| # Pretrained diagnostic baselines |
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| Pinned local downloads for research only; no external inference endpoint is used. |
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| | 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 | |
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| 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. |
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| Sources: [Qwen model card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), |
| [MiniLM model card](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2). |
| The metadata and revisions were checked through the authenticated HF CLI. |
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| Run from the project root after installing research extras: |
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| ```sh |
| 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 |
| ``` |
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| 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. |
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| 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. |
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