But after a year and a half of major acts, queues and economic successes, the arena's redemption is well underway.
Source: Computational Materials Science, Volume 267
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首先，大模型本身没那么可靠：存在无法根除的幻觉问题、知识时效性问题，任务拆解和规划经常不合理，也缺乏面向特定任务的系统性校验机制。这样一来，以其为“大脑”的智能体使用价值会大打折扣：智能体把模型从“对话”推向“行动”，错误不再只是答错问题，而是可能引发实际操作风险；而真实业务任务往往是跨系统、长链路的，一次小错误会在链路中层层放大，令长链路任务的失败率居高不下（例如单步成功率为95%时，一个 20步链路的整体成功率只有约 36%）。
As the founding member of the backend team, I worked to establish the underlying technical architecture that powers the persistent live components of the game. As the backend team grew, we built numerous C# microservices running in Kubernetes hosted on Azure. Viewing this as a long-term live-service game, we designed our systems with that in mind. Multiple region-aware matchmaking flows. An internal web portal for customer support. Player reporting and moderation systems. Cross-platform account linking. Login queues. Extensive load testing. The list goes on and on.