BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning explores BID-LoRA, a parameter-efficient framework for Continual Learning and Unlearning, enabling precise knowledge deletion and efficient integration of new knowledge with minimal leakage.. Commercial viability score: 7/10 in Continual Learning.
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Freshness
Canonical route: /paper/bid-lora-a-parameter-efficient-framework-for-continual-learning-and-unlearning
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Agent Handoff
Canonical ID bid-lora-a-parameter-efficient-framework-for-continual-learning-and-unlearning | Route /paper/bid-lora-a-parameter-efficient-framework-for-continual-learning-and-unlearning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/bid-lora-a-parameter-efficient-framework-for-continual-learning-and-unlearningMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.12686"
}
}source_context
{
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"query": "BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning",
"normalized_query": "2604.12686",
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"paper_ref": "bid-lora-a-parameter-efficient-framework-for-continual-learning-and-unlearning",
"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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Dimensions overall score 7.0
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