RoboPhD: Evolving Diverse Complex Agents Under Tight Evaluation Budgets explores RoboPhD optimizes AI agents efficiently under tight evaluation budgets using a novel validation-free Elo-based evolutionary approach.. Commercial viability score: 6/10 in AI Optimization.
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Freshness
Canonical route: /paper/robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgets
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Agent Handoff
Canonical ID robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgets | Route /paper/robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgets
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgetsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.04347"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "RoboPhD: Evolving Diverse Complex Agents Under Tight Evaluation Budgets",
"normalized_query": "2604.04347",
"route": "/paper/robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgets",
"paper_ref": "robophd-evolving-diverse-complex-agents-under-tight-evaluation-budgets",
"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 6.0
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