Blast Radius
Blast Radius predicts which code and context a prompt will reach, then reversibly archives unused history. Across seven OpenAI models, it cut token consumption by 17–26%.
Blast Radius manages coding-agent memory by estimating a prompt’s reach across context and code. Across **seven OpenAI models**, it reduced token use by **17–26%** while keeping archived context byte-exact and reversible.
For long-running agents, consider eviction policies that archive rather than summarize context, and track repeated transcripts separately. The paper classified **378 of 450** evicted records as recurring dead matter.
Blast Radius manages coding-agent memory by estimating a prompt’s reach across context and code. Across **seven OpenAI models**, it reduced token use by **17–26%** while keeping archived context byte-exact and reversible. For long-running agents, consider eviction policies that archive rather than summarize context, and track repeated transcripts separately. The paper classified **378 of 450** evicted records as recurring dead matter. The evidence comes from the authors’ tested policies and models. **Zero archived records were recalled**, but that observation does not establish how eviction behaves across other repositories, workflows, or longer horizons.
This narrows long-context memory management to a reversible eviction problem: repeated prompt debris can be removed without lossy summarization or deleting its source record. It complements durable shared-memory proposals but does not show that archived material is useful later—the absence of recalls leaves retrieval policy, provenance, and long-horizon behavior unresolved.