Qianfan-OCR: A Unified End-to-End Model for Document Intelligence

We present Qianfan-OCR, a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and document understanding within a single architecture. It performs direct image-to-Markdown conversion and supports diverse prompt-driven tasks including table extraction, chart understanding, document QA, and key information extraction. To address the loss of explicit layout analysis in end-to-end OCR, we propose Layout-as-Thought, an optional thinking phase triggered by special think tokens that generates structured layout representations -- bounding boxes, element types, and reading order -- before producing final outputs, recovering layout grounding capabilities while improving accuracy on complex layouts. Qianfan-OCR ranks first among end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8), achieves competitive results on OCRBench, CCOCR, DocVQA, and ChartQA against general VLMs of comparable scale, and attains the highest average score on public key information extraction benchmarks, surpassing Gemini-3.1-Pro, Seed-2.0, and Qwen3-VL-235B. The model is publicly accessible via the Baidu AI Cloud Qianfan platform.

Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light

For Minimally Invasive Surgical (MIS) robots, accurate haptic interaction force feedback is essential for ensuring the safety of interacting with soft tissue. However, most existing MIS robotic systems cannot facilitate direct measurement of the interaction force with hardware sensors due to space limitations. This letter introduces an effective vision-based scheme that utilizes a One-Shot structured light projection with a designed pattern on soft tissue coupled with haptic information processing through a trained image-to-force neural network. The images captured from the endoscopic stereo camera are analyzed to reconstruct high-resolution 3D point clouds for soft tissue deformation. Based on this, a modified PointNet-based force estimation method is proposed, which excels in representing the complex mechanical properties of soft tissue. Numerical force interaction experiments are conducted on three silicon materials with different stiffness. The results validate the effectiveness of the proposed scheme.