🚀 MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale How can we make small models stronger for on-device agent deployment? MERA uses stronger models to guide an iterative loop of RL/GRPO, skill learning, and router optimization. Student failures become verified demonstrations, reusable SkillBook procedures, and LoRA updates, helping the small model take on more work over time. 🔥 Results: Qwen2.5-Coder-1.5B: 28.7% → 49.7% coding pass Qwen3.5-2B on TAU-2: 14/35 → 18/35 Fine-tuned 2B matches an unadapted 4B model Don’t just route around small models. Evolve them. 📄 💻

Excited to share that TwinRouterBench has been accepted to the #RLEval# Workshop at #CAIS2026# 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: Code: Dataset: Website: #LLM# #AgenticAI# #LLMRouting# #Benchmark# #SWEBench#