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Multi-modal imbalanced cross-source entity alignment aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs) that encompass diverse data sources with imbalanced modality, which poses significant challenges due to the non-uniform distribution of information across different modalities. Existing methods encounter major limitations in aligning entities across MMKGs, where missing data and modality-specific inconsistencies thus create information gaps. These gaps, stemming from disparities in neighborhood structure and attribute availability, result in reduced alignment performance. To address these challenges, we propose a novel multi-modal fact knowledge generation framework to advance imbalanced cross-source entity alignment. Utilizing large language models (LLMs) for comprehensive knowledge completion, our framework enriches MMKGs by synthesizing missing neighboring entities and relational attributes, enabling precise one-to-one similarity comparisons across all relations and attributes. Specifically, neighbor entity completion generates probable neighboring entities to fill structural gaps, while attribute completion synthesizes missing relational attributes to improve alignment. The facts evaluation module assesses generated triples, ensuring that only high-quality information supports the alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms strong competitors, achieving superior entity alignment performance.

AAAI 2026

Multi-Modal Fact Knowledge Generation for Imbalanced Cross-Source Entity Alignment

fact knowledge generation

multi-modal knowledge graph

entity alignment

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technical paper

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Dense retrieval models commonly use flat indexes to achieve high-precision retrieval by computing exact distances between embedding vectors. However, flat indexes are memory-intensive and inefficient, limiting their scalability in large-scale retrieval tasks. In contrast, quantized indexes enable faster retrieval with significantly lower memory usage, but their accuracy tends to decrease. Therefore, we propose a scalable and efficient training method for the dual-encoder models to improves the retrieval accuracy on quantized indexes. Our approach combines the direct gradient update to the cached target embeddings with large scale negative sampling based on similarity, significantly reducing computational overhead and GPU memory usage. Target embeddings are initialized with a pre-trained encoder and stored in a memory buffer, which is directly updated via backpropagation, thus avoiding the repeated re-encoding of the full corpus. To build a rich set of negatives, we retrieve the top-$k$ most similar targets for each query from cached embeddings using the quantized index, including both query-specific and cross-batch top-$k$ results. This design effectively approximates the truncated softmax distribution. The experiments show that our method achieves performs exceptionally well on quantized indexes, providing a practical and scalable solution for real-world retrieval systems.

Improving the Accuracy of Dense Retrieval on the Quantized Indexes via Gradient Optimization of the Target Embeddings

Graph neural networks (GNNs) have shown promise on combinatorial problems such as \textsc{Max-Clique}, yet it remains unclear what algorithmic principles they actually learn. This paper introduces a concept-driven framework for evaluating and interpreting GNNs on such tasks. We begin with a principled benchmark based on synthetic graphs with known difficulty levels—easy, medium, and hard—derived from theoretical thresholds for planted cliques. Using this setup, we show that GNNs reliably learn a simple yet powerful concept: degree-based ranking. This insight motivates a new decoder, Least-Probable Removal (LPR), which significantly outperforms the common top-$k$ strategy, especially on harder and real-world instances. Our analysis pipeline connects latent representations to classical heuristics, improving both interpretability and performance. Finally, we demonstrate cross-domain generalization to sparse PCA, showing that the same GNN architecture and decoding strategy succeed in recovering sparse principal components, revealing a shared underlying principle across domains.

Learning to Rank: How GNNs Solve Max-Clique and Sparse PCA

The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods often rely on resource-heavy Large Language Models (LLMs) or static knowledge graphs and struggle with two fundamental challenges in real-world clinical data: (1) missing modalities, such as incomplete clinical context , and (2) feature entanglement, where mixed modality-specific and shared information leads to suboptimal fusion and clinically unfaithful hallucinated findings. To address these challenges, we propose the DiA-gnostic VLVAE, which achieves robust radiology reporting through Disentangled Alignment. Our framework is designed to be resilient to missing modalities by disentangling shared and modality-specific features using a Mixture-of-Experts (MoE) based Vision-Language Variational Autoencoder (VLVAE). A constrained optimization objective enforces orthogonality and alignment between these latent representations to prevent suboptimal fusion. A compact LLaMA-X decoder then uses these disentangled representations to generate reports efficiently. On the IU X-Ray and MIMIC-CXR datasets, DiA has set new state-of-the-art BLEU@4 scores of 0.266 and 0.134, respectively. Experimental results show that the proposed method significantly outperforms state-of-the-art models.

DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities

Spiking Neural Networks (SNNs), with their brain-inspired spatiotemporal dynamics and spike-driven computation, have emerged as promising energy-efficient alternatives to Artificial Neural Networks (ANNs). However, existing SNNs typically replicate inputs directly or aggregate them into frames at fixed intervals. Such strategies lead to neurons receiving nearly identical stimuli across time steps, severely limiting the model's expressive power—particularly in complex tasks like object detection. In this work, we propose the Temporal Dynamics Enhancer (TDE) to strengthen SNNs' capacity for temporal information modeling. TDE consists of two modules: a Spiking Encoder (SE) that generates diverse input stimuli across time steps, and an Attention Gating Module (AGM) that guides the SE generation based on inter-temporal dependencies. Moreover, to eliminate the high-energy multiplication operations introduced by the AGM, we propose a Spike-Driven Attention (SDA) to reduce attention-related energy consumption. Extensive experiments demonstrate that TDE can be seamlessly integrated into existing SNN-based detectors and consistently outperforms state-of-the-art methods, achieving mAP@50-95 scores of 57.7% on the static PASCAL VOC dataset and 47.6% on the neuromorphic EvDET200K dataset. In terms of energy consumption, the SDA consumes only 0.240× the energy of conventional attention modules.

Temporal Dynamics Enhancer for Directly Trained Spiking Object Detectors

Timely detection of retinal diseases is crucial for prevent- ing vision loss; yet the limited availability of ophthalmolo- gists and disparities in access to diagnostic services continue to hinder widespread screening, particularly in primary care settings. We present REMEDIS, a Software-as-a-Service (SaaS)–based clinical AI framework for the automated diag- nosis of major retinal diseases, including age-related macu- lar degeneration (AMD), diabetic retinopathy (DR), epireti- nal membrane (ERM), and glaucoma, using fundus images. The system analyzes high-resolution fundus photographs in a secure cloud environment via a Swin-Large–based multi- disease classification network, producing disease-specific probability scores. To ensure clinically meaningful decision- making, Youden’s Index is applied to determine optimized sensitivity–specificity thresholds for each condition. An ex- plainability module based on Grad-CAM generates lesion- localization contour visualizations, providing interpretable evidence that assists ophthalmologists in case review and fa- cilitates integration into electronic medical records (EMR). The framework was evaluated in an IRB-approved multi- center prospective clinical trial conducted under real-world conditions, achieving an average AUC exceeding 0.94 across the four target diseases and demonstrating strong concor- dance with expert diagnoses. To our knowledge, this repre- sents one of the first SaaS-based AI diagnostic frameworks for retinal diseases validated through prospective clinical studies, highlighting its potential as an emerging clinical ap- plication of AI.

REMEDIS: A Clinical AI Framework for Retinal Disease Diagnosis with Explainable Fundus Image Analysis

Recommender systems are widely required and deployed to address real-world problems. In this paper, we study a new yet challenging real-world setting for recommender systems, where only user browsing histories are available without any explicit feedback. No item acquisition information, e.g., purchasing or rating, is given. By assuming that user browsing sequences are likely to contain the items to acquire, we draw an analogy to the setting of partial label learning in weakly supervised learning. This enables us to train reliable recommender systems only using browsing histories. We term the proposed method as Partial Acquisition Recommender System (PARS). Empirical results on real-world benchmark datasets show the effectiveness of the proposed method. Surprisingly, we also show that the proposed method even surpasses some baselines using item acquisition information.

PARS: Partial-Label-Learning-inspired Recommender Systems

Diffusion-based generative models have demonstrated remarkable capabilities in image synthesis, yet realistic hand generation remains a persistent challenge due to complex articulations, self-occlusion, and the lack of explicit structural guidance. 
To address these issues, we present SGMHand, a novel structure-guided hand inpainting framework that explicitly injects topological priors to enhance structural fidelity and spatial precision. 
Specifically, we present a structure-guided modulation (SGM) module that synergistically combines structure spatial attention with global feature calibration, enabling fine-grained geometric control over the generative process. 
Then, we devise a keypoint-aware (KA) loss that enforces topological coherence by aligning attention activations with structures, thereby bridging the gap between high-level semantics and low-level geometry. 
By jointly optimizing over structural constraints in both representation and learning objectives, SGMHand achieves semantically consistent and geometrically plausible hand synthesis, even under severe occlusion. 
Extensive experiments demonstrate the effectiveness and strong generalization ability of SGMHand across various foundation models, significantly enhancing the quality and realism of human image synthesis in diverse scenarios.
Code and models will be publicly available.

SGMHand: Structure-Guided Modulation for Structure-Aware Hand Inpainting

We study a class of two-player zero-sum Colonel Blotto games in which, after allocating soldiers across battlefields, players engage in (possibly distinct) normal-form games on each battlefield. Per-battlefield payoffs are parameterized by the soldier allocations. This generalizes the classical Blotto setting, where outcomes depend only on relative soldier allocations. We consider both discrete and continuous allocation models and examine two types of aggregate objectives: linear aggregation and worst-case battlefield value. For each setting, we analyze the existence and computability of Nash equilibrium. The general problem is not convex-concave, which limits the applicability of standard convex optimization techniques. However, we show that in several settings it is possible to reformulate the strategy space in a way where convex-concave structure is recovered. We evaluate the proposed methods on synthetic and real-world instances inspired by security applications, suggesting that our approaches scale well in practice.

Colonel Blotto with Battlefield Games

Sequential recommendation (SR) aims to predict users' next action based on their historical behavior, and is widely adopted by a number of platforms. The performance of SR models relies on rich interaction data. However, in real-world scenarios, many users only have a few historical interactions, leading to the problem of data sparsity. Data sparsity not only leads to model overfitting on sparse sequences, but also hinders the model’s ability to capture the underlying hierarchy of user intents. This results in misinterpreting the user's true intents and recommending irrelevant items. Existing data augmentation methods attempt to mitigate overfitting by generating relevant and varied data. However, they overlook the problem of reconstructing the user's intent hierarchy, which is lost in sparse data. Consequently, the augmented data often fails to align with the user's true intents, potentially leading to misguided recommendations. To address this, we propose the Adaptive Diffusion Augmentation for Recommendation (ADARec) framework. Critically, instead of using a diffusion model as a black-box generator, we use its entire step-wise denoising trajectory to reconstruct a user's intent hierarchy from a single sparse sequence. To ensure both efficiency and effectiveness, our framework adaptively determines the required augmentation depth for each sequence and employs a specialized mixture-of-experts architecture to decouple coarse- and fine-grained intents. Experiments show ADARec outperforms state-of-the-art methods by 2-9\% on standard benchmarks and 3-17\% on sparse sequences, demonstrating its ability to reconstruct hierarchical intent representations from sparse data. Our code is available at: https://anonymous.4open.science/r/ADARec-6C8D.

De-collapsing User Intent: Adaptive Diffusion Augmentation with Mixture-of-Experts for Sequential Recommendation

Visualizations play a crucial part in effective communication of concepts and information. Recent advances in reasoning and retrieval augmented generation have enabled Large Language Models (LLMs) to perform deep research and generate comprehensive reports. Despite its progress, existing deep research frameworks primarily focus on generating text-only content, leaving the automated generation of interleaved texts and visualizations underexplored. This novel task poses key challenges in designing informative visualizations and effectively integrating them with text reports. To address these challenges, we propose Formal Description of Visualization (FDV), a structured textual representation of charts that enables LLMs to learn from and generate diverse, high-quality visualizations. Building on this representation, we introduce Multimodal DeepResearcher, an agentic framework that decomposes the task into four stages: (1) researching, (2) exemplar report textualization, (3) planning and (4) multimodal report generation. For the evaluation of the generated reports, we develop MultimodalReportBench which contains 100 diverse topics as inputs, and a set of dedicated metrics for report and chart evaluation. Extensive experiments across models and evaluation methods demonstrate the effectiveness of Multimodal DeepResearcher. Notably, utilizing the same Claude 3.7 Sonnet model, Multimodal DeepResearcher achieves an 82\% overall win rate over the baseline method.

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Improving the Accuracy of Dense Retrieval on the Quantized Indexes via Gradient Optimization of the Target Embeddings

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