Introducing NoesisLab/Kai-3B-Instruct What happens when you force a 3B model to reason entirely in its latent space ? Meet Kai-3B, our latest industrial-grade reasoning model fine-tuned using the Adaptive Dual Search (ADS) algorithm. GSM8K (0-shot, Direct Answer): 39.27% π€― (Llama-2-7B is ~14.6%) HumanEval (Pass@1): 39.02% π» (Overtakes Gemma-2-2B's 30%) MMLU (5-shot): 53.62% π (Crushing the 50% barrier) ARC-Challenge: 51.88%π― PIQA: 77.53% HellaSwag: 69.53% Kai-3B proves that reasoning density doesn't strictly require parameter bloat or verbose generation. It acts as a perfect, cold-blooded Agent action-engineβideal for JSON routing, SWE-bench patch generation, and anywhere you need absolute structured certainty without token waste.