Imagine trying to read a massive book with only a tiny scrap of paper to write notes on. That’s essentially the challenge Large Language Models (LLMs) face when processing long texts. Their memory, like that scrap of paper, is limited. But what if there was a way to continually distill the most important information, keeping only the essence of the text as you read? That's the core idea behind InfiniPot, a groundbreaking new framework that allows LLMs to handle practically infinite context, even on devices with limited memory. Traditional LLMs struggle with long texts because they need to store information about every word, quickly filling up their memory. InfiniPot tackles this by using a clever 'consume-and-compress' cycle. It works like a virtual pot: as new text comes in, InfiniPot evaluates the importance of each piece of information. When the pot is full, it distills the existing contents, retaining only the crucial bits and making room for more. This continuous distillation allows the LLM to keep processing text without being constrained by memory limitations. The magic of InfiniPot lies in its unique distillation process. It uses two key innovations: the Catalyst Prompt (CaP) and the Novelty under Compression (NuC) score. CaP acts like a flash of insight, providing hints to the LLM about what might be important later on. Imagine quickly glancing at the ending of a chapter before reading it – CaP provides that future context. NuC, on the other hand, focuses on identifying new and unique information. It prioritizes retaining novel elements, ensuring the LLM doesn’t lose track of crucial details amidst repetitive content. Together, CaP and NuC ensure that only the most representative and novel information is kept. Tests show that InfiniPot-equipped LLMs outperform models trained for long contexts, even on memory-constrained devices. They excel in tasks like question answering and summarization, even with enormous texts. InfiniPot not only extends the capabilities of LLMs, but also opens exciting new possibilities for AI on mobile devices and other resource-limited environments. It's a significant step towards more versatile and powerful language processing, making LLMs more practical for real-world applications.