arXiv:2611.06603v1 Announce Type: new
Abstract: Continual agents must handle urgent interaction while retaining alternatives, replaying errors, and committing safe long-term changes. A previous digital-life framework distinguishes online (busy) and offline (idle) channels, but leaves open whether these are separate programs or phases of a common process and how their operations should be ordered. Motivated by recent observations of rapidly alternating perceptual representations in primate cortex, we develop a temporal organization layer with phase-tagged micro-epochs, an observable duty cycle, independent content and resource controls, and three nested clocks separating candidate exploration from durable commitment. We give a projected duty-cycle update, state its deterministic descent guarantee and the additional noise term required for a stochastic guarantee, and specify coupled busy/idle execution contracts. Five synthetic experiments (64 seeds each) examine descent and noise, objective misalignment, gain asymmetry, resource coupling, and dropped replay logs. In the gain/noise model the best tested gain ratio is 1.55; decoupled scheduling yields 26.6% versus 11.1% success on a constructed urgent/high-load subset. A frozen-backbone Qwen3-8B prototype further checks orchestration, but its proxy outcomes are not task-benchmark evidence. The results establish a testable scheduling design and identify its conditions of validity, not machine consciousness or general-purpose agent superiority.
arxiv
Time-Division Multiplexing Layer for Continual Agents
The paper proposes a temporal organization layer for continual agents using phase-tagged micro-epochs and nested clocks to manage urgent interactions and long-term commitments. It provides a projected duty-cycle update with deterministic and stochastic guarantees, and specifies coupled busy/idle execution contracts. Synthetic experiments test descent, noise, misalignment, gain asymmetry, resource coupling, and dropped replay logs, with a frozen-backbone Qwen3-8B prototype for orchestration checks.
Offers a concrete, testable scheduling design for continual agents, relevant to AI systems that must balance real-time and background tasks.
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