Layout-as-Thought: structured layout recovery for complex documents
The Layout-as-Thought mechanism is an elegant solution to the end-to-end vs pipeline dilemma. Traditional OCR systems chain detection→recognition→comprehension modules, which introduces error propagation and latency. Your single-model approach with optional ⟨think⟩ tokens recovers layout analysis without sacrificing the unified architecture.
Two practical questions for production deployment:
The thinking phase adds structured bounding boxes and reading order — have you benchmarked the latency impact? For real-time document processing at scale, is the overhead acceptable, or should thinking be reserved for complex layouts only?
The 192-language support is impressive. How does performance degrade on low-resource scripts (Arabic, Thai, Devanagari) compared to Latin/CJK? In multilingual document pipelines, script mixing often causes alignment issues.
The 1.024 PPS with W8A8 quantization on A100 is strong — for edge deployment on constrained hardware (mobile, embedded), do you have benchmarks for INT4 or INT8 variants?
Looking forward to testing against Arabic document layouts with mixed RTL/LTR content.