Kids' Content Is Deeply Broken. Low-quality AI-generated content currently dominates children's media. Maka, a streaming platform run by child development experts, is offering an alternative. If you have young kids or if you know somebody who does, then you know about YouTube's AI slop problem. Videos targeted at children on the platform are often filled with bizarre and hyperstimulating content that's AI generated .

Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper presents a novel framework, Multi-Agents for Knowledge Augmentation (MAKA), designed to enhance patient-trial matching by dynamically supplementing matching prompts with external, domain-specific knowledge. The MAKA architecture consists of five key components: a knowledge probing agent that detects gaps in domain knowledge, a navigation agent that manages interactions among multiple specialized knowledge augmentation agents, a knowledge augmentation agent that incorporates relevant information into patient-trial matching prompts, a supervision agent aligning the outputs from other agents with the instructions and a matching agent making the final selection decision. This approach enhances the accuracy and contextual richness of patient matching, addresses inherent knowledge gaps in both trail criteria and large language models (LLMs), and improves the alignment between patient characteristics and the criteria.