New research from UIUC and Stanford introduces RecursiveMAS, a framework that ditches text-based agent talk for latent-space recursion. The results: 8.3% better accuracy and up to 75.6% fewer tokens used! ainexusdaily.com/article/recurs… #AI #MachineLearning #RecursiveMAS #TechNews

LoopLLM shows how a single LLM can reason through latent inner loops. Can we extend this idea across LLM agents? Check out our #RecursiveMAS, where agents collaborate and form recursive optimizations in latent space for agentic AI. Strong results on efficiency and scaling!

🔥Can Agent Collaboration itself be scaled through recursion? 🚀In #RecursiveMAS, we introduce a new learning framework that casts the entire multi-agent system as a unified latent-space recursion, unlocking a powerful scaling axis for AI teams' collaboration. 🌐 Website: recursivemas.github.io 🤗 Paper: huggingface.co/papers/2604.25… RecursiveMAS unlocks a new scaling paradigm shift for agentic AI👇 Not just bigger models, not just more agents, but deeper connected collaboration. ✨ Key Features - 🔁 System-level latent recursion for MAS - 🔗 Lightweight Connector to Link Heterogeneous Agents - ⚙️ Inner-outer loop recursive learning for whole-system co-optimization - 🧩 Plug-and-play across all common MAS styles - ⚡ Super Efficiency for Inference 📊 The Results Across 9 benchmarks in math, science, medicine, search, and code generation, RecursiveMAS achieves: 🏆 +8.3% average accuracy improvement ⚡ 1.2×–2.4× end-to-end inference speedup 🪙 34.6%–75.6% token usage reduction Try it yourself! 🌐 Project: recursivemas.github.io 💻Code: github.com/RecursiveMAS/R… 🤗 HF Models&Collections: huggingface.co/RecursiveMAS 📄 Paper: arxiv.org/abs/2604.25917 #AgenticAI #MAS #RecursiveLearning #Agents