Comment by frumiousirc

> Bicycle is the right shape. Pelican beak is excellent. Nice background.

Relative to other results I agree. But on an absolute measure, there is not a single element in the current bicycle that is real-world accurate and many elements are omitted or non-physical (eg, the transparent seat tube top, entire lack of a head tube).

Consider a series of followup benchmarks.

With a fresh context of the LLM under test, ask it to generate a list of findings for how the pelican-on-a-bicycle SVG that was produced is inaccurate compared what the real world scene might appear, accepting for the limitations of SVG as a medium. Then, feed back the list of findings to the original context for a second try. The benchmark can stop here by humans looking at the result and forming their own conclusion.

Next phase is to repeat the analysis phase using the 2nd context to determine what findings were satisfied and what new inaccuracies are found. These two differences can form a second benchmark.

Last phase is to iterate with the goal to drive the number of findings to zero.