Cognition-Augmented Dexterous Manipulation A Structured Expert Knowledge Stream for Multimodal Action Models

In May 2026 the RLDX-1 paper from RLWRLD and KAIST showed that a Multi-Stream Action Transformer processing vision, language, torque, tactile, and memory streams reaches 86.8% success on the ALLEX humanoid benchmark, against around 40% for vision-language baselines such as π-0.5 and GR00T N1.6. In parallel, EgoScale (NVIDIA, 2026) established a clean log-linear scaling law between human-data volume and downstream robot performance, demonstrating that human manipulation data is a predictable supervision source. The architecture wants multimodal streams, and the data thesis wants more, better human capture. But RLDX-1's memory stream is a sliding cache of past model states, computationally derived and disconnected from the human expertise that produced the demonstration data in the first place. TacitForge has been building since April 2026 around a different idea: capture the cognition of a human expert alongside the multimodal sensor data, and treat that cognition as its own modality stream. This paper specifies the schema (the Cognitive Action Stream, or CAS), the integration pathway with MSAT-class architectures, and the case for why structured human expertise is the next frontier in dexterous manipulation. Validation through fine-tuning experiments is the next phase of the work, with dataset capture beginning in the New Zealand Whakairo pilot. Captured data feeds two outputs: the CAS dataset for robotics, and a structured human curriculum that returns the master's knowledge to apprentices in their own craft tradition.