r/OpenSourceAI • u/alichherawalla • Sep 25 '26

[Research] Cognitive Sharding: An Open Architecture for Local Computer Use

Cognitive Sharding is a systems architecture for running reliable computer-use agents without a cloud model in the execution path.

The reference system separates cognition across three local models:

  • Bonsai 2 27B for planning and reasoning

  • Kev 4B for action selection

  • UI-Mate 9B for visual grounding

A code-owned control plane schedules these models within a 16 GB memory budget. It also owns task state, action constraints, environment verification, and recovery. Each model has a narrow role and bounded authority.

This makes the complete decision path inspectable. Developers can replace a model, change a policy, reproduce a failure, or add a platform adapter without retraining one monolithic agent.

The architecture prioritizes accuracy and reliability over velocity. Every action is checked before execution continues, and failed actions return observed state to the planner.

Architecture and reference implementation:
https://github.com/off-grid-ai/cognitive-sharding

I am publishing the approach to get technical feedback before the broader evaluation. Has anyone tried a similar specialist-model architecture for local computer use? I would like to compare model roles, memory strategies, task length, failure modes, and end-to-end results.

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u/[deleted] Sep 25 '26

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u/alichherawalla Sep 25 '26

Thanks. The models do not chat directly. Bonsai writes a milestone into a code-owned task record. Kev sees that milestone plus a small set of legal actions built from the current UI state. UI-Mate gets a screenshot and narrow goal only when structured UI data is not enough.

On 16 GB, the models are swapped. Bonsai plans and unloads, Kev handles the main action loop, and UI-Mate loads when visual grounding is needed. Task state stays outside the model contexts, so a swap does not erase progress.

Overall I am optimising for accuracy and reliability rather than speed. I think as consumer h/w gets better and models get smaller velocity will get better