# NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation

arXiv: https://arxiv.org/abs/2603.01756 · HTML: https://mlsanity.com/paper/2603.01756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [R2GenGPT: Radiology Report Generation with Frozen LLMs](https://mlsanity.com/paper/2309.09812/) (2023)
- [PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation](https://mlsanity.com/paper/2308.12604/) (2023)
- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [Cross-modal Prototype Driven Network for Radiology Report Generation](https://mlsanity.com/paper/2207.04818/) (2022)
- [A Medical Semantic-Assisted Transformer for Radiographic Report Generation](https://mlsanity.com/paper/2208.10358/) (2022)
- [Progressive Transformer-Based Generation of Radiology Reports](https://mlsanity.com/paper/2102.09777/) (2021)
- [MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks](https://mlsanity.com/paper/2407.03919/) (2024)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Multimodal Large Language Models for Medical Report Generation via Customized Prompt Tuning](https://mlsanity.com/paper/2506.15477/) (2025)
- [FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction](https://mlsanity.com/paper/2502.10156/) (2025)
- [A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic](https://mlsanity.com/paper/2601.18595/) (2026)

## Built on by (most cited first, 1 of 1)

- [When Models Learn to Ask Why: Adaptive Causal Reasoning for Trustworthy Medical Vision-Language Models](https://mlsanity.com/paper/2603.23085/) (2026)

## Abstract (arXiv)

Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-augmented pipelines achieve progress in fluency but remain vulnerable to visual-linguistic biases, factual inconsistency, and lack of explicit multi-hop clinical reasoning. We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, clinically grounded reports. The system maps image features to probabilistic clinical concepts, composes differentiable logic-based reasoning chains, decodes those chains into templated clauses, and refines the textual output via retrieval and constrained language-model editing. An active sampling loop driven by rule-level uncertainty and diversity guides clinician-in-the-loop adjudication and promptbook refinement. Experiments on standard benchmarks demonstrate consistent improvements in factual consistency and standard language metrics compared to representative baselines.