# Miner Task Competition Mode

Taking LLM training or data crawling as examples, these types of AI-workers are usually for research-oriented businesses, such as cancer and earthquake prediction, which require continuous and significant computing power for competition. However, to avoid severe resource waste, we have set stricter requirements for the AI-worker's owner. The challenge of this business is to classify Tasks, allowing for distributed computing of multiple Tasks.

<figure><img src="/files/Jy9uYAkQlHkeBYmykoHt" alt=""><figcaption></figcaption></figure>

1. The AI-Worker classifies and orders tasks, as shown in the following figure, where tasks in a certain round are allocated to several Task queues, and each Task queue has an order list for counting Miners.
2. As shown in the figure below: Miners 1-6 have chosen Task1. The order list counts the Miners that have chosen this Task.
3. Miner 7-11, upon reviewing all Task queues, finds that Task 1 is extremely competitive, so they proactively choose Task 2 and update the order list 2.
4. At this moment, the newly joined Miner X comprehensively analyzes its own computing power and the difficulty of the Task, proactively choosing Task X because at this time, Task-X has the lowest difficulty and no competition.
5. Six Miners that chose Task 1 have returned their computational results, but the AI-worker's Monitor process will only select the first result submitted. However, to balance the distribution of rewards, Task 1 can also choose to retain the results provided by the top three Miners, similar to Ethereum's uncle block incentive, but this increases the complexity of the incentive, and we will address this issue in more detail in the future.


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