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人工智能

83 篇 · 资讯流
  1. 雷锋网(AI 媒体)

    阶跃新旗舰 Step 5 Preview 发布,跻身 AA 榜单全球开源前三

    9 月 20 日,阶跃星辰发布新一代旗舰基座模型 Step 5 Preview,重点面向 AI 编程、软件工程、专业知识工作、金融等场景,提升模型在真实世界 Agentic 任务中的综合表现,并进一步拓展智能与成本之间的最优边界。在最新的全

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  2. arXiv cs.AI(人工智能)

    General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems

    Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which d

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  3. arXiv cs.AI(人工智能)

    Beyond Single-Model Injection: A Threat Model and Defense Architecture for Prompt Injection in Multi-Agent Systems

    Existing prompt injection research focuses on single-model chatbot scenarios, where an attacker manipulates one LLM thro

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  4. arXiv cs.LG(机器学习)

    The Weight Is Over - Interactive Diffusion on Consumer GPUs

    On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry

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  5. 开源中国(资讯)

    别再让 Agent 裸连数据库了:如何用 MCP + 语义网关解决 NL2SQL 幻觉与权限失控

    在 2026 年,给 AI Agent(Cursor、Claude Desktop、Dify、Coze 等)外挂一个数据库连接,已经成为很多开发者的标配操作。从简单的官方 SQLite/MySQL MCP 到各种通用客户端扩展,让大模型“直

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  6. arXiv cs.AI(人工智能)

    Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems

    Retrieval-Augmented Generation (RAG) improves large language models by grounding outputs in external knowledge sources,

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  7. 开源中国(资讯)

    华为云 All in Agentic Cloud,灵衢昇腾 950 智算集群全球上线

    华为云这届全联接大会的调子,是"Agentic Cloud"。华为云 CEO 周跃峰演讲里那句被反复引用的判断是:智能体时代基础设施的核心,不再只是给算力,而是"让每一个 Token 更高效"。 最能说明态度的莫过于那个出圈的名字——灵衢昇

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  8. arXiv cs.CL(自然语言)

    Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction

    Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate

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  9. arXiv cs.AI(人工智能)

    Large Language Models as Falsifiers for Cyber-Physical Systems

    Falsification searches for counterexamples to formal specifications in cyber-physical systems (CPS). With specifications

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  10. arXiv cs.CL(自然语言)

    Uni-LaDiR: Latent Diffusion Unifies Multimodal Reasoning

    Multimodal reasoning requires models to draw on information from multiple modalities throughout the reasoning process. Y

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