# Google DeepMind 论文提出智能 AI 委托框架

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-06-05 01:25
- AIHOT 分数：39
- AIHOT 链接：https://aihot.virxact.com/items/cmpzso0z006l2slkpcfdm9t5i
- 原文链接：https://x.com/rohanpaul_ai/status/2062586493521293799

## AI 摘要

Google DeepMind 论文《Intelligent AI Delegation》将任务委托视为一系列选择：是否委托、如何解释、如何验证结果。系统构建动态市场，智能体通过智能合约竞标任务，利用加密证明保证正确性与隐私。基于信任模型，避免过度委托（给 AI 难完成的任务）或不足委托（自己做 AI 能胜任的事）。输出验证规则根据 AI 置信度决定接受与否，并有备用计划处理失败。还涵盖 AI 智能体间的委托与问责追踪，确保贡献符合整体目标。该框架使企业更安全地在日常运营中使用 AI。

## 正文

🗞️ 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.

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arxiv. org/abs/2602.11865

"Intelligent AI Delegation"