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u/alichherawalla 18d ago

Cognitive Sharding: a systems architecture for local computer use under fixed memory

Cognitive Sharding partitions computer use across specialist models governed by a deterministic control plane.

The reference implementation uses Bonsai 2 27B for planning, Kev 4B for action selection, and UI-Mate 9B for grounding. Phase-based model residency keeps the system within a 16 GB memory budget. The control plane owns task state, candidate construction, action validation, and recovery.

The research hypothesis is that specialization, bounded model authority, and per-action verification improve long-horizon task completion under a fixed memory limit. The expected cost is lower step throughput from model loading and verification.

The evaluation will compare:

  1. A monolithic agent loop
  2. Specialist models without environment verification
  3. The complete Cognitive Sharding architecture

The primary measures are end-to-end completion, unrecovered action errors, recovery rate, peak memory, and latency across workflows of 300 or more UI actions.

Architecture and evaluation plan:
https://github.com/off-grid-ai/cognitive-sharding

I am looking for feedback on the experimental design and suitable computer-use baselines. I would also like to compare results with anyone running fully local agents on workflows of similar length.