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🚀 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 CAD：AI生成可打印的CAD模型 清华IEI实验室开源，4个Agent接力把文字变成可打印3D模型。把CAD生成拆成规划、设计、编码、质检四步，各阶段只传递结构化数据不带完整对话记录。 相比CAD Skill，token消耗降到1/116，成本降到1/13，通过率从97.9%升到99.3%。 Github：

Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.

We’ve decided to open-source a multi-agent harness we use internally at YC. We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!). The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.