cs.AI（2024-11-11）

📊 共 12 篇论文 | 🔗 1 篇有代码

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支柱九：具身大模型 (Embodied Foundation Models) (9 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (3)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (9 篇)

#题目一句话要点标签🔗⭐
1 NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics NatureLM-audio：面向生物声学的音频-语言基础模型，实现零样本物种分类。 large language model foundation model
2 Towards Characterizing Cyber Networks with Large Language Models 提出CLEM模型，利用大语言模型表征网络流量日志，发现网络安全异常。 large language model
3 Autonomous Droplet Microfluidic Design Framework with Large Language Models 提出MicroFluidic-LLMs框架，利用大语言模型提升微流控液滴设计的自动化水平。 large language model
4 Generative midtended cognition and Artificial Intelligence. Thinging with thinging things 提出“生成式中介认知”概念，探索生成式AI与人类认知融合，超越传统扩展认知理论。 large language model multimodal
5 Stronger Models are NOT Stronger Teachers for Instruction Tuning 挑战指令调优中“更强模型即更强教师”的假设，提出兼容性调整奖励CAR。 large language model instruction following
6 Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data 提出TimeText Corpus多模态数据集，并探索混合多模态LLM在时间序列和文本数据联合预测中的应用 multimodal TAMP ✅
7 Automatically Detecting Online Deceptive Patterns 提出AutoBot框架，通过视觉模型和LLM自动检测网页欺骗性模式。 large language model
8 ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving ChemToolAgent：工具对语言模型解决化学问题的影响研究 large language model
9 Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications Minion：通过专家和用户驱动策略解决AI伴侣应用中的价值冲突 large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)

#题目一句话要点标签🔗⭐
10 To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing 提出一种动态训练决策算法，平衡移动边缘计算中深度强化学习的效率与训练成本。 reinforcement learning deep reinforcement learning DRL
11 A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs AutoRestTest：利用多智能体、语义图和LLM驱动输入进行REST API测试。 reinforcement learning large language model
12 OCMDP: Observation-Constrained Markov Decision Process 提出观测约束MDP，解决观测成本敏感环境下的决策问题。 reinforcement learning deep reinforcement learning

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