Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of drag-based editing. However, existing drag-based methods often struggle to balance drag accuracy with natural, plausible, and intent-aligned generation. We propose MoRe-Drag, a motion-grounded drag-based editing method. Our key insight is to treat pixel-space warping as coarse motion evidence, and to inject this evidence into the generative sampling trajectory. Specifically, MoRe-Drag performs region-aware latent recomposition over refinement, inpainting, and preservation regions, coupled with stage-adaptive conditioning that progressively shifts from motion-grounded structure formation to semantic refinement. We further support a no-manual-instruction interface by adapting the MLLM-based text encoder for drag-aware instruction inference. Experiments on DragBench-SR and DragBench-DR show that MoRe-Drag substantially improves drag precision over strong base editors and achieves superior drag accuracy among SOTA drag-based methods, while delivering strong semantic consistency and visually realistic results. Project page: https://xinyu-pu.github.io/projects/more-drag/.
Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen Shanghai Jiaotong University, Pan Zhou Huazhong University of Science and Technology 等 10 位作者
LLM-based agents are rapidly being deployed in real-world applications (e.g., digital assistants and customer service), making safety a critical concern. However, in multi-turn, tool-augmented settings, dynamic user interactions, external tool use, and unintended harmful behaviors make robust safety assurance challenging. To address these challenges, we propose SafeAgent, a framework that improves agent safety through fully automated synthetic data generation. SafeAgent introduces (1) an open and extensible threat model OTS that decomposes agent risk into instruction-, context-, and action-induced sources to ground safety analysis and alignment; and (2) an automated pipeline that instantiates OTS to surface scenario-specific failure modes, stress-test agents, and generate self-reflective safe responses—without hazardous real-world data collection. We evaluate SafeAgent on two safety benchmarks and one real-world terminal task. Across four widely used open-source models, SafeAgent improves safety performance by 45% on average and delivers a 28.91% gain on the real-world task, outperforming state-of-the-art closed-source models. These results highlight the practical advancement and scalability of SafeAgent in building safer LLM agents for real-world deployment.
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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
Ziji Sheng, Guiyao Tie, Weidong Wang, Pan Zhou Huazhong University of Science and Technology, Daizong Liu Wuhan University
Existing adaptive learning systems struggle to simultaneously achieve deep personalization, dynamic adaptability, and content trustworthiness, particularly in logically rigorous STEM fields where Large Language Models (LLMs) are prone to “hallucination”. This paper introduces LearnerCoMPASS (Cognitive Multi-model Planning Adaptive System), an integrated, end-to-end framework for adaptive learning. At its core, the framework features a novel multi-model path planning algorithm that orchestrates and fuses the outputs of heterogeneous LLM experts to generate and optimize learning sequences. To enable deep personalization, we design a dynamic cognitive diagnosis module that employs an innovative encoder-decoder architecture to generate precise, multi-dimensional cognitive state vectors for learners. To ensure trustworthiness, the system leverages an adaptively constructed dynamic knowledge graph and a Graph-RAG mechanism to provide factual anchors and logical constraints for LLM reasoning, thereby mitigating hallucinations. Extensive experiments demonstrate that LearnerCoMPASS significantly outperforms state-of-the-art baselines in generating high-quality personalized learning paths. Furthermore, ablation studies validate the critical contributions of our dynamic cognitive diagnosis and multi-model planning components.
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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