RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair explores Interactive machine unlearning for language models to empower users with personal data control.. Commercial viability score: 6/10 in AI Ethics and Privacy.
Use This Via API or MCP
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/repair-interactive-machine-unlearning-through-prompt-aware-model-repair
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID repair-interactive-machine-unlearning-through-prompt-aware-model-repair | Route /paper/repair-interactive-machine-unlearning-through-prompt-aware-model-repair
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/repair-interactive-machine-unlearning-through-prompt-aware-model-repairMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.12820"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair",
"normalized_query": "2604.12820",
"route": "/paper/repair-interactive-machine-unlearning-through-prompt-aware-model-repair",
"paper_ref": "repair-interactive-machine-unlearning-through-prompt-aware-model-repair",
"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.
Research neighborhood
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Preparing verified analysis
Dimensions overall score 6.0
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