Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

ArXi:2605.07639v1 Announce Type: new Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This challenge is especially relevant in procedural domains, where successful execution depends not only on explicit instructions, but also on implicit assumptions, contextual constraints, embodied skills, and experience-based judgments rarely documented.