cs.AI(2025-03-24)

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

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

#题目一句话要点标签🔗
1 Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models 理论分析揭示LLM微调中安全性和能力之间的根本性权衡 large language model
2 Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation 揭示LLM在信息检索评估中的相互影响:排序器、评判器与助手 large language model
3 REALM: A Dataset of Real-World LLM Use Cases 构建REALM数据集,揭示LLM在现实世界的应用场景与用户画像 large language model
4 BitDecoding: Unlocking Tensor Cores for Long-Context LLMs with Low-Bit KV Cache BitDecoding:利用Tensor Core加速低比特KV缓存长文本LLM推理 large language model
5 Verbal Process Supervision Elicits Better Coding Agents CURA:通过口头过程监督提升代码生成Agent性能 large language model
6 VeriSafe Agent: Safeguarding Mobile GUI Agent via Logic-based Action Verification 提出VeriSafe Agent,通过逻辑验证保障移动GUI Agent的可靠性 foundation model
7 Bridging Writing Manner Gap in Visual Instruction Tuning by Creating LLM-aligned Instructions 提出LLM对齐指令,弥合视觉指令调优中的写作风格差距,提升多模态模型性能。 large language model
8 Improving RAG for Personalization with Author Features and Contrastive Examples 提出结合作者特征和对比样本的RAG方法,提升个性化文本生成效果 large language model

🔬 支柱二:RL算法与架构 (RL & Architecture) (1 篇)

#题目一句话要点标签🔗
9 AdaWorld: Learning Adaptable World Models with Latent Actions AdaWorld:通过潜在动作学习可适应的World Model,提升泛化能力 world model

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