Abstract. Visual Grounding (VG) is a core multimodal task that localizes image targets via natural language descriptions, and it is crucial for Unmanned Aerial Vehicle (UAV) applications. However, existing remote sensing (RS) VG datasets primarily rely on rule-driven explicit descriptions, which are inconsistent with real-world demands for interpreting implicit descriptions based on context, common sense, or domain knowledge. In addition, the cross-lingual robustness of Large Vision-Language Models (LVLMs) in implicit VG remains to be thoroughly investigated. This study evaluates the cross-lingual performance of Qwen2.5-VL-7B and InternVL3.5-8B across nine languages, incorporating analyses of text length dynamics, visual attention, and language structural effects. The results demonstrate that Qwen2.5-VL-7B exhibits outstanding performance in maintaining consistent task paradigm alignment (explicit VG outperforms implicit VG) and balanced text output, which benefits from the syntactic stability and low cognitive load of East Asian languages. In contrast, InternVL3.5-8B presents task paradigm misalignment, uncontrolled text expansion, and generative hallucinations. Furthermore, differences in language structures: East Asian languages depend on word order for semantic expression, whereas Western languages feature complex lexical morphology, significantly affect attention allocation and VG accuracy. This study provides key insights for optimizing cross-lingual vision-language alignment of LVLMs and advancing practical multimodal applications in UAV scenarios.
Jue Chen, Penghui Huang, Ran Ding et al.· The International Archives o...· 0 citations
In the quantitative finance area, particularly in order execution, reinforcement learning (RL) has shown great promise due to its ability to interact with market environments based on real data. However, traditional RL methods suffer from slow research speed and rely on static market assumptions, which do not consider the impact of the agent's execution action on the environment. To address these, we propose a Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework. The framework utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code. SE-RL could continuously improve the accuracy of LLM-generated RL algorithms through a dual-enhancement kit at both high-level (prompt refinement) and low-level (parameter fine-tuning). Additionally, we use a multi-agent system to simulate dynamic financial markets, accounting for the impact of order executions on market dynamics. To further enhance training in such a dynamic market, we develop a hybrid environment training method that could rebalance each environment's loss weight. Comprehensive experiments on 200 realistic stock datasets demonstrate that our proposed framework outperforms current state-of-the-art baselines. Project page: https://kdd2026-se-rl.github.io/.
Vincent Fu, Xinxin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 0 citations