Optimizing Agentic Context via Blast Radius

Summary & Methodology Analysis

Blast Radius optimizes agentic coding environments by addressing the inefficiency of processing dead context. The method employs a two-channel approach: a context channel that archives concluded tasks into a midden as token-efficient skeletons, and a code channel that monitors dependency graph reachability to provide a commit-pressure signal. This architecture utilizes a HCRC gate to authorize state transitions, while Recurring Dead Matter identification uses Laplace's rule of succession to detect and compress repetitive transcripts like build logs. These components work together to ensure only active context occupies expensive token space. Necrophoresis provides a safety mechanism for this process, allowing for reversible eviction where archived bodies can be restored byte-exact upon demand at a bounded cost. This architecture was evaluated across seven OpenAI models, including gpt-4.1, gpt-5.1, gpt-5.4, gpt-5.5, gpt-5.6-luna, gpt-5.6-sol, and gpt-5.6-terra, resulting in a consistent reduction of token usage between 17 and 26 percent. Despite these gains, the current implementation relies on the assumption that buried candidates have zero resurrection probability. This is a simplification of the underlying theory, and the work remains an ongoing development toward the broader goal of Algosophy. The paper does not specify the precise hardware requirements or the specific latency impact for the exhumation process beyond stating it is performed at a bounded cost.