Learning to Learn from Multimodal Experience
The paper discusses a new approach to experience-driven learning in artificial intelligence. It emphasizes the need for adaptive memory design in multimodal environments, as traditional methods are often limited to textual settings. The proposed framework allows agents to dynamically structure and utilize memory based on task requirements and interaction history, improving performance across various tasks.
- ▪Experience-driven learning enables agents to improve from interaction trajectories by reusing past experiences.
- ▪Existing methods primarily focus on textual data and rely on fixed memory schemas, which are inadequate for multimodal scenarios.
- ▪The proposed framework shifts memory design to an adaptive process, allowing for better performance in multimodal tasks.
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Computer Science > Artificial Intelligence arXiv:2605.16857 (cs) [Submitted on 16 May 2026] Title:Learning to Learn from Multimodal Experience Authors:Xingyu Sui, Weixiang Zhao, Yongxin Tang, Yanyan Zhao, Yang Wu, Dandan Tu, Bing Qin View a PDF of the paper titled Learning to Learn from Multimodal Experience, by Xingyu Sui and 6 other authors View PDF HTML (experimental) Abstract:Experience-driven learning has emerged as a promising paradigm for enabling agents to improve from interaction trajectories by accumulating and reusing past experience. However, existing approaches are predominantly developed in textual settings and rely on manually designed memory schemas, limiting their applicability to multimodal environments.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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