Rohan Paul@rohanpaul_ai
32AI 编辑部评分,满分 100

Google DeepMind 论文提出智能 AI 委派框架

2026-07-08 13:50· 39天前
AI 导读

Google DeepMind 发表论文"Intelligent AI Delegation",将任务委派给 AI 视为结构化选择框架。建议建立动态市场,AI 智能体通过智能合约竞标任务,利用加密证明保证执行正确且不泄露私有数据。系统使用可验证数字证书证明技能,支持实时调整授权与责任的适应性。通过形式化信任模型,根据任务难度和历史表现防止过度委派或委派不足。框架涵盖输出验证机制、AI 间委派时的责任追踪,以及确保贡献与整体目标一致的分步方法。

🗞️ Google DeepMind's paper has some great advice on how we should actually give tasks to AI.

It is not just about telling an AI to do something and hoping for the best. Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward.

Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.

This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.

Instead of trusting a simple rating, agents will use verifiable digital certificates to prove their exact skills.

• Keeping things flexible when things change

This new system is built to be adaptive rather than stuck in its ways. It treats the handoff as a live process where authority and responsibility can shift around in real time. If the situation changes or something breaks, the framework helps manage that failure so the whole project does not go off the rails. It works for both humans giving tasks to AI and for when AI needs to handle things on its own.

• Finding the right amount of trust One of the coolest parts is how it handles trust. They made formal trust models that look at how hard a task is and how well the AI has done in the past. This stops people from "over-delegating," which is when you give an AI something it is not ready for. It also stops "under-delegating," which happens when you do all the work yourself even though the AI could have handled it easily.

• Double checking the work

You cannot just take an AI's word for it, so this framework has specific ways to validate the output. It sets up rules for when to accept an answer based on how confident the AI is. It also has backup plans ready to go if the AI fails. This is super important for real world jobs where trusting a machine blindly could cause a bunch of errors to pile up.

• When AI agents hire other AI agents

The framework also covers what happens when 1 AI agent hands a task to another AI agent. The system tracks who is actually accountable and makes sure the right authority is passed down the line so nothing gets lost in the network.

• Making sure the work actually fits It is a step by step approach to make sure the AI's contribution actually makes sense for the bigger goal. By treating this as a structured process, they are making it much safer for companies to use AI in their daily operations without worrying about constant mistakes.

----

arxiv. org/abs/2602.11865

"Intelligent AI Delegation"

来源:Rohan Paul · x.com

Google DeepMind 论文提出智能 AI 委派框架

Rohan Paul · @rohanpaul_ai · X·2026-07-08 13:50·39天前
AI 导读

Google DeepMind 发表论文"Intelligent AI Delegation",将任务委派给 AI 视为结构化选择框架。建议建立动态市场,AI 智能体通过智能合约竞标任务,利用加密证明保证执行正确且不泄露私有数据。系统使用可验证数字证书证明技能,支持实时调整授权与责任的适应性。通过形式化信任模型,根据任务难度和历史表现防止过度委派或委派不足。框架涵盖输出验证机制、AI 间委派时的责任追踪,以及确保贡献与整体目标一致的分步方法。

🗞️ Google DeepMind's paper has some great advice on how we should actually give tasks to AI.

It is not just about telling an AI to do something and hoping for the best. Instead, this framework looks at delegation as a string of choices where you figure out if you should even hand the task over, how to explain it, and how to check the work afterward.

Current systems rely on rigid rules that break when things fail unexpectedly. The researchers suggest building a dynamic market where agents bid on tasks using smart contracts.

This requires strict monitoring and cryptographic proofs to guarantee correct work without leaking private data.

Instead of trusting a simple rating, agents will use verifiable digital certificates to prove their exact skills.

• Keeping things flexible when things change

This new system is built to be adaptive rather than stuck in its ways. It treats the handoff as a live process where authority and responsibility can shift around in real time. If the situation changes or something breaks, the framework helps manage that failure so the whole project does not go off the rails. It works for both humans giving tasks to AI and for when AI needs to handle things on its own.

• Finding the right amount of trust One of the coolest parts is how it handles trust. They made formal trust models that look at how hard a task is and how well the AI has done in the past. This stops people from "over-delegating," which is when you give an AI something it is not ready for. It also stops "under-delegating," which happens when you do all the work yourself even though the AI could have handled it easily.

• Double checking the work

You cannot just take an AI's word for it, so this framework has specific ways to validate the output. It sets up rules for when to accept an answer based on how confident the AI is. It also has backup plans ready to go if the AI fails. This is super important for real world jobs where trusting a machine blindly could cause a bunch of errors to pile up.

• When AI agents hire other AI agents

The framework also covers what happens when 1 AI agent hands a task to another AI agent. The system tracks who is actually accountable and makes sure the right authority is passed down the line so nothing gets lost in the network.

• Making sure the work actually fits It is a step by step approach to make sure the AI's contribution actually makes sense for the bigger goal. By treating this as a structured process, they are making it much safer for companies to use AI in their daily operations without worrying about constant mistakes.

----

arxiv. org/abs/2602.11865

"Intelligent AI Delegation"

来源:Rohan Paul· x.com