Task quality
How well does the result address the objective?
Evaluation across applications
Did the action solve the problem?
A useful AI system needs to understand the task, choose a feasible action, and respond to what happens. Make those steps comparable.
Give every comparison a common starting point.
Define the task and the information available before comparing approaches.
01 / Task
What matters?
Set the objective, starting scene, and condition for completion.
02 / Observations
What can AI know?
Specify the scene information and measurements available to the system.
03 / Actions
What can change?
Set permitted changes, tool access, physical limits, and resource budgets.
04 / Outcomes
What happened?
Review the result, the constraints respected, and the steps that led there.
A COMMON STANDARD
Look beyond a single score.
Understand the outcome and the cost of reaching it.
Physical feasibility
Do the choices respect the scene and the permitted limits?
Tool use
Were the right tools used with appropriate inputs?
Resource use
Which measurements, computations, and actions were needed?
Robustness
How does the approach respond to defined changes in the task or environment?
Decision trace
Can the observations and actions explain the final result?
One framework. Three practical questions.
Evaluate the decision that matters in each application.
Wireless deployment
Which sites and settings address the coverage objective within the installation limits?
Site choices, spatial trade-offs, and deployment constraints.
Explore applicationAntenna design
Which paired changes support the phone–watch link across the required use conditions?
Both link directions, physical fit, and the contribution of each device.
Explore applicationRobotic active sensing
Which next position adds useful evidence about the installed radar?
Directional observations, probe alignment, and the movement each measurement requires.
Explore applicationFROM PREDICTION TO REALITY
Start with a model you can check.
Physical measurements test the model. Controlled tasks test the decisions made with it. Keep both in view.
Match the physical conditions
Compare prediction with corresponding measurements, including geometry, materials, and sensor setup.
Keep comparisons consistent
Use the same task conditions and declared resources. Include unsuccessful attempts and remaining uncertainty.
Learn from the difference
Separate a model mismatch, a tool problem, and a poor decision so the next improvement has a clear target.