Pretrained diagnostic baselines

Pinned local downloads for research only; no external inference endpoint is used.

Model	Revision	Hub license metadata	Purpose
Qwen/Qwen2.5-0.5B-Instruct	7ae557604adf67be50417f59c2c2f167def9a775	Apache-2.0	Local autoregressive action baseline
cross-encoder/ms-marco-MiniLM-L6-v2	233902d25c440f23af6f7d6e94d2946bac0bee0a	Apache-2.0	Semantic candidate reranking

Models are loaded with local_files_only=True and trust_remote_code=False. Model weights use safetensors. Qwen's source LICENSE and both model cards were downloaded with the checkpoints under the intermediate work directory.

Sources: Qwen model card, MiniLM model card. The metadata and revisions were checked through the authenticated HF CLI.

Run from the project root after installing research extras:

python -m baim.retrieval_audit --source ../../work/mind2web-source/data/train/train_10.json --model ../../work/pretrained/minilm-cross
python -m baim.qwen_baseline --source ../../work/mind2web-source/data/train/train_10.json --model ../../work/pretrained/qwen-0.5b --limit 8

History is teacher-forced: only prior human action descriptions are supplied. Neither evaluator receives the current labelled action or target as model input. This is not autonomous multi-step success. Only aggregate metrics and numeric per-step timing results are persisted, not raw page text, prompts or generations. The Qwen diagnostic scores action/target matching, not typed-value correctness.

The broad BM25 candidate pool includes generic roles because source annotations already classify those records as candidates. This offline choice is not a live DOM interaction policy. Live generic elements still require interactability evidence and execution validation.