https://preview.redd.it/fc0v3wjktkrh1.png?width=2408&format=png&auto=webp&s=a06d56b90787738ffde109bf72af890de2cfe491 Most computer-use systems ask one general model to plan, interpret the interface, select actions, and recover from errors. Cognitive Sharding separates these functions across specialist models: Bonsai 2 27B handles planning and reasoning. Kev 4B handles routine action selection. UI-Mate 9B handles visual grounding. A code-owned control plane schedules model residency within 16 GB, maintains task state, restricts the actions available to each model, and verifies the environment after every step. The architecture prioritizes accuracy and reliability over velocity. Model swaps and verification increase latency, but they contain errors before those errors propagate through workflows containing hundreds of actions. The broader proposition is that capable local agents depend on how cognitive functions are composed, not only on the capability of one model. Technical approach: https://github.com/off-grid-ai/cognitive-sharding Has anyone tested a similar specialist-model architecture for computer use? I am looking for comparable results, especially from fully local systems running complex workflows of 300 or more UI actions. submitted by /u/alichherawalla
Originally posted by u/alichherawalla on r/ArtificialInteligence
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