AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended and non-stationary environments. To address these limitations, we present AutoAgent, a self-evolving multi-agent framework built on three tightly coupled components: evolving cognition, on-the-fly contextu
Record details
Published: 10 March 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (10 March 2026), “AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents,” evidence record 7420, https://ethics.ai/record/7420 (originally published by arXiv).
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