cs.AI（2024-11-05）

📊 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 Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs 利用大型语言模型分析长链非编码RNA转录调控的潜力与挑战 large language model foundation model
2 AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution AI Metropolis：通过乱序执行加速基于LLM的多智能体仿真 large language model
3 SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents 提出稀疏混合Agent（SMoA）框架，提升多Agent LLM的效率和多样性 large language model ✅
4 Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping 提出Interaction2Code基准，评估并提升MLLM在交互式网页代码生成中的能力。 large language model multimodal
5 LLM Generated Distribution-Based Prediction of US Electoral Results, Part I 提出基于LLM输出分布的预测方法，用于美国总统选举结果预测。 large language model
6 dafny-annotator: AI-Assisted Verification of Dafny Programs 提出dafny-annotator，利用AI辅助Dafny程序的形式化验证，提升验证效率。 large language model
7 Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents Watson：用于LLM驱动Agent推理的认知可观测性框架 large language model
8 Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities 通过LLM智能体社交互动涌现个体独特性 large language model
9 HumanVLM: Foundation for Human-Scene Vision-Language Model 提出HumanVLM，为人类-场景视觉语言任务提供基础模型。 multimodal

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

#题目一句话要点标签🔗⭐
10 Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey 综述视频生成与世界模型在自动驾驶中的交互，探索其在提升系统鲁棒性与可靠性的潜力 world model scene reconstruction
11 Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning 提出基于强化学习的多无人机协同追逃自主决策模型 reinforcement learning deep reinforcement learning
12 Accelerating Task Generalisation with Multi-Level Skill Hierarchies 提出FraCOs多层技能分层强化学习方法，加速任务泛化并实现最优性能。 reinforcement learning deep reinforcement learning

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