Google DeepMind上周公布具备2种思维模式的系统框架，可让AI代理人（agent）能兼具快、慢思考的效益。现有大型语言模型能以自然语言和用户互动，具有二种能力，包括对话和规划／思考。DeepMind团队设计的一种由双系统组成的Talker-Reasoner代理人框架，能同时执行对话及多步骤推理任务，一如由以色列诺贝尔经济学家Daniel Kah...
Google DeepMind上周公布具备2种思维模式的系统框架,可让AI代理人(agent)能兼具快、慢思考的效益。 现有大型语言模型能以自然语言和用户互动,具有二种能力,包括对话和规划/思考。DeepMind团队设计的一种由双系统组成的Talker-Reasoner代理人框架,能同时执行对话及多步骤推理任务,一如由以色列诺贝尔经济学家Daniel Kahneman...
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The combination aims to equip an agent with the ability to make decisions in various situations, even if they were not anticipated during the design phase. PDF Abstract Code Edit No code implementations yet. Submit your code now Tasks Edit Decision Making Datasets Edit Add Datasets ...
Vital AI Agent Ecosystem, Ensemble Reasoning. Contribute to vital-ai/vital-llm-reasoner development by creating an account on GitHub.
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ghosts.moss_agent import MossAgent __ghost__ = MossAgent( moss_module=__name__, persona=""" 你是一个精通 Ubuntu 系统的 Agent. """, instructions=""" 你的主要任务是协助用户理解并且操作当前系统. """, name="Ubuntu Agent", llm_api="deepseek-chat", ) def __shell_providers__():...
DeepMind:快与慢思考的Agent | 记得在近期的几篇笔记以及曾经那篇「融合RL与LLM思想,探寻世界模型以迈向AGI·中篇」长文中,对system1与system2 快与慢思考下两种模式进行了深入阐释,并探索了两种思考模式在模型训练-推理·时的过程性平衡、整体任务目标的设定与优化策略、任务目标与训练范式下模型网络结构与目标/损失...
Below is a diagram of how it works with the "project goose" agent, which makes use of ollama with all local models: This is a generic MCP server so should work with any agent system, as long as ollama is running, and can run entirely locally. Usage: npx -y deepseek-reasoner-mcp ...
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