Zou Ting | Searcharxiv

arXiv2026

FastAgent: A Physics-Aware FastFluent-to-Fluent Workflow for Rapid CFD Screening and Industrial Steam Dewaxing Design

Industrial computational fluid dynamics (CFD) becomes costly when complex geometries, long transients and coupled physics must be evaluated across large design spaces. We introduce FastAgent, a physics-aware campaign controller built around FastFluent, a lightweight numerical engine for rapid physical screening. FastFluent supplies stage-specific fields and diagnostics; FastAgent allocates physical models, numerical resolution and simulation horizons by the active quantities of interest (QoIs), while returned Fluent discrepancies update subsequent decisions. Across six public CFD benchmark campaigns, the framework reduces normalised worst-QoI error by 5.68-99.14% and accelerates decision-relevant computation by factors of 1.53-15.36 relative to a matched Generic Agent control. Applied to high-pressure steam dewaxing in investment casting, FastAgent identifies nonuniform condensation heating as the dominant startup mechanism and separates rapid risk-window localisation from high-fidelity melt-completion confirmation. The rapid route requires 81.39% fewer cell-time updates than a fine-grid, small-time-step reference. Structural analysis localises fracture at the observed blade edge. Screening 131,072 outlet layouts and selectively evaluating candidates with Fluent and Abaqus identifies Sparse-6, which reduces simulated crack opening by 96.0% and connected crack extent by 66.7% relative to the Original design. These results demonstrate how rapid numerical evidence and adaptive fidelity control connect large CFD search spaces to mechanism-guided industrial design.

cs.AI↗

arXiv2025

DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry

Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning ability for precise diagnosis. To address these limitations, we present DentalGPT, a specialized dental MLLM developed through high-quality domain knowledge injection and reinforcement learning. Specifically, the largest annotated multimodal dataset for dentistry to date was constructed by aggregating over 120k dental images paired with detailed descriptions that highlight diagnostically relevant visual features, making it the multimodal dataset with the most extensive collection of dental images to date. Training on this dataset significantly enhances the MLLM's visual understanding of dental conditions, while the subsequent reinforcement learning stage further strengthens its capability for multimodal complex reasoning. Comprehensive evaluations on intraoral and panoramic benchmarks, along with dental subsets of medical VQA benchmarks, show that DentalGPT achieves superior performance in disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite having only 7B parameters. These results demonstrate that high-quality dental data combined with staged adaptation provides an effective pathway for building capable and domain-specialized dental MLLMs.

cs.CV↗