Interfaze: The Future of AI is built on Task-Specific Small Models 文章

arXiv:2602.04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder through a shared embedding space. Specialized perceptual encoders handle optical character recognition (OCR) over complex multilingual PDFs, open-vocabulary object and graphical user interface (GUI) detection, and multilingual speech recognition with diarization. Each is exposed through a task-specific adapter and can be activated on its own, so a query touches only the parameters it needs. A built-in action foundation supplies a grounded external state: a proxied headless browser and scraper, a code sandbox, a multi-domain web index, and a scalable vector store. The decoder filters and merges these signals, reasons over them when a task requires it, and emits deterministic outputs built on confidence.