I found a way to reduce context redundancy 30-60% (www.triage-sec.com)

Delta, an innovative open-source tool for Lossless Token Sequence Compression (LTSC), has been introduced to tackle the issue of context redundancy in large language model (LLM) inference. By identifying and replacing repeated multi-token subsequences with compact meta-token references, Delta achieves an impressive compression rate of 30-60% on structured inputs while ensuring perfect reconstruction of original sequences. This advancement holds significant implications for the AI/ML community, particularly as LLMs increasingly utilize context augmentation strategies that can inflate input costs, with some models potentially incurring monthly expenses exceeding $75,000 due to repeated subsequences. Technically, Delta’s architecture comprises several stages, including pattern discovery using suffix arrays and various iterative methods, candidate filtering, and hierarchical compression to maximize efficiency. Its design accommodates multiple discovery strategies tailored to specific input types, from code to near-duplicates, allowing for greater adaptability. Additionally, Delta incorporates advanced features such as quality prediction and region-aware compression, ensuring that critical information remains intact during the compression process. By integrating seamlessly with existing AI tools via a Model Context Protocol (MCP) server, Delta not only optimizes computational resources but also presents a substantial economic benefit, setting a new standard for LLM deployment efficiency.