CAREAgent is a novel agent designed for fine-grained clinical order generation, achieving significant performance improvements on unseen benchmarks by integrating structured reasoning and tool usage.

The paper proposes ARSM-Agent, a full-link security enhancement framework, to significantly improve the adversarial robustness and security of large language model agents used for critical medical dec…

The paper surveys the use of LLMs for agentic NetOps and AIOps, arguing that operational reliability depends not on the model itself, but on robust surrounding machinery and workflow-centered evaluati…

The paper introduces SafeRx-Agent, a knowledge-grounded multi-agent framework that improves medication recommendation accuracy and safety by incorporating fine-grained ATC codes and rigorous safety ve…

The paper introduces AutoMedBench, a novel workflow-aware benchmark that evaluates autonomous medical-AI agents across a five-stage research process, revealing that agents struggle most with validatio…

The paper proposes a Sovereign AI architecture for clinical triage that ensures maximum security by performing all inference on-device and receiving data only through physically unidirectional channel…

This protocol paper outlines a comprehensive, AI-driven framework using CT imaging to predict the risk of colorectal anastomotic leak, aiming to shift surgical planning toward data-driven precision.

The paper proposes 'Think Fast, Talk Smart,' a pipeline that separates deterministic data analysis from LLM generation, showing that offloading recurring, structured tasks to code significantly improv…

The paper introduces SafeMed-R1, a clinically audited LLM that significantly improves safety and ethical alignment for medical applications, matching or exceeding resident performance on safety-critic…

The paper proposes HetMedAgent, a multi-agent framework, demonstrating that combining generalist LLMs with domain-specific specialist models significantly improves medical AI performance by enabling s…

The paper introduces ClinEnv, a novel interactive, multi-stage benchmark designed to evaluate LLMs' decision-making and information-gathering process during longitudinal inpatient medical simulations.

The paper proposes and validates a comprehensive four-layer Zero Trust security architecture designed to mitigate critical vulnerabilities in autonomous AI agents handling Protected Health Information…

AgentSOC introduces a multi-layered agentic AI framework designed to automate Security Operations Centers (SOCs) by integrating perception, anticipatory reasoning, and risk-based action planning to im…

This study analyzes ClinicalTrials.gov records to track the rising trend of AI in clinical trials and demonstrates that a hybrid human-AI screening approach is viable but requires clearer reporting of…

This paper introduces a proactive multi-modal assistant system and a large-scale dataset for procedural assistance.

The paper introduces EHRBench, a large-scale, automated, and reliable benchmark derived from real Electronic Health Records (EHRs) to rigorously evaluate the clinical decision-making capabilities of L…

The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion task…

The paper demonstrates that tool-augmented agentic AI can learn from prior field experiment data to automatically generate superior, domain-specific interventions, transforming one-shot A/B testing in…

The paper introduces ForeSci, a novel benchmark that evaluates LLM agents' ability to make forward-looking research judgments using only historical evidence, finding that explicit evidence organizatio…

The paper introduces the Causal Sensitivity Score (CSS), an interventional metric that reveals that standard coverage-based evaluations fail to detect critical responsiveness deficits in clinical LLMs…