Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications. Recently, researchers have begun using LLMs to automate survey generation for better efficiency. However, the quality gap between LLM-generated surveys and those written by human remains significant, particularly in terms of outline quality and citation accuracy. To close these gaps, we introduce SURVEYFORGE, which first generates the outline by analyzing the logical structure of human-written outlines and referring to the retrieved domain-related articles. Subsequently, leveraging high-quality papers retrieved from memory by our scholar navigation agent, SURVEYFORGE can automatically generate and refine the content of the generated article. Moreover, to achieve a comprehensive evaluation, we construct SurveyBench, which includes 100 human-written survey papers for win-rate comparison and assesses AI-generated survey papers across three dimensions: reference, outline, and content quality. Experiments demonstrate that SURVEYFORGEcan outperform previous works such as AutoSurvey.

Although remote sensing (RS) data with multiple modalities can be used to significantly improve the accuracy of semantic segmentation in RS data, how to effectively extract multimodal information through multimodal feature fusion remains a challenging task. Specifically, existing methods for multimodal feature fusion still face two major challenges: 1) Due to the diverse imaging mechanisms of multimodal RS data, the boundaries of the same foreground may vary across different modalities, leading to the inclusion of unwanted background semantics in the fused foreground features; 2) RS data from different modalities exhibit varying discriminative abilities for different foregrounds, making it challenging to determine the proportion of semantic information for each modality in the fusion results. To address the above issues, we propose a dynamic feature fusion method based on region-wise queries, namely DF2RQ, for SS of multimodal RS data. This method is primarily composed of two components: the spatial reconstruction (SR) module and the dynamic fusion (DF) module. Within the SR module, we propose a spatial reconstruction scheme that samples foreground features from different modalities, achieving independent reconstruction of different unimodal features, thereby alleviating the semantic mixing between foreground and background across modalities. In the DF module, a feature fusion scheme based on unimodal feature reference positions is proposed to obtain fusion weights for each modality, thereby enabling the dynamic fusion of complementary features from multiple modalities. The performance of the proposed method has been extensively evaluated on various multimodal RS datasets for SS, and the experimental results consistently show that the proposed method achieves state-of-the-art accuracy on multiple commonly used metrics. In addition, our code is available at https://github.com/I3ab/DF2RQ.

Object detection in remote sensing images (RSIs) remains a challenging task due to complex variations in object scale, dense arrangements, and arbitrary orientations. Compared to the widely used multistage and one-stage approaches, query-based methods that avoid postprocessing procedures and implement end-to-end inference, have recently attracted much attention. However, existing query-based methods still face two main challenges: 1) the feature sampling regions predicted by the query vectors often fail to be aligned with the foreground features, making it difficult to accurately classify and locate potential objects; and 2) the cascade decoders are crucial for optimizing the query vectors, resulting in a slower inference process. To address the above issues, we propose a novel object detection method named point set decoding based on semantic query (PSD-SQ), which mainly consists of two components: a semantic query generator (SQG) module and an oriented point set decoder (OPSD) module. The SQG module is proposed to generate semantic query vectors with rich object information based on the semantic correlations among feature vectors. The OPSD module includes two blocks: a point sampling with angle (PSA) block and a dynamic interactor (DI) block. The PSA block is constructed to refine the sampling locations with predicted angles, aligning the sampling locations and oriented object regions, and the DI block is designed to decode the sampled features with dynamic weights, making the decoding process more efficient. The proposed method is extensively evaluated on various object detection datasets of RSIs, and the experimental results consistently demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in terms of both accuracy and inference speed. In addition, our code is available athttps://github.com/I3ab/PSD_SQ.