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- ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset28
This work introduces ToolMind, a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances and employs fine-grained turn-level filtering to remove erroneous or suboptimal steps, ensuring that only high-quality reasoning traces are retained.
- SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments26
This work proposes a training-free, multi-modal LLM-driven scene-coherent typographic adversarial planning (SceneTAP) that employs a three-stage process: scene understanding, adversarial planning, and seamless integration.
- MetaRepair: Learning to Repair Deep Neural Networks from Repairing Experiences7
This work proposes to repair DNN from a novel perspective, where the repairing of target DNN is realized as a general learning-to-learn, a.k.a. meta-learning, process, and re-designs the meta-learning components under DNN repair context into a concrete model named MetaRepair.
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- MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agents4
The proposed MAGIC (Mastering Physical Adversarial Generation In Context), a novel framework powered by multi-modal LLM agents to address physical adversarial attacks as a one-shot patch generation problem, generates adversarial patches through a deep generative model that considers the specific scene context.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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