Adaptive Self-Evolution via Real-Time Multimodal Feedback and World Sensing

Research Question: Does the integration of real-time, multi-modal environmental feedback (e.g., vision, audio, proprioception) accelerate and enrich the self-evolution capabilities of AgentEvolver-style agents?

Hypothesis: Agents that learn from multi-modal data streams will demonstrate superior adaptation to novel, dynamic environments, with improved problem-solving and robustness compared to text-only AgentEvolver agents.

Experiment Plan: - Extend the AgentEvolver framework to process and learn from multi-modal sensory data (images, audio, environmental sensors).

• Implement self-questioning, self-navigating, and self-attributing mechanisms that incorporate non-textual feedback.
• Evaluate agent performance and adaptability in simulated environments with complex, multi-modal cues (e.g., robots navigating real-world spaces, virtual agents in multimedia-rich games).
• Compare learning curves, sample efficiency, and generalization to the original AgentEvolver.

References:

• Xi, Z., Ding, Y., Chen, W., Hong, B., Guo, H., Wang, J., Yang, D., Liao, C., Guo, X., He, W., Gao, S., Chen, L., Zheng, R., Zou, Y., Gui, T., Zhang, Q., Qiu, X., Huang, X., Wu, Z., & Jiang, Y.-G. (2024). AgentGym: Evolving Large Language Model-based Agents across Diverse Environments. arXiv.org.
• Zhai, Y. et al. (2025). AgentEvolver: Towards Efficient Self-Evolving Agent System.