docs/aspect_policy_description.md

Adaptive Canvas Policy

We do not use a fixed 1:1 canvas for all samples. Portrait composition prompts often imply different spatial structures: some are naturally square portraits, some need a vertical canvas to preserve full-body framing and upper/lower breathing room, and some need a horizontal canvas to carry environmental context, roads, coastlines, leading lines, or large negative space.

We therefore design a prompt-conditioned adaptive canvas policy. The policy uses the input prompt and a learned policy state to choose the canvas size before generation. Its keyword weights, decision thresholds, and candidate ratios were optimized on the training set through an iterative evolutionary search procedure. The selected canvas always normalizes the longer side to 1584 pixels.

For reproducibility, we release the final learned policy state together with the inference code. This allows reviewers to recover the same canvas selection used in our submission. For unseen prompts, the implementation falls back to a deterministic prompt-only rule policy.