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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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  "authorName": "Air Street Capital",
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  "notes": "Includes a process diagram illustrating a filtering pipeline for training documents.",
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      "text": "Data-centric “tamper-resistance” helps, but open weights remain inherently modifiable. Filtering and safety-tuned pretraining can raise the cost of adversarial fine-tuning, yet motivated actors can still re-enable capabilities.",
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      "text": "Multi-stage pretraining filters and safety objectives can make models resist simple adversarial fine-tunes and do so with little general-task loss and low extra compute.\nBut targeted fine-tuning on the order of 100Ms tokens can largely recover original capabilities and stacking stronger mitigations still loses to stacked attacks. So we must treat defenses as cost-raising friction, not prevention.\nFor high-risk domains (bio, cyber), helpful and harmful knowledge overlap. We could release models that are weak on these domains, and keep capable models behind API with monitoring and abuse controls, while recognizing even API-gated models can be finetuned once weights leak.\nThere are no \"tamper-proof\" open models today: only tamper-resistant ones under narrow threat models. Governance and release decisions should be designed accordingly.",
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      "text": "Data-centric \"tamper-resistance\" helps, but open weights remain inherently modifiable. Filtering and safety-tuned pretraining can raise the cost of adversarial fine-tuning, yet motivated actors can still re-enable capabilities. Policymakers should assume open access brings both benefits and persistent misuse risk.",
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      "text": "DEEP IGNORANCE: FILTERING PRETRAINING DATA BUILDS TAMPER-RESISTANT SAFEGUARDS INTO OPEN-WEIGHT LLMS",
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