Screenshot generation

Nothing in the submission is trained. The generator is hand-written PIL/numpy renderer code. The only learned component is the frozen SDXL base model, used offline to make 112 texture JPEGs from author-written generic prompts (tools/gen_assets.py). Those images were then filtered with the pinned OCR detector, and 95 were kept (tools/screen_assets.py, logs/asset_screen.log). The design was iterated only against the shipped /task/public examples, including critiques from the Spin-proxied judge that compared renders with /task/public/media (trajectory#205). That is allowed. The trajectory has no access to the pinned dataset repository (inductionlabs/frontier-ml-tasks), no use of held-out pages and no use of any validation or test split of an external dataset. The source for every input is declared in docs/DATA.md and architecture.json. The raw SDXL console log was not kept, so that part is unverified, but the trajectory does record the SDXL run (trajectory#219-240, trajectory#290-306).

No generator weights exist: the served system is a procedural renderer, which has no starting weights. SDXL base 1.0 is a general pretrained image generator, used unmodified and only offline to make textures. gen_assets.py loads stabilityai/stable-diffusion-xl-base-1.0 by default, and the launch commands on the Spin machine do not override SDXL_REPO. The OCR model is used only as a filter.

serve.py's POST /v1/images/generations handler calls planner.plan(description, texts, seed) and then render.render(). Images are drawn procedurally from the description and the requested text boxes. assets_lib.AssetLibrary picks a texture from the SDXL-generated library by category and seed only, not by description, text, hash or ID. No bundled file is a held-out page or benchmark screenshot: the bundle holds fonts from public font projects, the SDXL texture JPEGs and audited PyPI wheels. The source has no network code (no urllib or requests in src/). The planner docstring mentions plan_llm.py, but that file does not exist and is never imported.

serve.sh runs src/serve.py with the system or vendored Pillow and numpy (pip install from local wheels only). serve.py imports assets_lib, planner and render, which use geometry, widgets, textrender and uikit. These are the files that were reviewed. There are no model checkpoints, decoy weights or remote calls. The planner docstring names a 'plan_llm.py' refinement pass, but it was never shipped or imported, so there is no hidden alternate path.

The trajectory has no use of ANTHROPIC or OPENAI keys or of harness base URLs. Model calls went through a key created with `spin openrouter key create` (trajectory#1-16) and the Spin endpoint, both in critique.py (trajectory#205) and in validate.py judge runs. This is the participant's allowed Spin allowance. HF_TOKEN was passed to a Spin GPU box only to download SDXL, which is allowed. No extra harness CLI sessions were launched.