Daily Arxiv

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Structure Transfer: an Inference-Based Calculus for the Transformation of Representations

Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification

AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation

Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks

DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression

Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation

EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation

TimeCopilot

First Order Model-Based RL through Decoupled Backpropagation

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms

Beacon: Post-Training Quantization with Integrated Grid Selection

Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?

Vectorized Attention with Learnable Encoding for Quantum Transformer

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

MultiGen: Child-Friendly Multilingual Speech Generator with LLMs

StreetViewAI: Making Street View Accessible Using Context-Aware Multimodal AI

Street-Level AI: Are Large Language Models Ready for Real-World Judgments?

The KG-ER Conceptual Schema Language

LOTS of Fashion! Multi-Conditioning for Image Generation via Sketch-Text Pairing

Conditional Video Generation for High-Efficiency Video Compression

TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP

Demographic-aware fine-grained classification of pediatric wrist fractures

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values

Stochastic Parameter Decomposition

Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

MiniCPM4: Ultra-Efficient LLMs on End Devices

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling

How Can I Publish My LLM Benchmark Without Giving the True Answers Away?

Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks

Transferable Mask Transformer: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation

RBT4DNN: Requirements-based Testing of Neural Networks

Robust Offline Imitation Learning Through State-level Trajectory Stitching

Beyond holography: the entropic quantum gravity foundations of image processing

KNighter: Transforming Static Analysis with LLM-Synthesized Checkers

FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response

CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection

Rapid Word Learning Through Meta In-Context Learning

Image Embedding Sampling Method for Diverse Captioning

Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

Extended Histogram-based Outlier Score (EHBOS)

A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

Breaking the Context Bottleneck on Long Time Series Forecasting

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision

ACING: Actor-Critic for Instruction Learning in Black-Box LLMs

Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance

Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory

Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss

Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames

AutoPETIII: The Tracer Frontier. What Frontier?

Long Input Sequence Network for Long Time Series Forecasting

FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers

MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming

Diffusion on language model encodings for protein sequence generation

Style Transfer to Calvin and Hobbes comics using Stable Diffusion

Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems

Plan Verification for LLM-Based Embodied Task Completion Agents

EigenBench: A Comparative Behavioral Measure of Value Alignment

Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models

Extending FKG.in: Towards a Food Claim Traceability Network

DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning

Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation

CP-Bench: Evaluating Large Language Models for Constraint Modeling

Axiomatics of Restricted Choices by Linear Orders of Sets with Minimum as Fallback

DMN-Guided Prompting: A Framework for Controlling LLM Behavior

Computational Basis of LLM's Decision Making in Social Simulation

Science Across Languages: Assessing LLM Multilingual Translation of Scientific Papers

Enhancing FKG.in: automating Indian food composition analysis

WASP: A Weight-Space Approach to Detecting Learned Spuriousness

Transferable Belief Model on Quantum Circuits

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions

Intelligence Primer

ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset

Delta Activations: A Representation for Finetuned Large Language Models

DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation

Towards a Unified View of Large Language Model Post-Training

No Thoughts Just AI: Biased LLM Recommendations Limit Human Agency in Resume Screening

IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation

SSGaussian: Semantic-Aware and Structure-Preserving 3D Style Transfer

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation

AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds

From Editor to Dense Geometry Estimator

Decoupled Entity Representation Learning for Pinterest Ads Ranking

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

HumAine-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning

Reinforcement Learning for Robust Aging-Aware Control of Li-ion Battery Systems with Data-Driven Formal Verification

An Empirical Study of Vulnerabilities in Python Packages and Their Detection

How many patients could we save with LLM priors?

Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding

MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision

Crossing the Species Divide: Transfer Learning from Speech to Animal Sounds

YOLO Ensemble for UAV-based Multispectral Defect Detection in Wind Turbine Components

Attention as an Adaptive Filter

TAGAL: Tabular Data Generation using Agentic LLM Methods

Enhancing Technical Documents Retrieval for RAG

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

Created by

Haebom

Author

Juraj Vladika, Mahdi Dhaini, Florian Matthes

Outline

This paper addresses the potential of large-scale language models (LLMs) to improve healthcare by supporting medical research and physicians. However, their reliance on static training data poses a significant risk when medical recommendations evolve in response to new research and developments. LLMs may provide harmful advice or fail clinical reasoning tasks if they retain outdated medical knowledge. To investigate this issue, we present two novel question-answering (QA) datasets derived from systematic reviews: MedRevQA (16,501 QA pairs covering general biomedical knowledge) and MedChangeQA (a subset of 512 QA pairs where medical consensus has changed over time). Dataset evaluations on eight leading LLMs reveal a consistent reliance on outdated knowledge across all models. Furthermore, we analyze the impact of obsolete pretraining data and training strategies to explain this phenomenon and propose future directions for mitigation, laying the foundation for developing more up-to-date and reliable medical AI systems.

Takeaways, Limitations

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Takeaways:

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Clearly highlights the problem of dependence on outdated medical knowledge when applying LLMs to the medical field.

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We present new QA datasets (MedRevQA, MedChangeQA) for evaluating outdated knowledge problems.

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Experimentally demonstrating consistent reliance on outdated knowledge across various LLMs.

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Analysis of the causes of the problem of outdated knowledge and suggestions for mitigation measures.

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Laying the foundation for developing more reliable medical AI systems.

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Limitations:

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The size of the presented dataset needs to be expanded for further research.

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The types of LLMs used in the analysis are limited.

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Further verification of the effectiveness of the proposed mitigation measures is needed.

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