Agent Systems

Agent systems covers the move from single model prompts to AI that plans, uses tools, and acts across multiple steps. Articles here examine agent architectures, memory, tool use, and the evaluation problems that make autonomous behavior hard to trust. We focus on how these systems are actually built and where current designs still fall short.

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

1 Comment / Machine Learning, Agent Systems / Adnan Saeed

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: 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