Traditional Graph Neural Networks (GNNs) cannot generalize to new graphs with node attributes different from the training ones, making zero-shot generalization across different node attribute domains an open challenge in graph machine learning. In this paper, we propose STAGE, which encodes *statistical dependencies* between attributes rather than individual attribute values, which may differ in test graphs. By assuming these dependencies remain invariant under changes in node attributes, STAGE achieves provable generalization guarantees for a family of domain shifts. Empirically, STAGE demonstrates strong zero-shot performance on medium-sized datasets: when trained on multiple graph datasets with different attribute spaces (varying in types and number) and evaluated on graphs with entirely new attributes, STAGE achieves a relative improvement in Hits@1 between 40% to 103% in link prediction and a 10% improvement in node classification compared to state-of-the-art baselines.

Causal discovery from observational data is a fundamental task in artificial intelligence, with far-reaching implications for decision-making, predictions, and interventions. Despite significant advances, existing methods can be broadly categorized as constraint-based or score-based approaches. Constraint-based methods offer rigorous causal discovery but are often hindered by small sample sizes, while score-based methods provide flexible optimization but typically forgo explicit conditional independence testing. This work explores a third avenue: developing differentiable $d$-separation scores, obtained through a percolation theory using soft logic. This enables the implementation of a new type of causal discovery method: gradient-based optimization of conditional independence constraints. Empirical evaluations demonstrate the robust performance of our approach in low-sample regimes, surpassing traditional constraint-based and score-based baselines on a real-world dataset. Code implementing the proposed method is publicly available at [https://github.com/PurdueMINDS/DAGPA](https://github.com/PurdueMINDS/DAGPA).

Abstract Merge sort as a divide-sort-merge paradigm has been widely applied in computer science fields. As modern reduced instruction set computing architectures like the fifth generation (RISC-V) regard multiple registers as a vector register group for wide instruction parallelism, optimizing merge sort with this vectorized property is becoming increasingly common. In this paper, we overhaul the divide-sort-merge paradigm, from its register-level sort to the cache-aware merge, to develop a fine-grained RISC-V vectorized merge sort (RVMS). From the register-level view, the inline vectorized transpose instruction is missed in RISC-V, so implementing it efficiently is non-trivial. Besides, the vectorized comparisons do not always work well in the merging networks. Both issues primarily stem from the expensive data shuffle instruction. To bypass it, RVMS strides to take register data as the proxy of data shuffle to accelerate the transpose operation, and meanwhile replaces vectorized comparisons with scalar cousin for more light real value swap. On the other hand, as cache-aware merge makes larger data merge in the cache, most merge schemes have two drawbacks: the in-cache merge usually has low cache utilization, while the out-of-cache merging network remains an ineffectively symmetric structure. To this end, we propose the half-merge scheme to employ the auxiliary space of in-place merge to halve the footprint of naïve merge sort, and meanwhile copy one sequence to this space to avoid the former data exchange. Furthermore, an asymmetric merging network is developed to adapt to two different input sizes. Experiments on the RISC-V processor SG2042 show that four fine-grained optimization schemes including register strided transpose, hybrid merging network, half-merge strategy, and asymmetric merging network, improve performance by 4.05%, 19.88%, 12.23%, and 11.04% respectively. Importantly, the overall performance is 1.34x faster than the parallel sorting in the Boost C++ library, and 1.85x faster than std::sort.

Infrared small target detection (IRSTD) is critical for low-visibility scenarios, but cluster IRSTD (CIRSTD) faces unique challenges: target ambiguity, background complexity, inter-cluster interference, and inadequate spatial modeling. Current deep learning approaches struggle with dense clusters due to limited fine-grained feature extraction, insufficient contextual constraints, and spatial resolution degradation. This paper proposes the Synergistic Shape-Contextual Fusion Network (SSCFNet) to address these issues. Firstly, the Shape Prior Self-Evolution (SPSE) module employs self-attention to model global spatial dependencies and generate structural priors for consistent detection. Accordingly, the Convolution-Shape Cross-Updating (CSCU) module integrates convolutional operations into shape representations to refine local geometric cues for low-contrast targets. Lastly, the Dynamic Synergistic Detection (DSD) module is designed to adaptively fuse individual and cluster-level features via attention mechanisms to preserve target boundaries and suppress noise. By bridging global structural and local shape refinement, SSCFNet effectively mitigates missed detections, false alarms, and localization errors in CIRSTD. Experimental results demonstrate that the proposed method outperforms state-of-the-art techniques in challenging scenarios. The code will be public at https://github.com/wangtuntun/SSCFNet.

Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA methods that learn parameters bound to certain entity or relation vocabularies can only be applied to the graph they are trained on which requires substantial training time before being deployed on a new graph. Here we present UltraQuery, the first foundation model for inductive reasoning that can zero-shot answer logical queries on any KG. The core idea of UltraQuery is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG. With the projection operation initialized from a pre-trained inductive KG completion model, UltraQuery can solve CLQA on any KG after finetuning on a single dataset. Experimenting on 23 datasets, UltraQuery in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 15 of them.