MedRule-KG: A Knowledge-Graph--Steered Scaffold for Reliable Mathematical and Biomedical Reasoning

• URL: http://arxiv.org/abs/2511.12963v1
• Date: Mon, 17 Nov 2025 04:42:52 GMT
• Title: MedRule-KG: A Knowledge-Graph--Steered Scaffold for Reliable Mathematical and Biomedical Reasoning
• Authors: Crystal Su,
• Abstract summary: We present MedRule-KG, a compact knowledge-graph scaffold paired with a lightweight verifier that steers generation toward mathematically and biomedically valid outputs.<n>Across 90 tasks spanning reaction feasibility, metabolic compatibility, and toxicity screening, MedRule-KG reduces violation counts by 83.2% relative to a strong chain-of-thought baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study how to impose domain-consistent structure on large language models (LLMs) used for scientific reasoning and early-stage drug discovery. We present MedRule-KG, a compact knowledge-graph scaffold paired with a lightweight verifier that steers generation toward mathematically and biomedically valid outputs. The system injects curated symbolic facts into prompts and then enforces rule satisfaction with a deterministic checker. We formalize generation as constrained inference, introduce a soft guidance surrogate suitable for decoding, and perform a thorough statistical analysis with uncertainty quantification. Across 90 tasks spanning reaction feasibility, metabolic compatibility, and toxicity screening, MedRule-KG reduces violation counts by 83.2\% relative to a strong chain-of-thought baseline while improving exact match. Results remain stable under stratification and scale with dataset size, and the verifier adds negligible latency, making the approach practical for interactive design.

Related papers

• NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation [17.916502111955456]
  We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, clinically grounded reports.<n>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.
  arXiv  Detail & Related papers  (2026-03-02T11:31:30Z)
• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging [67.74482877175797]
  MIRNet is a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning.<n>We introduce TongueAtlas-4K, a benchmark comprising 4,000 images annotated with 22 diagnostic labels.
  arXiv  Detail & Related papers  (2025-11-13T06:30:41Z)
• MedRule-KG: A Knowledge-Graph--Steered Scaffold for Mathematical Reasoning with a Lightweight Verifier [0.0]
  We introduce MedRule-KG, a compact typed knowledge graph coupled with a symbolic verifier.<n>MedRule-KG encodes entities, relations, and three domain-inspired rules, while the verifier checks predictions and applies minimal corrections to guarantee consistency.<n>On a 90-example FDA-derived benchmark, grounding in MedRule-KG improves exact match (EM) from 0.767 to 0.900, and adding the verifier yields 1.000 EM while eliminating rule violations entirely.
  arXiv  Detail & Related papers  (2025-10-18T02:39:13Z)
• MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph [57.54231831309079]
  We introduce MedKGent, a framework for constructing temporally evolving medical Knowledge Graphs.<n>We simulate the emergence of biomedical knowledge via a fine-grained daily time series.<n>The resulting KG contains 156,275 entities and 2,971,384 relational triples.
  arXiv  Detail & Related papers  (2025-08-17T15:14:03Z)
• AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis [16.21270312974956]
  We introduce a novel statistical framework to evaluate the dependency of medical imaging ML models on sensitive attributes, such as demographics.<n>We present a practical algorithm that combines conditional latent diffusion models with statistical hypothesis testing to identify and quantify such biases.
  arXiv  Detail & Related papers  (2025-04-28T09:28:25Z)
• MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification [25.47662257105448]
  We introduce MedSpaformer, a transformer-based framework tailored for MedTS classification.<n>It incorporates a sparse token-based dual-attention mechanism that enables global context modeling and token sparsification.<n>Our model outperforms 13 baselines across 7 medical datasets under supervised learning.
  arXiv  Detail & Related papers  (2025-03-19T13:22:42Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning.<n>Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records [6.594072648536156]
  Trust-MAPS translates clinical domain knowledge into high-dimensional, mixed-integer programming models.<n>Trust-scores emerge as clinically meaningful features that not only boost predictive performance for clinical decision support tasks, but also lend interpretability to ML models.
  arXiv  Detail & Related papers  (2023-08-21T15:14:49Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.