与「MultiAgent」相关的搜索结果

The most important agent on this team builds nothing. It only checks. In the video, four agents assemble a browser game from a 12-second clip. Rules works out the movement logic. Builder assembles the scene and interface. Logic takes controls and collisions. The fourth, Tester, opens the browser and plays it by hand. Sounds minor. But this is exactly where most AI projects break: the code is written, the report looks great, and the restart button doesn't work. Here, verification is part of the pipeline. The agents check hitbox overlap, jump timing, state after restart. Specific things, not "looks fine." Jev runs between them. It writes nothing, it passes context. One agent learns how the character jumps, and the next one already knows. It works like a relay race, not like four people in separate rooms. One more detail I liked. In the last frame, the gameplay checks are still marked pending. No green checkmarks in advance. Want AI to make reliable things? Give it a separate agent that doesn't trust the others. #AIAgents# #GameDev# #MultiAgent#

🚀 Excited to introduce BrainPilot — a human-in-the-loop 1-1-N multi-agent framework designed to accelerate brain science research. 🧠 BrainPilot integrates: • 72 expert skills spanning 7 major neuroscience domains • A curated knowledge base of 7,200+ papers • Automatic review & verification • Trace visualization for transparent, reproducible scientific workflows Our system achieves performance comparable to state-of-the-art agentic frameworks on both Agents' Last Exam and our newly proposed BrainPilotBench. Everything is open source! We'd love for you to ⭐ star the repos, join the community, deploy BrainPilot locally, and adding new features, or proposing new benchmarks. (1/8) 🏠 Homepage: 📄 Technical Report: 🌟 BrainPilot: 📈 BrainPilotBench: #AI# #Neuroscience# #MultiAgent# #AgenticAI# #ScientificAI# #OpenSource# #BrainScience# #LLM# #ResearchAgents# #NeuroAI#

🚨 Thrilled to share that our lab will be presenting the 🏆 Best Paper at the NExT-Game Workshop at #ICML2026# today! 🎤 When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games 🏆 Best Paper @ NExT-Game Workshop 📍 Conference Room S307 📅 Fri, Jul 10 🕐 13:00–13:20 KST Authors: @JerickShi @TerryJCZhang @bschoelkopf @conitzer @ZhijingJin 🤖 We introduce a three-stage endogenous promise protocol for repeated multi-agent games that asks not only whether LLM agents honor their public commitments when they can privately deviate, but also how model-on-model composition influences premeditated deception and persistent exploitation. 📊 Across six canonical games spanning binary and numerical action spaces, our evaluation of frontier models (GPT-5.2, Llama-4-Maverick, Claude-Opus-4.6) reveals: 🔹 Over 90% of promise-breaking instances are premeditated in agents' private plans. 🔹 Mixed-model groups with mismatched communication frameworks create systemic, persistent payoff gaps of up to 5.00 points from Round 0. 📄 Paper: #MultiAgentSystems# #LLMs# #GameTheory# #AI# #ICML2026#

Google acaba de soltar un curso de solo 1 hora sobre Ingeniería Agentiva que destroza a la mayoría de cursos de pago 🔥 Timestamps: 00:00 → Cómo construir tu primer agente de IA 08:24 → Memoria de agente (corta, persistente y larga) 28:34 → Bucles agentivos y agentes de larga duración 40:04 → MCP vs API (esto solo ya vale la pena) 1:00:22 → Sistemas multiagentivos En 60 minutos aprendes más que en 10 cursos pagos. Míralo hoy. Luego lee el artículo y construye un sistema agentivo que se auto-mejora solo. ¿Lo vas a ver ahora o lo guardas para “después”? 👀

📣 We are presenting 6 main conference papers 🚀and 14 workshop papers (including 🏆2 Best Papers🏆) at #ICML2026# in Korea! Also hosting one of the largest workshops, Trustworthy AI for Good, on July 10th 🌍❤️. We push the frontiers on #AISafety#, #MultiAgent#, and #CausalReasoning# at @JinesisLab! 🎉 Huge congratulations to all collaborators and co-authors. Excited to discuss these projects in Seoul! Feel free to reach out and talk to our 20+ members and collaborators in Korea @ZhijingJin, @_AndreiMuresanu, @iarthsingh, @ChanglingXavier, @davidguzman1120, @EmanuelTewolde, @ettogran, @FurkanDanismann, @Jerick1380, @PepijnCobben, @rishit_dagli, @_rfaulk, @SimkoSamuel, @TerryJCZhang, @vantru0ng, @zhxiao03, @x_angelohuang, @yahang_qi, @ozzaney0101, @_yongjinny. Happy for collaboration on any of the above topics 🤝 EuroSafeAI, University of Toronto, ETH Zürich, Max Planck Institute for Intelligent Systems Main conference spotlight 🌟 Safe Models Do Not Guarantee Safe Societies: The Case for Sociopolitical Risk Main conference posters 📌 CauSciBench: Can LLMs Automate Causal Inference in Real-World Scientific Research? 📌 Training with Honeypots: Reshaping How LLMs Fail 📌 CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas 📌 Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution 📌 LLM for Physics Research Requires Domain-Specialized Training and Tooling Workshop best papers 🏆When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games BEST PAPER@NExT-Game Workshop 🏆Transferability for General Reasoning: An Automated Curriculum for Multi-Domain LLM RL BEST PAPER@RLxF Workshop Workshop oral and spotlight 🎤 AF-ARENA: A Multi-Dimensional Evaluation Suite for Alignment Faking — AIWILD 🌟 Multi-Agent AI Systems Need Institutional Design, Not Just Model-Level Alignment — AI4GOOD Workshop papers 📄 The Wedge Questions: Latent Cultural Boundaries in LLMs via Persona Projection Divergence — Pluralistic Alignment 📄 GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory — NExT-Game 📄 Stargazer: A Scalable Model-fitting Benchmark Environment for AI Agents under Astrophysical Constraints — AI4Physics 📄 Test of Time: Rethinking Temporal Signal of Benchmark Contamination — FoGen 📄 CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas — AI4GOOD 📄 Weight-Level Defenses Improve LLM Agent Adversarial Robustness — AI4GOOD 📄 Evaluating Cooperation in LLM Social Groups through Elected Leadership — AI4GOOD 📄 Causal AI Scientist: Towards End-to-End Causal Inference with Large Language Models — AI4Research 📄What Game-Theoretic Benchmarks Miss: Strategic Silence in Multi-Agent LLMs — FAGEN 📄Proving Your Way to Cooperation: Formalizing Proof-Based Open Source Game Theory in Lean — AI4Math

Excited to share that #LatentMAS# has been accepted to ICML 2026 as a spotlight! 💻Code: 📄Paper: We push multi-agent collaboration into the latent space — beyond human language. Most multi-agent systems rely on text: agents reason in words, exchange messages, and repeatedly decode/re-encode information. But language can be slow, lossy, and unnecessarily constrained. 💡LatentMAS takes a different path: LLM agents reason and communicate directly through hidden embeddings. No text decoding. No extra training. No token-level message passing. Instead, agents collaborate through: 🧠 Autoregressive Latent Thoughts — hidden-state-level reasoning steps 🔁 Latent Communication — information sharing via KV-cache transfer 📌 Input-output Alignment — keeping latent representations in-distribution 🚀 Training-free Collaboration — plug-and-play with existing LLMs Why it matters: ✅ Up to +14.6% better accuracy on complex reasoning tasks ⚡ 4-4.6x faster end-to-end inference ✂️ 70.8%–83.7% reduction in output token usage A step toward multi-agent systems that collaborate not by speaking more, but by thinking together in latent space. #MultiAgentSystems# #ModelCollaboration# #LatentReasoning# #LLM# #AgenticAI# #ICML#

(1/2) Glad to announce our OpenMAIC! 🎉 Open-sourcing MAIC (Multi-Agent Interactive Classroom) from Tsinghua University — LLM-driven multi-agent classroom for scalable & adaptive online education. 🏗️ Core Architecture: ✅ MAIC-Craft: Read (multimodal extraction) → Plan (course components + agent generation) ✅ Adaptive Engine: Cognitive student modeling + Token-level personalization (RAG + Bloom's/ZPD/UDL) ✅ Multi-Agent Classroom: 1 Student + N Agents (Teacher, Assistant, 4 Peer Archetypes) ✅ Manager Agent: Class state receptor for turn-taking orchestration 🔗 Give it a try 👉🏻 GitHub: #AI# #EdTech# #MultiAgent# #LLM# #Research# #OpenSource# #Tsinghua#

Multi-agent workflows have a scaling problem! The agents inside the same workflow rarely need the same amount of capacity at the same time. Say you have a planner, multiple research agents, and a final synthesis agent. The planner might finish quickly while requests start piling up behind one of the research agents. Scaling the entire application would add capacity everywhere, even though only one part of the workflow actually needs it. CanyonOS is an open-source, workflow-aware platform for multi-agent Python applications and the harness between your agents. Each agent runs as a separate service with its own queue and replica limits, so the busy research agent can get additional capacity while the planner and synthesis agents stay where they are. It also handles communication between the agents and gives you visibility into what happens across the workflow after a request comes in. CanyonOS records the path a request took, including the agents it called, parallel branches, model usage, execution time, and estimated model costs. And you don't need to add tracing decorators or a separate instrumentation SDK throughout your agent code. You still integrate the workflow with CanyonOS, but tracing is handled at the platform layer. I also ran a multi-agent portfolio analysis workflow through it with separate agents working across three stocks. A single request produced 10 traceable steps across the workflow, and I could see which agents ran, where the LLM calls happened, how long each stage took, and the estimated model cost. For multi-agent applications, being able to operate each agent independently while still seeing the complete execution path becomes much more useful as the workflow grows. CanyonOS is open source. Link to the GitHub repo in the comments!

建议大家都去油管学 AI Agent，真的能打破信息差！ ① Jeff Su
节奏快、实用导向，适合普通人入门。他有大量把 Prompt、AI Agent、工具落地讲清楚的短视频（例如约 10 分钟的 AI Agents, Clearly Explained 播放量很高）。 内容直给，新手跟着做能上手。频道约 190 万订阅，风格轻松实用。 ② Andrej Karpathy
AI 领域真正的大神（前 Tesla AI、OpenAI 等）。他的 LLM 入门/深度讲解视频（1 小时版和更长的 Deep Dive）在圈内刷屏，原理、训练、应用讲得通俗，非技术背景也能跟上。干货密度极高。 ③ Tina Huang
前 Meta 数据工程师/科学家。录有《Building AI Agents In 44 Minutes》《AI Agents Fundamentals In 21 Minutes》等视频，擅长把复杂资料提炼成清晰框架和实操路径，非常适合作为入门第一课。频道约 140 万订阅。 ④ Dave Ebbelaar（Datalumina）
Datalumina 创始人，内容聚焦可落地的 AI 系统工程（生产级 Agent、架构、部署）和 AI 自由职业/创业建议。 实战导向，经验分享多，适合想真正做出系统的人。 ⑤ IBM Technology
IBM 官方频道，覆盖 AI、自动化、DevOps、量子等，常有领域专家讲解。内容广度好，解释相对严谨可靠，像一个小型 AI 知识库。适合建立系统性认知。 ⑥ Google Cloud Tech
Google Cloud 官方频道，更垂直深入（Vertex AI、Agent 相关、NotebookLM、实用工具等）。 如果说 IBM 更“广”，这个更“深”，尤其适合想用 Google 生态落地的人。 ⑦ Cole Medin
强烈推荐做 Agent 实操。重点讲生产级 AI Agent、n8n / LangGraph 全流程构建、本地部署、架构设计。 内容实战、有完整项目和仓库，适合从“会用”进阶到“能做出稳定系统”。在很多“学 Agent 必看”列表里常出现。 ⑧ Liam Ottley
偏商业落地和变现角度。有超长完整指南（如《How to Build & Sell AI Agents》），覆盖无代码/低代码构建、自动化业务、接单卖服务。 适合想把 AI Agent 技能直接变成收入的人。 ⑨ Andrew Ng / DeepLearningAI
Andrew Ng 是 AI 教育与实践的标杆人物。DeepLearningAI 频道有关于 Agentic AI、设计模式（reflection、tool use、planning、multi-agent）的短课程和讲解，质量高、结构清晰。 适合想建立扎实基础并理解“为什么这样设计”的人。 ⑩ Nate Herk
以 n8n + AI Agent 实操闻名，有完整的无代码/低代码 Agent 教程和长视频课程。 非常适合不想先写大量代码、又想快速做出能用的自动化 Agent 的人。 和 Cole Medin、Liam Ottley 互补。 学习建议（快速上手路径） 1.完全新手：Jeff Su + Tina Huang 先建立概念 → 再看 Karpathy 补原理。 2.想动手做：Tina Huang / Cole Medin / Nate Herk 跟做项目。 3.想做生意/自由职业：Liam Ottley + Dave Ebbelaar。 3.想系统扎实：Karpathy + Andrew Ng + IBM / Google Cloud 官方内容。 这些频道大多免费、更新相对及时，能有效打破信息差。 建议先看 1-2 个入门视频确认风格是否适合自己，再深入。 如果有具体方向（比如纯无代码、Python 工程、商业变现），可以再细化推荐。

谷歌刚刚放出一门 70 分钟的官方课，手把手教你打造一个7 * 24小时完全自动化的生产级 Agent 。 很多人学 Agent 看了几十个小时零散教程依然搭不出可用的东西，核心是没有搞清玩具 Demo 与工业级架构的边界。 谷歌这门 1 小时课程没有任何水分，只聚焦 5 个真正的工程核心： 1. 从 0 跑通首个 Agent：拆解模型调用、工具绑定与执行环境的基础闭环。 2. 状态持久化（Persistent Memory）：告别单次对话即失忆，构建跨会话的记忆索引与上下文回填。 3. 长时自主循环（Agentic Loops）：摆脱线性问答，让 Agent 具备自我反思、容错重试与多轮状态流转能力。 4. 吃透 MCP 核心架构：彻底理清 Model Context Protocol 与传统 API 的本质区别，学会构建标准化的上下文协议。 5. 多智能体协同系统（Multi-Agent）：把分散的专职 Agent 串联成高协作度的自动化系统。 完全免费。 比市面上价值700刀的课还要干货。 建议收藏，今晚就观看 。🔖