Recent studies have shown that unsupervised graph contrastive learning (GCL) is vulnerable to adversarial attacks. Automatic adversarial augmentation techniques are proposed to improve both the effectiveness and robustness of GCL. Existing methods typically regard unsupervised contrastive loss as the adversarial goal, essentially aiming to maximize inter-view instance-wise discrepancies between adversarial and original views. However, such attacks overlook intra-view neighborhood inconsistency, which hinders the robustness of GCL models against local neighborhood noises, resulting in performance degradation on low-homophily graphs. To tackle this issue, we propose a novel adversarial contrastive paradigm, named Edge self-aDversarial Augmentation for Graph Contrastive Learning (EDA-GCL). We theoretically establish that the adversarial objective of the intra-view neighborhood is equivalent to maximizing the discrepancy between bidirectional edge features. Hence, we build our adversarial framework based on edge self-adversarial learning. It generates pairwise adversarial augmentations from the original view by learning distinct neighborhood connectivity structures. The learned pairwise adversarial views are utilized for GCL model training in the minimization stage. Notably, this edge-level adversarial approach reduces the computational complexity to the level of the edge number. Experiments on various graph tasks and complex noise scenarios demonstrate the superiority and robustness of our EDA-GCL.

Recent advancements in large language models (LLMs) have improved performance on standardized medical benchmarks. However, existing benchmarks often rely on truncated context and idealized scenarios. In practice, clinical diagnosis requires synthesizing patient history, physical examination, laboratory tests, and imaging under time constraints, and LLMs can struggle with accuracy and may hallucinate when confronted with authentic cases. To address this challenge, we propose the Clinical Agentic Reasoning Engine (CARE) , a physician-inspired workflow that structures diagnosis into retrieval, preliminary diagnosis, final diagnosis, and confidence-gated recheck, with intermediate outputs serialized in JSON for verifiable, training-free inference. Using CARE as a data-generation pipeline with clinician-prepared diagnostic criteria, we curate 2,000 de-identified real-world cases across 15 abdominal disease categories and produce stepwise CARE annotations under a fixed schema. We adopt Dual-stage Alignment for Reasoning Enhancement (DARE) , which trains on these CARE-annotated trajectories, uses supervised fine-tuning on physician-structured long-form demonstrations and then applies group relative policy optimization to refine policy and promote self-correction. Finally, we introduce CARE-Dx , the resulting diagnosis model obtained by applying DARE to an instruction-tuned backbone, while CARE also remains a training-free inference protocol that can be applied to other LLMs. Experiments show that, under DARE, CARE-Dx achieves strong in-domain and zero-shot out-of-domain performance and approaches leading closed-source accuracy on evaluations, with clinician assessment by 12 experienced physicians from multiple departments confirming that its reasoning aligns with real clinical workflows. Moreover, on the de-identified private cohort Rui-EHR , which covers a broader set of diseases, the CARE pipeline maintains diagnostic quality.