Graph neural networks (GNNs) rely critically on neighborhood aggregation, yet conventional mean aggregation may fail to capture class-relevant distributional information, particularly in heterophilic graphs. We propose ExpectileGCN, an adaptive graph aggregation framework that introduces a learnable expectile level to capture neighborhood information beyond the mean. The proposed operator continuously adjusts aggregation between minimum- and maximum-type behaviors, with mean aggregation as a special case. A differentiable approximation based on sigmoid smoothing and fixed-point iterations enables end-to-end optimization. Theoretically, we establish stability properties and transductive generalization guarantees for ExpectileGCN. We further investigate its classification risk under heterophilic neighborhood models. For informative neighborhoods, we show that expectile aggregation can exploit class-dependent scale differences to achieve a classification advantage over mean aggregation. Under a local asymptotic regime with diminishing per-neighbor label informativeness, we establish that this advantage can persist in terms of limiting classification risk. Experiments on homophilic and heterophilic benchmark graphs demonstrate strong predictive performance and improved robustness under feature corruption and structural perturbations.
Tianwei Lan, Jiaqi Wu, Zeming Liu, Zhaoxin Fan Beijing University of Aeronautics and Astronautics, Haifeng Wang Baidu, Yuhang Guo
Embodied Action Sequence Planning focuses on the capability of embodied agents to implement action planning via environmental perception. This technology enables diverse intelligent assistance for real-world scenarios such as home and office environments. To address the limitations of existing embodied agents in meeting the requirement for proactivity and achieving joint understanding of visual and audio information, this study investigates the ability of embodied agents to proactively provide assistance through action sequence planning based on joint understanding of vision and audio perception without explicit human instructions. Correspondingly, we propose PEAP, the first multimodal proactive embodied action sequence planning dataset. We evaluate the performance of multiple Large Language Models on the PEAP dataset. The results demonstrate that these models still exhibit significant deficiencies on this task particularly lacking accurate environmental perception capabilities. Furthermore, ablation experiment and replacement experiment further corroborate that the joint understanding of multimodal information can significantly improve the models’ performance on proactive embodied action sequence planning task.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jianlyu Chen, Junwei Lan Tencent and University of Science and Technology of China, Chaofan Li, Defu Lian University of Science and Technology of China, Zheng Liu
In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. First, we propose ReMixer, a new data synthesis method that overcomes the triviality problem prevalent in previous synthetic datasets, enabling large-scale production of 82K high-quality training samples. Second, we design Redapter, a self-adaptive learning algorithm that dynamically adjusts training each sample’s weight based on its reasoning intensity. This allows the model to effectively capture the complex semantic relationships between queries and documents. Third, we implement ReasonEmbed across multiple backbones of varying sizes, all of which achieve superior performance on reasoning-intensive retrieval tasks. Notably, our ReasonEmbed-Qwen3-8B model offers a record-high nDCG@10 score of 38.1 on the BRIGHT benchmark, which significantly outperforms existing text embedding models. We will fully open-source our created resource in ReasonEmbed to push forward the research advancement in this field.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表