Intelligent Generator of Research (IGoR) - ISO

Amendment 03 provides revisions to Appendix C_Proposal Format and Intructions, as highlighted in yellow. This amendment also provides revisions to Appendix C.4_Price Proposal Spreadsheet (removes phase durations on labor tab). --------------------------------------------------------------------------------------- Amendment 02 extends the proposal due date and provides revisions to Appendix A_Program and Technical Description and Appendix E_Draft Model OT Agreement, as highlighted in yellow. --------------------------------------------------------------------------------------- Amendment 01 adds Appendix E_Draft Model OT Agreement and provides revisions to Appendix B_Solution Summary Format and Instructions, as highlighted. --------------------------------------------------------------------------------------- ARPA-H intends to host a hybrid Proposers Day in early June in the Washington, D.C. metro area. Additional information will be posted when it becomes availiable. The Intelligent Generator of Research (IGoR) program aims to eliminate longstanding inefficiencies in research and accelerate the development of effective therapies for complex diseases by creating an AI-enabled interoperable research ecosystem. IGoR would develop: 1) mechanistic disease models that encode causal biological relationships across scales, 2) an AI orchestration layer that identifies knowledge gaps and designs optimal experiments, 3) a layered protocol architecture that enables any qualified laboratory to execute the same experiment reproducibly, and 4) a distributed marketplace of validated laboratories that execute standardized protocols and return gold-standard data. Together, these components form a cycle of hypothesis generation, experimentation, and model refinement that enables researchers to create validated knowledge at least 10x more rapidly than conventional approaches. Ultimately, IGoR will empower researchers at every level to pursue bold, unconventional research directions that are currently too slow, too complex, or too resource?intensive.