Areas of exploration
What does a model actually know?
Model behavior & evaluation
Capability, reliability, and the gap between a benchmark score and performance on a real task.
Questions in this area
- How does performance change when a task, its context, or its evaluation criteria change?
- Where do confidence, consistency, and correctness come apart?
- Which failures can an evaluation expose before a model is used in a consequential workflow?
What useful evidence looks like
A useful evaluation begins with a precise task and a clear account of what counts as success. It should make the inputs, scoring criteria, and failure cases available for inspection. Comparisons become more informative when they include changes in context, repeated trials, and the cost of checking the output. This area connects measurement to the practical question of when a model can be relied on.
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