cs.AI(2025-12-06)

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支柱九:具身大模型 (Embodied Foundation Models) (9 🔗1) 支柱一:机器人控制 (Robot Control) (3)

🔬 支柱九:具身大模型 (Embodied Foundation Models) (9 篇)

#题目一句话要点标签🔗
1 Who Will Top the Charts? Multimodal Music Popularity Prediction via Adaptive Fusion of Modality Experts and Temporal Engagement Modeling 提出GAMENet,通过自适应融合多模态专家和时序建模预测音乐流行度。 large language model multimodal
2 AgenticCyber: A GenAI-Powered Multi-Agent System for Multimodal Threat Detection and Adaptive Response in Cybersecurity AgenticCyber:基于生成式AI的多智能体系统,用于网络安全中的多模态威胁检测与自适应响应 multimodal
3 Echo-CoPilot: A Multi-View, Multi-Task Agent for Echocardiography Interpretation and Reporting Echo-CoPilot:用于心动超声解读和报告的多视角多任务智能体 large language model foundation model
4 DaGRPO: Rectifying Gradient Conflict in Reasoning via Distinctiveness-Aware Group Relative Policy Optimization DaGRPO:通过区分性感知的组相对策略优化来纠正推理中的梯度冲突 large language model instruction following
5 Less Is More for Multi-Step Logical Reasoning of LLM Generalisation Under Rule Removal, Paraphrasing, and Compression 提出逻辑推理评估框架,揭示LLM在规则扰动下的泛化能力瓶颈 large language model
6 Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices 提出Vec-LUT,解决边缘设备上超低比特LLM并行推理的内存带宽瓶颈。 large language model
7 GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols GENIUS:一个用于自主设计和执行模拟协议的Agentic AI框架 large language model
8 Protecting Bystander Privacy via Selective Hearing in Audio LLMs 提出SH-Bench和BPFT,提升音频LLM在多说话人场景下的旁观者隐私保护能力。 large language model
9 DUET: Agentic Design Understanding via Experimentation and Testing DUET:通过实验和测试实现Agentic设计理解,提升硬件设计任务性能 large language model

🔬 支柱一:机器人控制 (Robot Control) (3 篇)

#题目一句话要点标签🔗
10 BEACON: A Unified Behavioral-Tactical Framework for Explainable Cybercrime Analysis with Large Language Models BEACON:利用大语言模型进行可解释网络犯罪分析的统一行为-战术框架 manipulation large language model
11 Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation 针对语音转换模型在噪声环境下的鲁棒性问题,提出一种基于输入扰动的全面评估框架。 manipulation
12 Securing the Model Context Protocol: Defending LLMs Against Tool Poisoning and Adversarial Attacks 提出针对模型上下文协议(MCP)的安全框架,防御LLM工具中毒和对抗攻击 manipulation large language model

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