Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA explores A novel method for continual machine unlearning that uses SVD-guided orthogonal subspace projection to prevent parameter collision and maintain model performance across sequential deletion requests.. Commercial viability score: 3/10 in Machine Unlearning.
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Canonical route: /paper/orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-lora
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Agent Handoff
Canonical ID orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-lora | Route /paper/orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-lora
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-loraMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.12526"
}
}source_context
{
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"mode": "paper",
"query": "Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA",
"normalized_query": "2604.12526",
"route": "/paper/orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-lora",
"paper_ref": "orthogonal-subspace-projection-for-continual-machine-unlearning-via-svd-based-lora",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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