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"Schmidt Sciences Scaling AI Safety for a Multi-Agent World 2026 RFP for Oversight and Control of Multi-Actor AI Agent Ecosystems" is currently closed and not accepting applications.
Schmidt Sciences Scaling AI Safety for a Multi-Agent World 2026 RFP funds technical research advancing the safety, oversight, and control of large-scale multi-actor AI agent ecosystems.
The RFP focuses on emergent risks from interacting AI agents including collusion, deceptive coordination, principal-agent failures, multi-agent reinforcement learning safety, scalable oversight for agent swarms, and benchmark development for multi-agent safety evaluation. Tier 1 funds focused investigations up to $300K; Tier 2 funds broader programmes up to $1M over 1-2 years.
Strong fit for academic AI safety labs, applied research nonprofits, and frontier safety teams. Deadline August 8, 2026, with applications via Schmidt Sciences online portal.
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Or search similar grants →According to the current listing, eligibility includes: Open globally. Eligible applicants include academic researchers (PhD or equivalent), nonprofits, and independent research organizations. Industry researchers may apply as co-PIs or partners. Applications accepted from individual investigators and teams. Confirm the full requirements in the official notice before applying.
The current listing shows two-tier funding: Tier 1 awards up to USD 300,000 over 1-2 years. Tier 2 awards USD 300,000 to USD 1,000,000 over 1-2 years. Multiple awards expected. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was August 8, 2026, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Schmidt Sciences Scaling AI Safety for a Multi-Agent World 2026 RFP for Oversight and Control of Multi-Actor AI Agent Ecosystems is funded by Schmidt Sciences. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The Mozilla Foundation Fellows Program supports tomorrow's tech leaders working at the intersection of trustworthy AI, open source, and public interest technology. Mozilla Fellows pursue projects on AI auditing, algorithmic accountability, open source AI infrastructure, AI and democracy, content moderation governance, AI policy research, and bias and harm in machine learning systems. Fellows are embedded with host organizations (universities, NGOs, advocacy groups, news organizations) and receive a stipend plus a network of peers and amplification support. Mozilla has invested over $35 million across the last decade in building a better technology future. The 2026 cohort prioritizes Trustworthy AI and information ecosystem resilience.
Schmidt Sciences' AI in Science and Society initiative funds development of innovative AI tools that accelerate progress in engineering, physical sciences, and complex system design while also examining AI's societal implications. Funded scope includes domain-adapted foundation models for engineering and physical sciences, generative AI for materials discovery and molecular design, AI for complex system simulation and control, autonomous experimental platforms, and interdisciplinary projects bridging AI methods with deep scientific domains. The program also supports work on the societal context of scientific AI including responsible release, equitable access, and human-AI collaboration in scientific practice.
Schmidt Sciences opened a 'Scaling AI Safety for a Multi-Agent World' program with awards up to $1 million and an August 8, 2026 deadline. Against a backdrop of federal research slowdowns, here is why private science philanthropy matters more than ever, how these funders differ from federal agencies, and how researchers should approach them.
Read articleSchmidt Sciences' 2026 Science of Trustworthy AI RFP closes May 17 with two funding tiers — up to $1M (Tier 1) and $1–5M+ (Tier 2) over 1–3 years, with a 10% indirect cost cap. The three research aims target misalignment under distribution shift, predictive-validity evaluations, and oversight of superhuman systems. Here is why the structure favors team-based proposals.
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