IJCAI 2026 论文列表

Extracting multi-step explanations from knowledge graphs poses a combinatorial challenge requiring both heuristic guidance (as candidates proliferate with depth) and credit assignment (as path quality emerges over extended sequences). Frontier LLMs, strong on knowledge/reasoning benchmarks, offer a compelling source of such heuristics, yet their knowledge comes sans guarantees and compositional performance degrades as chains lengthen. We thus present TESSERA, a 3-part neuro-symbolic framework that uses LLMs in a circumscribed role: for local discriminative judgement rather than autonomous multi-step generation; the knowledge graph then defines the hypothesis space enforcing hard structural constraints, and MCTS coordinates the long-horizon search with principled credit assignment via backpropagation. LLMs perform dual roles as a prior policy biasing exploration and a comparative state evaluator supplying reward signals. Evaluation on drug mechanism elucidation across two complementary knowledge graphs demonstrates fidelity to curated biology while surfacing coherent alternative mechanisms, with ablations confirming discriminative contribution from both LLM components. Beyond its current application, our framework offers a general paradigm for compositional reasoning over structured knowledge.

Medical image quality assessment (IQA) serves as a critical safeguard for precise clinical diagnosis and treatment. However, existing methods still face challenges arising from data scarcity and heterogeneity across imaging domains, which confine solutions to domain-specific designs and limit their cross-domain generalization ability. In response, we construct a multi-domain and multi-organ dataset comprising 9,105 2D and 3D medical images across three imaging domains and 18 organs, annotated by radiologists. Building upon this, we propose a cross-domain universal medical IQA method termed CDMIQA, which integrates efficient feature extractors with Hierarchical Perceptual Encoding Modules to capture and refine multi-level perceptual features while mitigating interference from noise and artifacts. Notably, the Adaptive Semantic Perception Module is designed to extract semantic features; it enhances adaptability to domain-specific images by dynamically encoding quality variations across different imaging domains and organs. Furthermore, a Top-down Feature Fusion Module is utilized to progressively aggregate features under the guidance of semantic information, reinforcing the model's feature representation capability. Experimental results on the proposed dataset demonstrate that our method outperforms standard competitors and exhibits superior generalization ability across diverse domains. Our code and dataset are available at https://github.com/Leilei-Huang-work/CDMIQA.

Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical alignment metric that quantifies the correspondence between the surrogate model’s key tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.

Accurate volumetric medical image segmentation is critical for clinical diagnosis, yet adapting two-dimensional vision foundation models (VFMs) to three-dimensional medical imaging remains challenging. While parameter-efficient fine-tuning (PEFT) methods such as LoRA and adapter-based schemes provide efficient alternatives to full fine-tuning, their geometry-agnostic parameter-space adaptations are insufficient to reconcile discrepancies induced by anisotropic voxel spacing and inter-slice discontinuities. We propose GeoSFLoRA, a geometry-constrained spectral-flow low-rank adaptation framework for efficient 2D-to-3D transfer learning. GeoSFLoRA adapts frozen 2D pretrained vision backbones by operating within a fixed low-rank spectral subspace induced by pretrained linear projections. For each adapted layer, a truncated singular value decomposition is computed once and kept frozen, preserving the original pretrained bases. A lightweight Geometry-Conditioned Encoder (GCE) extracts local volumetric descriptors, which are mapped to token-conditional residuals in the singular-value space, enabling bounded and geometry-aware spectral modulation. GeoSFLoRA consistently improves Dice and HD95 on BraTS20, MSD-Prostate, and MSD-Lung, approaching full fine-tuning performance and demonstrating an effective paradigm for 2D-to-3D medical image segmentation. The code is publicly available at https://github.com/chenbn266/GeoSFLoRA.

Federated learning (FL) enables privacy-preserving collaboration for medical image analysis across decentralized institutions but faces major challenges from non-IID data distributions, high communication overhead in multi-round protocols, and catastrophic forgetting when models must adapt to sequentially arriving tasks. These issues are particularly critical in healthcare, where repeated client participation is often infeasible. We address this setting by proposing a novel class-incremental continual learning (CL) model for a one-shot FL paradigm, in which each task introduces new classes, clients observe heterogeneous and evolving class distributions, and communication with the server occurs only once. Clients estimate class-conditional feature distributions via Variational Inference (VI) from private data and transmit compact statistics to the server, which synthesizes features to train a global classifier in a single communication round. The server aggregates these distributional summaries, synthesizes feature embedding, and learns a global classifier without revisiting real past data or contacting clients again. Our major novelty is continual adaptation at the distribution level via synthetic replay from stored class mixtures, complemented by lightweight distillation. This approach substantially mitigates catastrophic forgetting while consistently enhancing recognition of newly introduced classes. Extensive experiments on multiple medical imaging benchmarks demonstrate that our method outperforms both state-of-the-art one-shot FL and class-incremental CL approaches. Compared with existing CL models, it achieves 90–97% average accuracy with near-zero forgetting (≤ 0.10%) across different tasks and evaluation settings.

Integrating heterogeneous clinical modalities, structured electronic health records (EHRs), clinical text, and medical imaging is crucial for reliable clinical prediction, yet real-world data are often sparse and imbalanced. Furthermore, prior approaches treat temporal dynamics and inter-patient relationships in isolation, overlooking the dynamic interaction of patient trajectories across populations. We introduce a modality-enhanced dynamic temporal relational graph (MedFiTRG), a unified framework that jointly models sparsity, temporal dynamics, and cross-patient relational dependencies. MedFiTRG leverages modulated graph neural networks (MGNN) to learn modality-aware embeddings, enabling meaningful representation of sparse modalities through adaptive feature modulation. These embeddings are integrated into a temporal relational graph (TRG), where directed intra-patient edges capture longitudinal progression and dynamic inter-patient edges model population-level similarities for synchronized temporal-relational reasoning. Extensive experiments on large-scale real-world datasets across four clinical tasks demonstrate that MedFiTRG achieves superior or comparable performance against state-of-the-art baselines, improving Macro-F1 from 0.155 to 0.310 for length of stay (LOS) classification and achieving an AUROC of 0.939 for mortality prediction (↑6.45%). The code is available at https://anonymous.4open.science/r/MedFiTRG-2714

3D dental segmentation is a key task in digital dentistry. In real intraoral scans data (IOS), occlusion, scanning noise, and reconstruction artifacts often break down the geometric separation structure between teeth, resulting in adjacent teeth being incorrectly merged or a single tooth being over-segmented. Since existing point cloud or mesh-based methods usually rely on local neighborhood consistency, when there are spurious geometric connections, features will diffuse across instances along geometric shortcuts, resulting in instance-level error propagation. To address this issue, we propose VGDM (Visual-Guided Diffusion Modulation), which serves as a bridge between extrinsic visual localization cues and intrinsic surface features under detection settings, enabling the extrinsic cues to regulate the propagation of intrinsic surface features. Instead of global propagation on the entire intraoral scan mesh, VGDM uses the single-view 2D detection results to roughly localize the tooth region and construct local 3D surface patches based on it. Within the patch, we soft-constrain feature propagation by visual cues to suppress the cross-instance propagation generated along spurious geometric connections, and introduce a dual-stream diffusion structure to improve the overall robustness. Experimental results on the largest public intraoral dataset(Teeth3DS) show that VGDM can significantly improve the segmentation rate of tooth instances and effectively reduce the merging and over-segmentation of adjacent teeth.

In gene expression analysis, high dimension low sample size data limits the applicability of deep learning, motivating increasing interest in variable-length evolutionary algorithm–based feature selection methods (VLEAs). However, existing VLEAs suffer from unreliable single-metric discrimination under dynamic search-space variation, mismatches between population size and search space dimensionality, and particle performance degradation after search space changes. To this end, a Multiscale-adaptive and Size-adaptive Particle Swarm Optimization (MASA-PSO) is proposed for gene expression analysis. MASA-PSO adopts a multiscale-adaptive weighting framework to explore feature subsets that distinguish between-class sample distributions during search spaces changes, and theoretically proves it enables the collaborative evaluation of multiple metrics. Meanwhile, it proposes an adaptive population division that explicitly models the functional relationship between the population size and the search space to resolve the mismatch. Furthermore, a particle degradation phenomenon impairing the performance of VLEAs is observed and alleviated through a hybrid elite strategy. Experiments on ten gene expression datasets verify that MASA-PSO outperforms state-of-the-art methods in classification accuracy while capturing smaller feature subsets.

Predicting drug-disease associations (DDAs) plays a crucial role in drug development and disease treatment. However, existing researches predominantly focus on single DDAs prediction task, often overlooking the intricate relationships among different tasks, which can further improve the performance of methods for DDAs prediction. To address this limitation, a multi-task prediction framework, capable of simultaneously predicting drug-disease, drug-protein, and disease-protein associations, is proposed, named MTP-DDA. The framework constructs three distinct graphs to reflect different relationships between biological entities. Then, based on these graphs, two sub-views and one main-view are constructed. For sub-view, corruption strategy is adopted to generate corrupted view, and Graph Convolutional Network (GCN) is employed to extract features from both the original view and its corrupted version, with contrastive learning applied to enhance feature representations. For main-view, GCN and Node2Vec are utilized to extract low-order and high-order node features respectively, and an attention mechanism is utilized for feature fusion. Finally, the node features from three views above are integrated, and the dot product operation is applied to the node features of association pairs to derive association scores, thereby enabling multi-task association prediction. Under 10-fold cross-validation, the proposed framework outperforms current methods on public datasets, demonstrating its effectiveness and robustness.

Self-supervised learning (SSL) is now a standard way to pretrain medical image models, but performance is still mostly judged by downstream accuracy. For safety-critical screening tasks such as diabetic retinopathy grading, this is not enough: a model must also know when its predictions are unreliable and defer uncertain cases for clinical review. In this work, we examine how the length of SSL pretraining influences confidence calibration and confidence-based abstention. We evaluate multiple SSL checkpoints under a fixed fine-tuning protocol and assess calibrated confidence, coverage, selective accuracy, and selective macro-F1. Across datasets and data regimes, SSL pretraining improves selective prediction compared to training from scratch. Unlike prior SSL studies that primarily evaluate downstream accuracy or AUROC, we analyze how SSL pretraining duration influences calibration and selective prediction behavior under confidence-based abstention. However, once accuracy saturates, selective performance can still change markedly across checkpoints, and longer pretraining does not consistently improve reliability. These results underscore the importance of abstention-aware evaluation and suggest that pretraining length should be treated as an important reliability-related design choice rather than only a computational detail. Code is available at https://github.com/29 muskaan712/ijcai-knowing-when-not-to-predict.

Despite being a cost-effective modality for volumetric imaging, freehand three-dimensional (3D) ultrasound produces inherently sparse data due to the significant elevational gaps left by tracked 2D sweeps. This sparsity poses a unique challenge for Implicit Neural Representations (INRs). While successful in other domains, INRs applied here tend to fail as the learned signed distance fields (SDF) collapse toward zero inside the object, leading to the loss of concavities and the incorrect closure of anatomical gaps. Our analysis identifies the root cause as a statistical bias in gradient-based sampling objectives, showing that symmetric volumetric sampling mathematically drives the expected SDF value to zero. To rectify this, we present a geometry-aware framework that explicitly anchors the non-negative half-space. Our method utilizes a boundary-directed exterior sampling strategy to ensure non-negative constraints in empty areas, complemented by an ellipsoid-based adversarial mechanism to regularize the global field distribution. Experiments on multiple anatomical datasets demonstrate that our approach mitigates field collapse and improves geometric fidelity and topological consistency on most metrics. Code is available at https://github.com/jiuanchen/Pulling-Fails-SDF.

Self-supervised pre-training has become a key paradigm for reducing annotation costs in 3D medical imaging, yet many recent approaches rely on complex objectives or incur substantial computational overhead. We propose a simple and efficient self-supervised pre-training framework for 3D medical images based on a two-fold patch-wise perturbation strategy. The method applies Bernoulli patch masking and discrete rotations, and trains a shared encoder with a three-head objective for reconstruction, perturbation localization, and rotation prediction. This design encourages spatially aware and transferable representations while remaining computationally lightweight. Experiments across diverse segmentation and classification benchmarks, including modality-shift scenarios, demonstrate consistent improvements over general self-supervised baselines and competitive or superior performance compared to recent medical self-supervised methods, while requiring substantially less memory, computation, and training time than the state-of-the-art pre-training pipelines.

Activated PI3Kδ Syndrome (APDS) is a rare genetic immune disorder caused by variants in PIK3CD or PIK3R1, with highly heterogeneous symptoms that often delay diagnosis. Early recognition is hampered by overlapping clinical presentations and limited clinician awareness, motivating systematic, data-driven approaches to detect APDS-associated phenotypic patterns in routine electronic health records. Traditional linear scoring systems cannot capture complex symptom interactions, while deep learning models, though expressive, often lack interpretability. To bridge this gap, we propose Shapley regression, a novel game-theoretic model replacing the linear predictor with a k-additive cooperative game, explicitly modeling co-occurrence of symptoms while maintaining the transparency and convexity of logistic regression. We carry out an empirical study of our lightweight method on eight public biomedical datasets, showing that a 2-additive model with ℓ2 regularization achieves an optimal trade-off between predictive power and noise robustness. We apply it to a real-world cohort of 222 patients, on which Shapley regression accurately distinguished APDS cases from matched controls, confirming and validating phenotypes known to be associated with APDS, and facilitating the exploration of pairwise interactions between symptoms, validated by clinical experts.

Medical image segmentation models based on Vision Mamba architectures have demonstrated strong performance, along with improved efficiency compared to convolutional and transformer-based approaches. However, existing lightweight and ultralight variants often suffer from boundary degradation and inconsistent shape prediction, thereby limiting their clinical reliability. We propose TvaraNet, an extremely lightweight segmentation network designed to preserve boundary fidelity under strict efficiency constraints. TvaraNet contains only 0.037M parameters and requires only 0.060 GFLOPs, enabling efficient deployment in resource-constrained environments. The architecture introduces two core lightweight modules. Parallel Skip Mamba Averaging (PASMA) enhances long-range dependency modeling by injecting globally aggregated channel context into mamba blocks. Dilated Asymmetric Spatial Mixer Attention (DASMA) improves boundary-aware feature refinement in skip connections through efficient multi-scale spatial modulation. In addition, training-time regularization strategies, including an Adaptive Gated Gradient Reversal Layer (AGGRL), an adaptive Singular Value Decomposition (SVD) loss, and a Boundary-weighted Cross-Entropy loss (BWCE), are employed to suppress redundant features and emphasize object boundaries without adding inference overhead. Experiments on ISIC-17, ISIC-18, and a spinal segmentation dataset demonstrate that TvaraNet consistently outperforms existing lightweight models and achieves competitive or superior boundary-aware performance compared to heavier architectures. Notably, it improves Mean Intersection over Union (mIoU) by +1.50, +1.42, and +3.35 over Ultralight VMUNet on the respective datasets, establishing TvaraNet as a practical solution for boundary-consistent medical image segmentation on edge and low-resource platforms. Code is available at: https://github.com/MindsLab-GitHub/TvaraNet

Cutting planes are central to mixed-integer linear programming (MILP) solving, yet their effectiveness hinges on expert tuning of separator configurations and hard-crafted cut-selection heuristics, creating a high barrier for non-specialists. Learning-based methods can reduce manual effort, but typically require large training datasets and often generalize poorly beyond the instance classes they are trained on. We propose an LLM-guided cutting-plane management framework that integrates large language models into the MILP solving pipeline. First, using chain-of-thought (CoT) prompting, the LLM infers an instance-specific separator configuration, deciding which separators to activate and how to set key parameters from the problem type and structural features. Then, it translates the evolving branch-and-bound state in natural language to perform stage-aware cut selection, choosing a high-quality subset of cuts that tightens the relaxation and improves overall solver performance. Leveraging the LLM's reasoning capabilities and rich background knowledge, our work removes dependence on domain-specific training data and substantially reduces reliance on expert-crafted configurations. Experiments show consistent improvements over SCIP's default settings, hard-crafted heuristics, and recent learning-based cut selection baselines.

Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework. Specifically, we design a dual-domain denoising architecture that couples topological connectivity encoded by heterogeneous GNNs with global geometric context modeled by a Transformer. Furthermore, we introduce Physics-Guided Sampling, an inference strategy that actively steers the generation using explicit gradients to ensure both statistical plausibility and physical validity. On the ISPD2005 MMS benchmarks, MacroDiff+ outperforms state-of-the-art baselines with a 6.1–6.2% reduction in wirelength. Notably, it exhibits superior stability and scalability on large-scale designs where prior methods fail to converge. The source code is provided at https://github.com/jhy00n/MacroDiff-plus.

Meteorological downscaling is crucial for high-resolution regional climate forecasting and disaster early warning. While neural operators have emerged as a promising paradigm for modeling complex spatiotemporal mappings, existing frameworks often struggle with spherical manifold geometric distortions, inherent atmospheric multi-scale coupling mismatches, and lack of explicit atmospheric laws. We propose the Spherical Physics-informed Neural Operator, which utilizes a Spherical Laplacian Decomposition to partition atmospheric fields into hierarchical frequency components, maintaining exact point-wise correspondence across scales. To evaluate these representations at arbitrary locations, we introduce a localized spherical integral operator that approximates continuous kernel transforms via geometry-aware attention. Dynamical consistency is further enforced by embedding differentiable constraints into the learning process. Extensive experiments demonstrate that our framework attains superior accuracy and zero-shot generalization across various meteorological variables and unseen queries, representing a robust and interpretable solution for global-to-regional meteorological downscaling.

Venous thromboembolism (VTE) is a common and life-threatening complication in cancer patients after treatment. Early risk assessment and detection of VTE primarily rely on clinical indicators, such as blood test results. However, existing studies are limited to static or snapshot-based models, failing to capture the evolving dynamics of disease progression, as deep time-series modeling is hindered by the lack of longitudinal clinical data. To address this gap, we introduce CliTsVTE, a large-scale clinical time-series dataset curated for VTE modeling, comprising 501,063 samples from 26,022 patients over seven years across nine cancer types. The dataset contains continuous time gaps between consecutive time points. Unlike many benchmarks, CliTsVTE reflects real-world clinical settings and presents unique challenges in continuous irregular time-series modeling with long-term irregularity and varying data granularity, which makes missingness significantly consequential. To tackle this, we propose a deep learning framework integrating multiple sequential backbones with an adversarially regularized autoencoder (ARAE) that learns latent representations to eliminate missingness. Experiments on CliTsVTE show that our best model achieves 88.7% accuracy and an AUC of 0.952, significantly outperforming traditional time-point models and regular time-series benchmarks. These results establish a strong benchmark for deep modeling of continuous irregularity in clinical time-series data and highlight the potential of AI-driven large-scale clinical datasets in solving real-world medical research challenges.

Dynamically Reconfigurable Battery (DRB) systems employ power electronic switches to create dynamic topologies. They enable effective management of cell difference through real-time adjustment of cell connections. However, existing DRB control methods struggle to learn effective strategies due to sparse rewards, which arise from blind exploration in large topological action spaces and complex operational constraints. This leads to insufficient policy learning, making safety and balancing performance difficult to ensure in practical applications. To this end, we propose a Subgraph-Augmented Hierarchical Reinforcement Learning (SAHRL) framework. By combining hierarchical policies with topological structural knowledge, SAHRL effectively accelerates policy exploration and mitigates reward sparsity. Specifically, the high-level policy determines the strategic direction, while the subgraph-augmented low-level policy refines actions to meet operational constraints. The topological structural knowledge, extracted in the form of subgraphs and incorporated as an inductive bias, helps the agent focus on meaningful action patterns and reduce invalid exploration in the large action space. Extensive simulations and real-world experiments show that SAHRL achieves safe and efficient balancing. Notably, it increases the energy release by 10.56% compared to conventional methods in real-world applications.