** 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧 ** Multi-agent systems are powerful, but they can drastically multiply inference costs. Many existing systems rely on fixed or densely activated agent pipelines without adapting computation to each query: Which agents actually need to be consulted? How deep should the reasoning go? And when is communication worth its compute cost? Presenting 𝐆𝐑𝐀𝐃𝐄 -- Gated Routing and Adaptive Depth for Efficient Reasoning 🔗 Preprint: arxiv.org/abs/2607.10836 GRADE optimises multi-agent reasoning by: 🧠 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧: We built a hierarchical system governed by lightweight gates that jointly manage agent selection, routing depth, communication, and pruning dynamically per query. ⚙️ 𝐂𝐨𝐆𝐑𝐏𝐎 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: We adapt GRPO for collaborative settings with a novel, critic-free RL recipe that assigns a shared advantage signal to all participating agents and gates during a rollout. 🔄 𝐇𝐨𝐭-𝐒𝐰𝐚𝐩𝐩𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐬: GRADE features an Expert Registry with per-agent calibration maps. You can swap out expert models at inference time using just 64 anchor queries, without retraining the gates. 🏆 At ~17B average active parameters, GRADE outperforms all baselines on GSM8K, GPQA, and MMLUPro -- beating the strongest baseline on MMLUPro by 4.8 points while using ~39% fewer active parameters. w/ @ScientificGhosh Do check out many more exciting works on small models and agentic coordination being developed as part of our mega project -- 𝐏𝐚𝐫𝐚𝐦𝐚𝐧𝐮 parmanu.lcs2.in/ @lcs2lab @iitdelhi #LLMEfficiency #MultiagentRounting #AgenticAI