Augmenting Language Models for Open-Domain Question Answering: A Survey

In this paper, the authors aim to demystify the concept of "self-improving agents" in the context of artificial intelligence. They explain that these agents are capable of learning and improving their performance over time, without the need for human intervention. The authors describe how self-improving agents can be trained using vast amounts of internet knowledge to train intelligent agents. They also outline a method for evaluating the performance of these agents by using multimodal retrieval, which involves aligning query and task keys in memory.
The authors highlight the potential benefits of self-improving agents, including their ability to learn and adapt quickly, and their capacity to perform complex tasks with minimal human intervention. However, they also acknowledge the challenges associated with training these agents, such as the need for large amounts of data and computing resources.
To train these agents, the authors use a combination of textual and visual queries, which are used to compute the alignment between the query and each trajectory in multimodal memory. They then select the memory entries with similarity higher than the confidence threshold as the candidate entries and compute the visual state embedding of the query and states in these entries. Finally, they retrieve the plan of top-k candidate entries as reference prompts.
Overall, the authors provide a comprehensive overview of self-improving agents and their potential applications in artificial intelligence. They demystify complex concepts by using everyday language and engaging metaphors or analogies, making the article accessible to a wide range of readers.