Agent-Systems

Move beyond simple text generation and step into the era of autonomous AI. This category covers Agentic Systems and AI Agents that can plan, reason, and interact with digital environments 🌐. Explore cutting-edge research on hierarchical control, reinforcement learning, and multi-agent frameworks that are teaching AI to navigate software, use apps, and solve multi-step problems just like a human.

AgentDropoutV2: Test-Time Rectify-or-Reject Pruning for Multi-Agent Systems.

AgentDropoutV2: Test-Time Rectify-or-Reject Pruning for Multi-Agent Systems

AgentDropoutV2: Test-Time Rectify-or-Reject Pruning for Multi-Agent Systems | AI Security Research AISecurity Research Machine Learning About Multi-Agent Systems · arXiv:2602.23258v1 [cs.AI] · 16 min read AgentDropoutV2: Teaching Multi-Agent Systems to Self-Correct Through Test-Time Rectify-or-Reject Pruning A novel test-time framework that intercepts and iteratively rectifies erroneous agent outputs using retrieval-augmented adversarial indicators, achieving 6.3% accuracy improvement […]

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K2-Agent: The Cognitive Architecture That Taught AI to Think Like Humans About Mobile Tasks.

K2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control

K2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control | AI Security Research AISecurity Research Machine Learning About Agent Systems · ICLR 2026 · 18 min read K2-Agent: The Cognitive Architecture That Taught AI to Think Like Humans About Mobile Tasks A hierarchical framework separates “knowing what” from “knowing how” — enabling co-evolution of

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