Every interaction your deployed AI agent handles could double as training data to make it smarter. That is the pitch of this paper. NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness 🧩 Overview The authors turn logs from a "routing harness" (the layer that decides which capability tier handles each request) directly into training data, with no extra synthetic data pipeline, to recursively improve the model. ❓ The problem it solves Recursive self-improvement needs a way for a system to observe its own capabilities and feed that back into training. Most approaches build a separate data pipeline for this, but the material is already sitting inside everyday routing logs. ⚙️ Method Execution trajectories are structured by user turn, quality-checked with six semantic dimensions plus structural validation, and routing scores are used to order a three-stage curriculum for both SFT and on-policy distillation. Evaluation results then steer the next batch of training data toward weak spots. 📊 Results Across 10 agentic, coding, and instruction-following benchmarks, the 4B model's macro-average rose from 58.94 to 64.87, and the 9B model from 65.60 to 69.04, beating public synthetic data by +6.26 macro-average points. #AIAgents# #SelfImprovement#