Imagine an AI that can effortlessly sift through an endless stream of information, extracting key insights and organizing them neatly. This isn't science fiction, it's the promise of Online Long-context Processing (OLP), a groundbreaking approach to handling massive amounts of text data. Traditional AI models often struggle with extremely long documents, but OLP tackles this challenge head-on. Think of it as a highly efficient team of specialized AI agents, each playing a specific role in understanding and structuring the content. One agent identifies key topics, another pinpoints their location, a third checks relationships between topics, and a fourth organizes everything coherently. This division of labor allows OLP to process information far more efficiently than traditional methods. But what if we could dynamically assign the best AI model for each task? That's where Role Reinforcement Learning (Role-RL) comes in. This innovative framework acts like a smart manager, constantly evaluating the performance of different AI models and assigning them to the roles where they excel. It considers factors like accuracy, cost, and response time to ensure optimal performance. The result? A system that's not only incredibly effective at processing long-form content but also highly cost-efficient. In tests on a dataset of live e-commerce transcripts, OLP with Role-RL achieved a remarkable 93.2% recall rate while saving a whopping 79.4% on AI model costs. This approach has far-reaching implications for various applications, from automated news reporting and live e-commerce analysis to deciphering complex research papers. While challenges remain, OLP and Role-RL represent a significant leap forward in our quest to unlock the full potential of AI for understanding and organizing information.