Rohan Paul@rohanpaul_ai · XSakana Fugu 发布技术报告，提出智能正从模型转移到其周围系统。Fugu 是一个编排器，由数据训练的管理器动态选择最合适的专家模型，而非简单规则（如投票或固定分工）。Regular 版快速选出单个 worker 模型；Ultra 版则能针对每个任务实时设计工作流，例如让一个模型求解、另一个检查、第三个从不同角度求解，再综合最佳答案。工作流非预设，而是根据任务实时构建。
Sakana Fugu Technical Report
The idea is that intelligence is moving from the model to the system around it.
Fugu is an orchestrator reads the task， chooses which specialist model to use， and in the Ultra version can build small workflows where models critique， extend， or correct one another.
Most multi-model systems use simple rules， like ask 3 models and vote， or always send coding to 1 model and math to another.
Fugu is different because the manager is trained from data to learn which model is actually best for each kind of situation， including small details like "this looks like coding， but the hard part is debugging， so bring in the model that is better at debugging."
The mechanism has 2 versions.
Regular Fugu is the fast version， where it reads the user's request and quickly chooses 1 worker model from a pool， so the user experiences it like calling 1 model， but behind the scenes Fugu picked the model it thinks is best for that exact request.
Fugu-Ultra is the slower but stronger version， where it can create a small workflow， such as asking 1 model to solve， another model to check， another model to solve from a different angle， and then choosing the best model to combine the answers.
The special part is that the workflow is not fixed before the task starts， because Fugu-Ultra can design a different teamwork pattern for each question.
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