an all-atom E(3)-equivariant message-passing architecture. This framework enables iterative optimization of antibody structures under explicit geometric and kinematic constraints, generating complete antibody structures (including backbone and side chains) and simultaneously jointly optimizing the sequences and 3D structures of the complementarity-determining regions (CDRs). DGENet also introduces a novel virtual anchor docking mechanism that employs an adaptive PNet-Kabsch module to explicitly guide antibody–antigen binding and achieve precise bound conformations. Evaluations on multiple benchmark datasets demonstrate that DGENet exhibits outstanding performance in antibody structure and sequence generation as well as in designing high-affinity antibodies, underscoring its reliability as an advanced antibody design model.

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