cs.AI（2024-11-19）

📊 共 12 篇论文

🎯 兴趣领域导航

支柱九：具身大模型 (Embodied Foundation Models) (8) 支柱二：RL算法与架构 (RL & Architecture) (2) 支柱八：物理动画 (Physics-based Animation) (1) 支柱一：机器人控制 (Robot Control) (1)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (8 篇)

#题目一句话要点标签🔗⭐
1 The Moral Mind(s) of Large Language Models 利用显示偏好理论评估大型语言模型的道德推理一致性与异质性 large language model
2 Large Language Models for Combinatorial Optimization of Design Structure Matrix 提出基于大语言模型（LLM）的框架，用于设计结构矩阵（DSM）的组合优化。 large language model
3 A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems 提出一种分层架构，提升基于大语言模型的软件系统能力并促进高效开发。 large language model
4 AI Flow at the Network Edge 提出AI Flow框架，利用异构资源实现网络边缘的智能服务 large language model multimodal
5 Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment 利用ChatGPT和CoT提示，克服金融领域行为偏差，提升黄金投资回报 large language model chain-of-thought
6 Human-In-the-Loop Software Development Agents 提出人机协同的软件开发智能体框架HULA，提升开发效率。 large language model
7 When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations 通过模型生成解释理解LLM后门攻击，揭示后门触发机制 large language model
8 Lucia: A Temporal Computing Platform for Contextual Intelligence Lucia：用于情境智能的时间计算平台，增强人类认知 large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (2 篇)

#题目一句话要点标签🔗⭐
9 Neurosymbolic Graph Enrichment for Grounded World Models 提出神经符号图增强方法，提升LLM在具身世界模型中的理解与推理能力 world model large language model multimodal
10 Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem 提出SPI框架，解决多智能体强化学习中Box-Pushing问题训练低效难题 reinforcement learning

🔬 支柱八：物理动画 (Physics-based Animation) (1 篇)

#题目一句话要点标签🔗⭐
11 Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events 提出基于大语言模型的检索重排序框架，用于增强时空语义关联的环境事件挖掘与推荐。 spatiotemporal large language model

🔬 支柱一：机器人控制 (Robot Control) (1 篇)

#题目一句话要点标签🔗⭐
12 Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity 提出基于偏好条件梯度变化的MO-ME算法，提升多目标质量多样性搜索效率。 locomotion

⬅️ 返回 cs.AI 首页 · 🏠 返回主页