Cost-optimal Sequential Testing via Doubly Robust Q-learning explores This paper introduces a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective data in clinical decision-making.. Commercial viability score: 7/10 in Clinical Decision Support.
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Freshness
Canonical route: /paper/cost-optimal-sequential-testing-via-doubly-robust-q-learning
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Agent Handoff
Canonical ID cost-optimal-sequential-testing-via-doubly-robust-q-learning | Route /paper/cost-optimal-sequential-testing-via-doubly-robust-q-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/cost-optimal-sequential-testing-via-doubly-robust-q-learningMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.11165"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Cost-optimal Sequential Testing via Doubly Robust Q-learning",
"normalized_query": "2604.11165",
"route": "/paper/cost-optimal-sequential-testing-via-doubly-robust-q-learning",
"paper_ref": "cost-optimal-sequential-testing-via-doubly-robust-q-learning",
"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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