代理编码老烧token，Blast Radius在七个OpenAI模型上省17-26%，还能完整恢复，溢出率最低，很实用。
Blast Radius提出预测性内存管理层，估算输入提示在上下文和代码通道中的影响范围。通过NECROPHORESIS将死上下文逐字存档实现可逆驱逐，RDM识别并掩埋重复出现的转录。在七个OpenAI模型上，token消耗减少17-26%，溢出率低于所有对比策略，且保持字节级可逆。在450份被掩埋内容中，378份为重复死内容，零份被召回。该方法在HCRC之下运行，决定哪些记录被掩埋及提示可触及的代码范围。
Blast Radius
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.