EAISE: A Simulation Environment for Self-Evolving Embodied AI with Mirror Testing and Multi-Agent Diagnostics

This paper introduces the Embodied AI Simulation Environment (EAISE), a proposed conceptual software framework designed to support the development and interpretability of advanced artificial intelligence systems. In contrast to methods of 'direct' self-recognition, which rely on pre-programmed knowledge or external large language model (LLM) interpretation for identifying an agent's self-image, EAISE specifically focuses on fostering 'emergent' self-awareness. EAISE offers a high-fidelity 3D simulation environment in which AI agents are embodied in virtual forms—such as robotic quadrupeds or humanoids—and exposed to interactive, structured tasks. A key focus lies in evaluating adaptive behavior and the emergence of internally coherent self-models through continuous sensorimotor feedback and internal pseudo-affective states. Notably, EAISE supports simultaneous multi-agent control and offers flexible observation tools, including internal state logging and multiple camera perspectives. Through simulation of agent interaction and controlled perceptual feedback (including complex reflection-based tasks), the framework seeks to offer a practical testbed for future AI architectures with complex internal monitoring and behavior adaptation capabilities, ultimately enhancing the understanding of how 'alien' intelligences develop their self-concept intrinsically.