C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.
3s-trader: A multi-llm framework for adaptive stock scoring, strategy, and selection in portfolio optimization
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
AgenticVM reduces vulnerability scanner alerts by up to 98% and predicts missing CVSS attributes with 89.3% accuracy using a multi-agent LLM framework integrated with security tools and public databases.
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
citing papers explorer
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Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.
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AgenticVM: Agentic AI for Adaptive Software Vulnerability Management
AgenticVM reduces vulnerability scanner alerts by up to 98% and predicts missing CVSS attributes with 89.3% accuracy using a multi-agent LLM framework integrated with security tools and public databases.
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Comparative Analysis of Large Language Models in Healthcare
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.