Required external assets

This public copy includes the APE-L(_D) checkpoint required by the main detector. It intentionally excludes generated artifacts, credential-bearing files, private data, historical experiment outputs, and full dataset image payloads.

Minimum smoke test

To validate one Orienter inference example, provide:

Asset	Expected location or interface	Required metadata
One representative XR screenshot	a test-only folder matching the question manifest	image license/permission and expected image ID
Matching question/context fixture	JSONL consumed by `approach/run_vlm.py`, generated by `scripts/generate_questions.py` when screenshots are available	selected prompt, provider, model ID, filename convention, and expected schema
App metadata cache	an external path supplied through `--app-metadata-cache`	`app_id`, app name, app description, source URL, version/date, license/permission
APE-L(_D) checkpoint	bundled at `approach/ovod/APE/ape_d_model_final.pth`	official source, Apache-2.0 license, SHA-256, and size recorded in `docs/MODEL_MANIFEST.md`
Built APE extension	local build/install under the target CUDA environment	generated from source after cloning; compiled artifacts are intentionally not redistributed
One supported provider credential	`openrouter-run` or an equivalent process-scoped secret manager	access to the selected OpenRouter model and an explicit spending limit
Expected prediction	private comparison fixture	expected candidate labels and approximate boxes

Run python -B scripts/verify_assets.py --repo-root . to validate the bundled checkpoint size. Add --hash-checkpoint for SHA-256 verification. When screenshots and metadata are mounted, pass --questions, --images-dir, and --app-metadata-cache to check image naming, image ID consistency, and metadata coverage. Pass --embedding-cache /path/to/embedding_dict.json to hash the external semantic cache against evaluation/cache_manifest.json before an offline evaluation.

The default Stage 2 runner also verifies the released checkpoint hash before loading it. Loading any different PyTorch checkpoint requires the explicit --trust-custom-checkpoint flag and should be done only after independent provenance and checksum verification.

Full paper reproduction

Provide or publish all of the following as versioned assets:

The 1,552-image XR UI screenshot payload and a canonical versioned annotation release. Selected COCO-format evaluation ground-truth splits are included in this repository, but must be tied to the public dataset version and license.
App, genre, and context-sensitive split manifests, including the extra non-interactable context annotations.
Steam/application metadata used for global context, preferably as a pinned JSON/JSONL cache that avoids live-page drift. The public runner fails closed on missing cache entries when --app-metadata-cache is supplied.
The complete prompt templates, demonstration examples, and the random-selection/seed policy.
Exact model identifiers and access paths for every Orienter variant evaluated in the paper. The code defaults to OpenRouter openai/gpt-5.6-sol; exact paper profiles are included for GPT-4o-2024-08-06, Claude 3.5 Sonnet, and Gemini 1.5 Pro. If a provider retires a historical route, supply equivalent access through a custom profile or direct vendor credentials and document the substitution.
The bundled APE-L(_D) checkpoint identity, official source, revision, license, and checksum in docs/MODEL_MANIFEST.md.
A working CUDA/PyTorch/Detectron2 APE environment with the APE native extension built locally from the released source tree.
The reviewed embedding-3 semantic cache identified by evaluation/cache_manifest.json. Mount it read-only and use offline mode for historical reproduction. Zhipu access is optional and should be enabled only to extend a separate cache copy when a new model produces an uncached category.
Expected per-split metrics or prediction checksums for a deterministic regression check.
Hardware, CUDA, PyTorch, Python, and package versions from the successful experiment environment. See docs/ENVIRONMENT.md for the current public build boundary.

Credential names found in code

The following names are referenced by the main or optional baseline paths:

OPENAI_API_KEY
OPENROUTER_API_KEY
GOOGLE_API_KEY
ANTHROPIC_API_KEY
ZHIPU_API_KEY
ARK_API_KEY
DASHSCOPE_API_KEY
DEEPSEEK_API_KEY
THEB_API_KEY
INTERNVL_API_KEY

Only OPENROUTER_API_KEY is needed for the recommended Orienter inference path; the other variables belong to optional evaluation or baseline code. Never send credentials through Git, issue trackers, logs, or README examples. Rotate any credential that has previously appeared in a file or experiment log.

Stage 1 sends full screenshots to the configured remote provider; reflection additionally sends crops and annotated scenes. Do not submit private or unauthorized screenshots. Review the provider's data-handling terms and keep generated candidates, predictions, traces, metadata caches, and error files outside the public repository because they can reveal source-image content or filenames.

Publication format

For each data/model asset, publish: human-readable name, stable URL, version/revision, SHA-256, license, expected local path, compressed/uncompressed size, and a minimal schema example. The bundled APE checkpoint should be published through Hugging Face/LFS-style large-file storage; keep private raw data and generated experiment outputs outside the public repository.