Add a per-model, per-difficulty token-budget note to judge-selection: models
vary ~6x in tokens/task (Artificial Analysis), reasoning models emit ~8x more
and scale with difficulty (Epoch AI, DeepSeek-R1), and the depth-vs-breadth
trade (cap reasoning low + N passes on easy tasks, which doubles as the
repeat-variance check; don't truncate near model capability). Add Gu et al.
"A Survey on LLM-as-a-Judge" as a second survey anchor. Cache new quotes.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>