AI RESEARCH
CADMAS-CTX: Contextual Capability Calibration for Multi-Agent Delegation
arXiv CS.AI
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ArXi:2604.17950v1 Announce Type: new We revisit multi-agent delegation under a stronger and realistic assumption: an agent's capability is not fixed at the skill level, but depends on task context. A coding agent may excel at short standalone edits yet fail on long-horizon debugging; a planner may perform well on shallow tasks yet degrade on chained dependencies. Static skill-level capability profiles therefore average over heterogeneous situations and can induce systematic misdelegation. We propose CADMAS-CTX, a framework for contextual capability calibration.