arXiv:2603.13297v1 Announce Type: cross Abstract: Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability, and cost. Machine learning (ML) offers promise but is hindered by small ESUS cohorts and […]
A Hybrid AI and Rule-Based Decision Support System for Disease Diagnosis and Management Using Labs
arXiv:2603.14876v1 Announce Type: new Abstract: This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses […]
Diverse communities promote the coexistence of closely-related strains through emergent equalization and stabilization
arXiv:2603.15052v1 Announce Type: new Abstract: Microbial communities harbor extensive fine-scale diversity: closely-related strains of the same species coexist alongside many distantly-related taxa. Yet strain coexistence remains poorly understood, largely because most studies neglect the diverse communities in which strains are embedded. Here we combine community ecology and statistical physics to study the dynamics of closely-related […]
SCAN: Sparse Circuit Anchor Interpretable Neuron for Lifelong Knowledge Editing
arXiv:2603.15226v1 Announce Type: new Abstract: Large Language Models (LLMs) often suffer from catastrophic forgetting and collapse during sequential knowledge editing. This vulnerability stems from the prevailing dense editing paradigm, which treats models as black boxes and relies on coarse-grained parameter interventions that inevitably disrupt preserved knowledge. To address this, we propose SCAN (a sparse editing […]
The Neuroscience of Transformers
arXiv:2603.15339v1 Announce Type: new Abstract: Neuroscience has long informed the development of artificial neural networks, but the success of modern architectures invites, in turn, the converse: can modern networks teach us lessons about brain function? Here, we examine the structure of the cortical column and propose that the transformer provides a natural computational analogy for […]
Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting
arXiv:2603.15452v1 Announce Type: new Abstract: Existing time series forecasting methods primarily rely on the numerical data itself. However, real-world time series exhibit complex patterns associated with multimodal information, making them difficult to predict with numerical data alone. While several multimodal time series forecasting methods have emerged, they either utilize text with limited supplementary information or […]
OpenClaw-RL: Train Any Agent Simply by Talking
arXiv:2603.10165v1 Announce Type: cross Abstract: Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning source. We present OpenClaw-RL, a framework built on a simple observation: next-state signals are universal, […]
The Missing Red Line: How Commercial Pressure Erodes AI Safety Boundaries
arXiv:2603.13250v1 Announce Type: cross Abstract: What happens when an AI assistant is told to “maximise sales” while a user asks about drug interactions? We find that commercial system prompts can override safety training, causing frontier models to lie about medical risks, dismiss safety concerns, and prioritise profit over user welfare. Testing 8 models in scenarios […]
Quality Assessment of Public Summary of Training Content for GPAI models required by AI Act Article 53(1)(d)
arXiv:2603.13270v1 Announce Type: cross Abstract: The AI Act’s Article 53(1)(d) requires providers of general-purpose AI (GPAI) models to publish a sufficiently detailed public summary about the content used for training based on a template provided by the AI Office. The stated goal of this obligation is to increase transparency regarding the data used for training […]
ICaRus: Identical Cache Reuse for Efficient Multi Model Inference
arXiv:2603.13281v1 Announce Type: cross Abstract: Multi model inference has recently emerged as a prominent paradigm, particularly in the development of agentic AI systems. However, in such scenarios, each model must maintain its own Key-Value (KV) cache for the identical prompt, leading to substantial memory consumption. This explosive growth of KV caches forces LLM serving systems […]
Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs
arXiv:2603.13292v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as jailbreaking but also to inadvertently generating harmful content for benign users. While internal safety alignment via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is a primary mitigation strategy, current methods […]
Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval
arXiv:2603.13301v1 Announce Type: cross Abstract: Prompt-only, single-step LLM query rewriting, where a rewrite is generated from the query alone without retrieval feedback, is commonly used in production RAG pipelines, but its effect on dense retrieval is poorly understood. We present a systematic empirical study across three BEIR benchmarks, two dense retrievers, and multiple training configurations, […]