Health
1353 storiesPublic health and medicine worldwide — sourced from WHO News.
Structure-Behavior Coalescence and the Limits of Traditional Systems Theory
arXiv:2609.00042v1 Announce Type: new Abstract: This paper examines a foundational assumption in traditional systems theory, namely that structure (the organization of components) and behavior (the evolution of system ac…
What Is a System? An Interaction-Based Account of Structure-Behavior Coalescence in General Systems Theory
arXiv:2609.00043v1 Announce Type: new Abstract: The question of what constitutes a system remains fundamental to General Systems Theory. Existing definitions commonly characterize a system in terms of elements, relations…
Dense Weak Hiding: Closing Complexity Gaps in Nonconvex and PL Finite-Sum Optimization under Individual Smoothness
arXiv:2609.00045v1 Announce Type: new Abstract: Under individual smoothness, the optimal incremental first-order oracle (IFO) complexity of nonconvex finite-sum optimization has remained open. Known algorithms use $O(n+\…
RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery
arXiv:2609.00046v2 Announce Type: new Abstract: Rapid and reliable disaster mapping of impacted areas, damaged infrastructure, and affected populations is essential for emergency response and recovery. However, existing …
Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training
arXiv:2609.00047v1 Announce Type: new Abstract: Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing multi-task graph pre-train…
GUI-CC: Benchmarking Contextual Consistency of GUI World Models as Agent Environments
arXiv:2609.00048v1 Announce Type: new Abstract: GUI world models are increasingly evaluated as one-step next-screen predictors, yet their intended use is often as multi-step environments for GUI agents. This mismatch lea…
REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
arXiv:2609.00049v1 Announce Type: new Abstract: Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer…
Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness
arXiv:2609.00050v1 Announce Type: new Abstract: Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and infrastructure, deploy s…
From Detection to Refusal: Safer LLMs via Circuit-Guided Weight Scaling
arXiv:2609.00051v1 Announce Type: new Abstract: Despite extensive alignment efforts, Large Language Models (LLMs) remain vulnerable to generating unsafe content under adversarial prompting, yet the internal mechanisms by…
AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes
arXiv:2609.00052v1 Announce Type: new Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to save deployment costs.…
Convergence issues in Relational Concept Analysis based on AOC-posets
arXiv:2609.00054v1 Announce Type: new Abstract: Formal Concept Analysis (FCA) is an approach for conceptual classification building and rule discovery from a binary table describing a set of objects by a set of attribute…
Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment
arXiv:2609.00055v1 Announce Type: new Abstract: Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference, limiting their utility without task-specific labeled data. W…
ValueGraph: Value-Signal Guided Graph Pre-training for Contextualized User Representation
arXiv:2609.00057v1 Announce Type: new Abstract: Value signals are aggregated user-level moral representations that capture users' inferred value-related tendencies from their online discourse. User behavior on social med…
CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language
arXiv:2609.00058v1 Announce Type: new Abstract: Developing high-performance CUDA kernels demands specialized knowledge in algorithm implementation, correctness validation, and hardware-aware parallel optimization, creati…
DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction
arXiv:2609.00059v1 Announce Type: new Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with th…
A Formal Analysis of Agent Payment Protocols
arXiv:2609.00060v1 Announce Type: new Abstract: Agent payment protocols are emerging as a key transaction layer for autonomous commerce, enabling AI agents to purchase goods and services and execute payments on users' be…
ReNFT: Repairing Mode Collapse in Reward Post-Training via Internal Probability-Mass Recalibration
arXiv:2609.00061v1 Announce Type: new Abstract: Reward post-training of diffusion generators inevitably concentrates probability mass on a few reward-favored modes, a mode collapse that erases within-prompt diversity. Ex…
RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
arXiv:2609.00062v1 Announce Type: new Abstract: Data contamination undermines the reliable evaluation of large language models (LLMs) on mathematical problem solving. While rewriting-based evaluation mitigates memorizati…
Medical Causal Hypothesis Verification with Large Language Models
arXiv:2609.00063v1 Announce Type: new Abstract: The growing use of large language models (LLMs) for search and information retrieval underscores the need to evaluate their reliability in high-stakes domains such as healt…
Attention Sensitivity Is Not Enough: Dissociating Attention-Level and Behavioural In-Context Learning under Fine-Tuning
arXiv:2609.00064v1 Announce Type: new Abstract: In-context learning (ICL) lets large language models adapt to new tasks from demonstrations, and fine-tuning can erode this behaviour. Many preservation diagnostics inspect…