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  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
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      "text": "As models are merged using existing methods (task arithmetic, TIES-Merging, etc.), the rank of the task vector space progressively decreases - meaning a 100-dimensional space of possible model behaviors might effectively shrink to just 20-30 dimensions, wasting the potential of additional experts.\nTheir subspace boosting method operates on the SVD-decomposed task vector space, explicitly preserving the rank by maintaining orthogonal components that represent each expert's unique contributions to the merged model.\nThey achieved >10% improvement on vision benchmarks including evaluation across multiple datasets when merging large numbers of experts, successfully merging up to 20 expert models with consistent performance gains (whereas traditional methods typically degrade after 5-10).",
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      "text": "Too many cooks?",
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