Public Health & Policy

Health Policy

Latest AI and machine learning research in health policy for healthcare professionals.

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Visual Preference Optimization with Rubric Rewards

The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect th...

Nationwide Prediction of Missed and Cancelled Appointments Using Real-World EHR Data

ObjectivesTo develop and evaluate predictive models for unused outpatient appointments (missed or ca...

Cost-optimal Sequential Testing via Doubly Robust Q-learning

Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming...

Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

Remote sensing understanding inherently requires multi-resolution observation, since different targe...

Post-Hoc Guidance for Consistency Models by Joint Flow Distribution Learning

Classifier-free Guidance (CFG) lets practitioners trade-off fidelity against diversity in Diffusion ...

PolicyLong: Towards On-Policy Context Extension

Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods s...

Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization

Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR...

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling

Reinforcement-Learning-based post-training has recently emerged as a promising paradigm for aligning...

Action Images: End-to-End Policy Learning via Multiview Video Generation

World action models (WAMs) have emerged as a promising direction for robot policy learning, as they ...

Token-Efficient Multimodal Reasoning via Image Prompt Packaging

Deploying large multimodal language models at scale is constrained by token-based inference costs, y...

DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning

Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and w...

Measuring the Unmeasurable: A Diagnostic Sensor for AI Reasoning Pathology in Sequential Clinical Decision-Making

Large Language Models achieve impressive accuracy on medical benchmarks that present clinical inform...

KV Cache Quantization for Self-Forcing Video Generation: A 33-Method Empirical Study

Self-forcing video generation extends a short-horizon video model to longer rollouts by repeatedly f...

TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as...

Large language model scoring of medical student reflection essays: Accuracy and reproducibility of prompt-model variations

Purpose: Evaluate large language models (LLMs) for scoring medical student essays, and compare vario...

Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards

Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-lik...

Q-Tacit: Image Quality Assessment via Latent Visual Reasoning

Vision-Language Model (VLM)-based image quality assessment (IQA) has been significantly advanced by ...

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints

Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthca...

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