cs.LG（2025-04-11）

📊 共 12 篇论文 | 🔗 1 篇有代码

🎯 兴趣领域导航

支柱九：具身大模型 (Embodied Foundation Models) (8 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (4)

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

#题目一句话要点标签🔗⭐
1 HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive Modules HyperCore：构建双曲基础模型的全面模块化核心框架 foundation model multimodal
2 Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries 提出非欧几何嵌入，提升下一代大模型处理复杂结构数据的能力 large language model foundation model
3 AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models AGENT：基于大语言模型的飞行器生成与设计工具，加速CAD流程。 large language model
4 Mimic In-Context Learning for Multimodal Tasks 提出MimIC以提高多模态任务中的上下文学习稳定性 multimodal ✅
5 Knowledge Graph-extended Retrieval Augmented Generation for Question Answering 提出知识图谱增强的检索增强生成（KG-RAG）框架，提升问答系统在多跳问题上的准确性和可解释性。 large language model chain-of-thought
6 VectorLiteRAG: Latency-Aware and Fine-Grained Resource Partitioning for Efficient RAG VectorLiteRAG：面向低延迟的RAG系统细粒度资源分配方案 large language model
7 SortBench: Benchmarking LLMs based on their ability to sort lists SortBench：提出用于评估LLM排序能力的可扩展基准测试 large language model
8 ML For Hardware Design Interpretability: Challenges and Opportunities 探索LLM在硬件设计可解释性中的应用，解决RTL代码自然语言描述难题。 large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (4 篇)

#题目一句话要点标签🔗⭐
9 Investigating the Treacherous Turn in Deep Reinforcement Learning 研究深度强化学习中的“背叛性转向”现象及应对策略 reinforcement learning deep reinforcement learning DRL
10 Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions 利用大语言模型知识蒸馏，增强化学反应贝叶斯优化 preference learning large language model
11 Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash ActiveFlow：通过DRAM与Flash间主动权重交换，扩展端侧LLM部署规模 distillation large language model
12 Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection 扩展神经进化方法，实现复杂环境下自主机器人近距离导航，应用于城市搜救和工业检测。 reinforcement learning deep reinforcement learning

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