Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

In this paper, we focus on weakly supervised referring expression comprehension (REC), and identify that the lack of fine-grained visual capability greatly limits the upper performance bound of existing methods. To address this issue, we propose a novel framework for weakly supervised REC, namely Dynamic Visual routing Network (DViN), which overcomes the visual shortcomings from the perspective of feature combination and alignment. In particular, DViN is equipped with a novel sparse routing mechanism to efficiently combine features of multiple visual encoders in a dynamic manner, thus improving the visual descriptive power. Besides, we further propose an innovative weakly supervised objective, namely Routing-based Feature Alignment (RFA), which facilitates the visual understanding of routed features through the intra-modal and inter-modal alignment. To validate DViN, we conduct extensive experiments on four REC benchmark datasets. Experiments demonstrate that DViN achieves state-of-the-art results on four benchmarks while maintaining competitive inference efficiency. Besides, the strong generalization ability of DViN is also validated on weakly supervised referring expression segmentation. Source codes are anonymously released at: https://github.com/XxFChen/DViN.

Residual connection has been extensively studied and widely applied at the model architecture level. However, its potential in the more challenging data-centric approaches remains unexplored. In this work, we introduce the concept of ***Data Residual Matching*** for the first time, leveraging data-level skip connections to facilitate data generation and mitigate data information vanishing. This approach maintains a balance between newly acquired knowledge through pixel space optimization and existing core local information identification within raw data modalities, specifically for the dataset distillation task. Furthermore, by incorporating training-time refinements, our method significantly improves computational efficiency, achieving superior performance while reducing training time and peak GPU memory usage by 50\%. Consequently, the proposed method **F**ast and **A**ccurate **D**ata **R**esidual **M**atching for Dataset Distillation (**FADRM**) establishes a new state-of-the-art, demonstrating substantial improvements over existing methods across multiple dataset benchmarks in both efficiency and effectiveness. For instance, with ResNet-18 as the student model and a 0.8\% compression ratio on ImageNet-1K, the method achieves 48.4\% test accuracy in single-model dataset distillation and 50.9\% in multi-model dataset distillation, surpassing RDED by +6.4\% and outperforming state-of-the-art multi-model approaches, EDC and CV-DD, by +2.3\% and +4.9\%.

CAPTCHAs have been a critical bottleneck for deploying web agents in real-world applications, often blocking them from completing end-to-end automation tasks. While modern multimodal LLM agents have demonstrated impressive performance in static perception tasks, their ability to handle interactive, multi-step reasoning challenges like CAPTCHAs is largely untested. To address this gap, we introduce Open CaptchaWorld, the first web-based benchmark and platform specifically designed to evaluate the visual reasoning and interaction capabilities of MLLM-powered agents through diverse and dynamic CAPTCHA puzzles. Our benchmark spans 20 modern CAPTCHA types, totaling 225 CAPTCHAs, annotated with a new metric we propose: CAPTCHA Reasoning Depth, which quantifies the number of cognitive and motor steps required to solve each puzzle. Experimental results show that humans consistently achieve near-perfect scores, state-of-the-art MLLM agents struggle significantly, with success rates at most 40.0\% by Browser-Use Openai-o3, far below human-level performance,93.3\%. This highlights Open CaptchaWorld as a vital benchmark for diagnosing the limits of current multimodal agents and guiding the development of more robust multimodal reasoning systems.