Multi-agent collaboration that exchanges text every round is expensive 🔄 The fresh idea: scale the collaboration itself through latent-space recursion. Title: Recursive Multi-Agent Systems URL: 🔄 Overview RecursiveMAS reframes multi-agent collaboration as recursive computation in a unified latent space, rather than sequential text exchange. It connects heterogeneous agents through a RecursiveLink module, enabling latent thought generation and cross-agent state transfer. ❓ Challenges Solved Multi-agent systems (MAS) typically rely on text-based communication. ・Agents conversing in natural language consume huge token counts and cost a lot of compute ・The driving question: can agent collaboration itself be scaled through recursion? 💡 Methodology & Proposed Approach ・It frames the whole system as recursive latent-space computation ・The RecursiveLink module lightly connects heterogeneous agents with gradient-based credit assignment across recursion rounds ・Optimization uses inner-outer loop learning while maintaining theoretical stability 📊 Experimental Results Across 9 benchmarks spanning math, science, medicine, search, and code generation, with 4 collaboration patterns tested. ・Average accuracy improved by 8.3% ・Inference sped up 1.2x-2.4x ・Token usage reduced by 34.6%-75.6% It raises accuracy while substantially improving speed and cost at the same time. #MultiAgent# #LLM#