# OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis

arXiv: https://arxiv.org/abs/2603.06366 · HTML: https://mlsanity.com/paper/2603.06366/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 30 of 32)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning](https://mlsanity.com/paper/2505.15966/) (2025)
- [HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale](https://mlsanity.com/paper/2406.19280/) (2024)
- [MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning](https://mlsanity.com/paper/2502.19634/) (2025)
- [Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search](https://mlsanity.com/paper/2509.07969/) (2025)
- [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://mlsanity.com/paper/2508.05748/) (2025)
- [HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation](https://mlsanity.com/paper/2502.09838/) (2025)
- [MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2408.00765/) (2024)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning](https://mlsanity.com/paper/2511.11025/) (2025)
- [Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset for Panoramic X-ray Analysis](https://mlsanity.com/paper/2509.09254/) (2025)
- [T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation](https://mlsanity.com/paper/2404.01065/) (2024)
- [DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice](https://mlsanity.com/paper/2509.23344/) (2025)
- [FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs](https://mlsanity.com/paper/2409.13540/) (2024)
- [OralGPT-Omni: A Versatile Dental Multimodal Large Language Model](https://mlsanity.com/paper/2511.22055/) (2025)
- [MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning](https://mlsanity.com/paper/2509.21113/) (2025)
- [DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry](https://mlsanity.com/paper/2512.11558/) (2025)
- [Language-aware Multiple Datasets Detection Pretraining for DETRs](https://mlsanity.com/paper/2304.03580/) (2023)
- [GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation Models](https://mlsanity.com/paper/2307.12018/) (2023)

## Built on by (most cited first, 6 of 6)

- [OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis](https://mlsanity.com/paper/2605.27378/) (2026)
- [Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models](https://mlsanity.com/paper/2606.02914/) (2026)
- [PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology](https://mlsanity.com/paper/2607.27378/) (2026)
- [DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning](https://mlsanity.com/paper/2608.18878/) (2026)
- [AgenTeeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection](https://mlsanity.com/paper/2609.17800/) (2026)
- [MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning](https://mlsanity.com/paper/2610.02824/) (2026)

## Abstract (arXiv)

Panoramic dental radiographs require fine-grained spatial reasoning, bilateral symmetry understanding, and multi-step diagnostic verification, yet existing vision-language models operate under a static single-pass paradigm that limits their clinical reliability. In this paper, we introduce OralGPT-Plus, an agentic vision-language model designed to perform iterative and symmetry-aware diagnostic reasoning for panoramic dental radiograph analysis. To support this paradigm, we construct DentalProbe, a five-thousand-image dataset with expert-curated diagnostic trajectories that provide structured supervision for localized inspection and contralateral comparison. We further develop a Reinspection-driven reinforcement learning framework that encourages clinically meaningful re-examination and stabilizes long-horizon reasoning with rubric-based reward and conditioned diagnostic-driven reward. In parallel, we present MMOral-X, the first benchmark for holistic panoramic diagnosis, containing 300 open-ended questions and region-level annotations across multiple difficulty levels. OralGPT-Plus demonstrates consistent and reliable improvements over strong baselines on MMOral-X and established panoramic benchmarks, indicating the effectiveness of interactive and symmetry-informed reasoning. Our work highlights the value of agentic modeling for dental imaging and provides a foundation for future research in clinically aligned panoramic radiograph analysis.