MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
hub
arXiv preprint arXiv:2408.04187 (2024) Medical Latent Memory Evolution 31
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
hub tools
citation-role summary
background 2
citation-polarity summary
roles
background 2representative citing papers
SAMe grounds complaints to organs, builds a lightweight patient anatomy model from one body image, and outputs probe initialization poses, outperforming keypoint baselines in real-robot liver and kidney trials.
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.
A unified framework for LLM agent memory is benchmarked, with a new hybrid method outperforming state-of-the-art on standard tasks.
A unified framework and large-scale comparison of graph-based RAG methods on QA tasks yields new high-performing variants obtained by recombining existing components.
ArchRAG proposes attributed-community hierarchical indexing and LLM clustering to improve accuracy and lower token usage in graph-based retrieval-augmented generation.
MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought methods.
ReCellTy constructs a knowledge graph with 18850 nodes and 48944 edges, retrieves relevant entities for differential genes, and applies multi-task LLM reasoning to improve single-cell type annotation over standard LLMs by up to 0.21 in human scores and 6.1% in semantic similarity.
CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.
A domain-specific LLM for TB care in South Africa, created by fine-tuning BioMistral-7B with QLoRA and GraphRAG on local guidelines, shows improved contextual alignment over the base model.
citing papers explorer
-
MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
-
SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound
SAMe grounds complaints to organs, builds a lightweight patient anatomy model from one body image, and outputs probe initialization poses, outperforming keypoint baselines in real-robot liver and kidney trials.
-
The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
-
Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models
OKH-RAG represents knowledge as ordered hyperedges and retrieves coherent interaction sequences via a learned transition model, outperforming permutation-invariant RAG baselines on order-sensitive QA tasks.
-
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
A unified framework for LLM agent memory is benchmarked, with a new hybrid method outperforming state-of-the-art on standard tasks.
-
In-depth Analysis of Graph-based RAG in a Unified Framework
A unified framework and large-scale comparison of graph-based RAG methods on QA tasks yields new high-performing variants obtained by recombining existing components.
-
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
ArchRAG proposes attributed-community hierarchical indexing and LLM clustering to improve accuracy and lower token usage in graph-based retrieval-augmented generation.
-
MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought methods.
-
ReCellTy: Domain-Specific Knowledge Graph Retrieval-Augmented LLMs Reasoning Workflow for Single-Cell Annotation
ReCellTy constructs a knowledge graph with 18850 nodes and 48944 edges, retrieves relevant entities for differential genes, and applies multi-task LLM reasoning to improve single-cell type annotation over standard LLMs by up to 0.21 in human scores and 6.1% in semantic similarity.
-
CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation
CLIN-LLM combines uncertainty-calibrated BioBERT classification with retrieval-augmented FLAN-T5 generation and safety post-processing to reach 98% accuracy on clinical cases while cutting unsafe antibiotic suggestions by 67%.
-
Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa
A domain-specific LLM for TB care in South Africa, created by fine-tuning BioMistral-7B with QLoRA and GraphRAG on local guidelines, shows improved contextual alignment over the base model.