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PUB-005/Benchmark/Aug 2026

Research sandbox scorecards: early criteria for vision, LLM and spatial model evaluation

A lightweight scoring method for future lab sandboxes, covering best use case, speed, cost, privacy mode, failure patterns and recommended Simam blueprint.

Research Questions
  1. 01How should a business user compare AI models without reading raw leaderboard papers?
  2. 02What should be measured besides accuracy?
  3. 03How do we connect a model result to a practical blueprint or deployment path?
Method
  1. 01Score each model against best-for use case, speed, cost, privacy mode, deployment path, failure pattern and review burden.
  2. 02Test models through representative business tasks rather than abstract prompts alone.
  3. 03Attach each scorecard to a recommended blueprint such as inspection, asset inventory, museum collection or document review.
Evidence
  1. 01Research sandbox concept notes for vision, image, video, audio, LLM and spatial AI demos.
  2. 02Benchmark note: What a useful AI benchmark should show.
  3. 03Local intelligence project patterns from Medibase and Symplicare AI.
Next Steps
  • Define the first Vision Sandbox benchmark task set.
  • Create scorecards for 3-5 model families using public or locally runnable models.
  • Add a Build with this model CTA once Agent Studio or blueprint pages exist.
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