Google DeepMind 联合多家机构发起最高 1000 万美元多智能体安全研究资助
Investing in multi-agent AI safety research
- 1Google DeepMind 联合 Schmidt Sciences、ARIA 等机构发起最高 1000 万美元的多智能体安全研究资助
- 2官方称现有安全评测多分析孤立模型,难以预测多智能体交互产生的涌现行为和群体性风险
- 3资助设沙盒测试环境、智能体网络科学、身份与信誉基础设施、监督与控制四个优先方向
- 4申请截止 2026 年 8 月 8 日,获资助名单预计 2026 年秋季公布,面向全球学术与独立研究者
Google DeepMind 把安全研究重心从单模型推向多智能体群体行为,并借外部研究网络补齐预测与监测工具缺口。
- 影响:同时运行多个智能体的企业不能只靠现有单模型评测覆盖群体交互风险;资助落地后沙盒、评测与监测类工具的供给可能增加;智能体身份与信誉等跨平台协议仍在压力测试阶段,跨组织对接需留调整余地。
- 参考:可考虑在多智能体项目预算中单独列出群体行为评测与监测开支;值得盘点已部署智能体之间的交互接口与权限边界;有研究能力的团队可对照四个优先方向准备提案,截止 2026 年 8 月 8 日。
- 依据:Google DeepMind 官方博客口径,合作方含 Schmidt Sciences、ARIA 等,资助上限 1000 万美元;文中自述目前缺乏预测与监测群体行为转变的工具。
June 11, 2026 Responsibility & Safety
Google DeepMind, Schmidt Sciences, the Cooperative AI Foundation, ARIA and Google.org
Scaling AI Safety Research for a Multi-Agent World
For the past decade, we’ve focused on making individual AI models more capable, helpful and safe. Today, Google DeepMind — together with Schmidt Sciences, the Cooperative AI Foundation, the Advanced Research and Invention Agency, and supported by Google.org — is announcing a new technical research funding call of up to $10M for researchers worldwide.
As AI technology scales, we’re entering a new era. Soon, millions of AI agents — built by different organizations — will interact across digital environments, communicating, negotiating and transacting with one another.
When these systems interact, they must do so safely and predictably. This shift creates a vital opportunity: we can strengthen the safety and stability of the entire AI ecosystem from the very beginning.
The funding call focuses on the study of how large-scale multi-agent AI systems behave as a group, and how we can provide frameworks to understand and mitigate against potential risks. By empowering researchers globally, we aim to solve the “invisible” safety risks that arise when independent systems interact across different networks.
Why the agent ecosystem matters
When large groups of AI agents interact, new collective behaviors and capabilities can emerge suddenly. Currently, we lack the tools to predict, measure and monitor these transitions. Most safety evaluations analyze models in isolation. However, as we and others have previously argued, interacting autonomous agents can produce complex, "emergent" behaviors that are difficult to anticipate.
Because this is a new area of research, it is critical to understand how these shifts occur. For example, could they cause an unpredictable flurry of economic activity or lead to new security challenges? Understanding how to manage these system-wide behaviors is our core objective.
Scaling the frontier of multi-agent safety research
Although foundational frameworks for multi-agent safety exist, the rapid evolution of these systems requires an immediate, large-scale expansion of research.
Our 2025 research established a framework for understanding these interactions, while our recent work on AI Agent Traps explores vulnerabilities agents face in adversarial environments. Now, we must move faster. We are at a critical juncture where the complexity of multi-agent interactions is outpacing existing safety models.
This funding call aims to accelerate progress by supporting a global network of independent researchers. A diverse community is essential to ensure safety standards are transparent and robust for everyone.
This effort also advances the mission of Schmidt Sciences’ Science of Trustworthy AI and AI Agents programs, which support foundational work on understanding and mitigating risks from frontier AI systems, as well as ARIA’s Scaling Trust programme, which seeks to unlock new forms of cyber-physical multi-agent coordination.
A collaborative call to action
No single lab can solve multi-agent safety alone. We invite academic and independent researchers to submit proposals in four priority areas:
- Sandboxes and testbeds: Building realistic, reproducible environments to evaluate, compare and accelerate progress across all areas of multi-agent safety. This includes virtual marketplaces, simulated ecosystems and multi-organisation workflows.
- The science of agent networks: Understanding the safety-relevant properties of interacting agent populations, including investigating how collective capabilities emerge and scale, how networks fail or become volatile and how to detect dangerous, unexpected population-level properties.
- Strengthening agent infrastructure: Stress-testing the protocols for identity, reputation and commitment that are secure cross-platform agent interactions.
- Oversight and control: Developing methods to monitor deployed agent populations and mitigate collective harms at scale.
How to participate
We invite researchers to review our call for proposals and join us in building a safe foundation for a multi-agent future.
The deadline to apply is August 8, 2026, with awardees expected to be announced in Autumn 2026.
For more details on technical requirements and the application process, visit our application portal.
来源:Google DeepMind:Blog · deepmind.google