cs.AI（2024-11-07）

📊 共 15 篇论文 | 🔗 1 篇有代码

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支柱九：具身大模型 (Embodied Foundation Models) (12 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (3)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (12 篇)

#	题目	一句话要点	标签	🔗	⭐
1	GUI Agents with Foundation Models: A Comprehensive Survey	综述：基于大模型的GUI智能体研究进展与未来趋势	large language model foundation model multimodal
2	Integrating Large Language Models for Genetic Variant Classification	整合大型语言模型以提升遗传变异分类的准确性和可靠性	large language model
3	Enhancing Reverse Engineering: Investigating and Benchmarking Large Language Models for Vulnerability Analysis in Decompiled Binaries	提出DeBinVul数据集，提升LLM在反编译二进制代码漏洞分析中的性能	large language model
4	Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability	大型语言模型诊断不确定性估计研究：提示词概率并非先验概率	large language model
5	AWARE Narrator and the Utilization of Large Language Models to Extract Behavioral Insights from Smartphone Sensing Data	AWARE Narrator：利用大语言模型从智能手机传感数据中提取行为洞察	large language model
6	Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks	Magentic-One：一个用于解决复杂任务的通用多智能体系统	generalist agent foundation model
7	CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation	提出CaPo：一种用于具身多智能体高效协作的协同规划优化方法	large language model	✅
8	Intellectual Property Protection for Deep Learning Model and Dataset Intelligence	综述深度学习模型与数据集的知识产权保护方法，涵盖主动防御与被动验证。	large language model
9	Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs	Alopex框架通过逻辑数据生成和混合训练，提升设备端LLM函数调用能力。	large language model
10	Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives	重新审视基于偏好的奖励建模中的Bradley-Terry模型，提出理论基础与替代方案	large language model
11	Green My LLM: Studying the key factors affecting the energy consumption of code assistants	研究代码助手能耗关键因素，优化配置实现节能，提升开发效率。	large language model
12	AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG	提出AMSnet-KG数据集，结合知识图谱RAG，用于LLM驱动的AMS电路自动设计。	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	🔗	⭐
13	Plasticity Loss in Deep Reinforcement Learning: A Survey	综述深度强化学习中的可塑性损失问题，分析原因、影响及应对策略。	reinforcement learning deep reinforcement learning
14	Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning	提出SMARL框架以解决多智能体强化学习中的安全问题	reinforcement learning PPO
15	Navigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning	提出一种基于策略行为和目标值的多目标强化学习策略集聚类方法，辅助决策者进行策略选择。	reinforcement learning

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