cs.AI(2025-12-24)
📊 共 10 篇论文
🎯 兴趣领域导航
支柱九:具身大模型 (Embodied Foundation Models) (6)
支柱一:机器人控制 (Robot Control) (2)
支柱二:RL算法与架构 (RL & Architecture) (2)
🔬 支柱九:具身大模型 (Embodied Foundation Models) (6 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | Beyond Context: Large Language Models Failure to Grasp Users Intent | 大型语言模型未能理解用户意图,易被恶意利用绕过安全机制 | large language model | ||
| 2 | Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation | 提出Agentic XAI框架,结合SHAP与多模态LLM迭代优化解释质量,应用于农业推荐系统。 | large language model multimodal | ||
| 3 | RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic | RoboSafe:通过可执行安全逻辑保障具身智能体的安全 | multimodal | ||
| 4 | Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks | 量化LLM规模对经济生产力的影响:咨询、数据分析与管理任务的实验证据 | large language model | ||
| 5 | Casting a SPELL: Sentence Pairing Exploration for LLM Limitation-breaking | SPELL:通过句子配对探索LLM在恶意代码生成中的安全漏洞 | large language model | ||
| 6 | Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality | 提出Mesh-Attention,通过优化数据局部性提升分布式Attention的通信效率,加速LLM训练。 | large language model |
🔬 支柱一:机器人控制 (Robot Control) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 7 | Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction | 提出基于具身AI的IoMT边缘计算框架,优化无人机轨迹和任务卸载,最小化任务完成时间。 | trajectory optimization reinforcement learning deep reinforcement learning | ||
| 8 | One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents | RepoNavigator:基于强化学习的单工具LLM智能体,用于仓库级代码定位 | manipulation reinforcement learning distillation |
🔬 支柱二:RL算法与架构 (RL & Architecture) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 9 | Policy-Conditioned Policies for Multi-Agent Task Solving | 提出基于策略条件策略的程序化迭代最佳响应算法,解决多智能体任务中的策略适应性问题。 | reinforcement learning deep reinforcement learning large language model | ||
| 10 | The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents | 提出概率框架以解决LLM代理的知识不对称问题 | reinforcement learning RLHF |