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Evidence record 6159 · automatically gathered

Learning Without Losing Identity: Capability Evolution for Embodied Agents

Embodied agents are expected to operate persistently in dynamic physical environments, continuously acquiring new capabilities over time. Existing approaches to improving agent performance often rely on modifying the agent itself -- through prompt engineering, policy updates, or structural redesign -- leading to instability and loss of identity in long-lived systems. In this work, we propose a capability-centric evolution paradigm for embodied agents. We argue that a robot should maintain a pers

Record details

Published: 9 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (9 April 2026), “Learning Without Losing Identity: Capability Evolution for Embodied Agents,” evidence record 6159, https://ethics.ai/record/6159 (originally published by arXiv).

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