企业软件里的两种世界观,造出了不同的Agent

· · 来源:dev信息网

关于早报|防挖角,以下几个关键信息值得重点关注。本文结合最新行业数据和专家观点,为您系统梳理核心要点。

首先,宋健:步子迈太大是因为你对Agent能力的想象空间还太差。All in One这件事情之所以能成立,就是因为AI在这件事情上是无损的。

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其次,A growing countertrend towards smaller (opens in new tab) models aims to boost efficiency, enabled by careful model design and data curation – a goal pioneered by the Phi family of models (opens in new tab) and furthered by Phi-4-reasoning-vision-15B. We specifically build on learnings from the Phi-4 and Phi-4-Reasoning language models and show how a multimodal model can be trained to cover a wide range of vision and language tasks without relying on extremely large training datasets, architectures, or excessive inference‑time token generation. Our model is intended to be lightweight enough to run on modest hardware while remaining capable of structured reasoning when it is beneficial. Our model was trained with far less compute than many recent open-weight VLMs of similar size. We used just 200 billion tokens of multimodal data leveraging Phi-4-reasoning (trained with 16 billion tokens) based on a core model Phi-4 (400 billion unique tokens), compared to more than 1 trillion tokens used for training multimodal models like Qwen 2.5 VL (opens in new tab) and 3 VL (opens in new tab), Kimi-VL (opens in new tab), and Gemma3 (opens in new tab). We can therefore present a compelling option compared to existing models pushing the pareto-frontier of the tradeoff between accuracy and compute costs.

多家研究机构的独立调查数据交叉验证显示,行业整体规模正以年均15%以上的速度稳步扩张。

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随着早报|防挖角领域的不断深化发展,我们有理由相信,未来将涌现出更多创新成果和发展机遇。感谢您的阅读,欢迎持续关注后续报道。

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