🤖 AI Summary
This work addresses the high computational cost and inefficient token consumption in agent programming caused by poor context management. The authors propose a predictive memory management layer that models contexts in a Polish context space, coupling contextual and code channels to assess prompt reachability and incorporating a reversible context eviction mechanism. They introduce an innovative archiving strategy termed NECROPHORESIS, enabling byte-level reversible storage of dead contexts, and integrate a Recurring Dead Matter (RDM) algorithm to identify and bury redundant, useless records. Experimental results across seven OpenAI models demonstrate a 17–26% reduction in token consumption—the lowest overflow rate among compared methods—and reveal that 378 of 450 buried records were duplicate dead content, none of which required recall, thereby validating both the efficacy and reversibility of the proposed approach.
📝 Abstract
Agentic coding faces growing problems of affordability and wasted tokens. We introduce Blast Radius, a predictive memory management layer that estimates an incoming prompt's reach through coupled context and code channels. NECROPHORESIS enables reversible eviction by archiving dead context verbatim, while Recurring Dead Matter (RDM) identifies and buries repeatedly occurring transcripts. We formulate reversible context eviction over a Polish context space, providing a measurable foundation for retention, recurrence, and eviction while connecting context entropy to resurrection probability. Across seven OpenAI models, Blast Radius reduced token consumption by 17-26%, achieved the lowest overflow rate among tested policies, and remained byte exact reversible. Of 450 buried bodies, 378 were recurring dead matter and zero were recalled. Blast Radius operates beneath HCRC, determining which records to bury and how far an incoming prompt may reach into the codebase. This work contributes to the broader goal of Algosophy: making large language models and agentic coding more reusable and sustainable.