// Context as Infrastructure for Agents //

The RAGFlow team at InfiniFlow ran a harness with only BM25 and grep on MiniMax M3. It answered 85% of BrowseComp-Plus and 91% of FRAMES with no vector or compiled index.

They then tested summary trees, PageIndex-style outlines, wikis, and graphs on top. None improved FRAMES accuracy, and the outline got worse at scale.

The structures help with context cost. A navigation tree whose leaves are claim and evidence pairs cut query tokens by 91% when the evidence was enough, and added 1.9% when it was not. That averages to a 38% reduction.

The savings cost extra retrieval rounds. On one paired subset, tokens halved while latency roughly tripled. A decision gate brought latency from 140 seconds to 30.

If you are building indexes for agentic RAG, measure them on tokens and latency, since accuracy may already be saturated.

https://arxiv.org/abs/2610.41172

Chat with Paper: https://academy.dair.ai/papers/context-as-infrastructure-knowledge-compilation-for-retrieval-in-agentic-systems-2610.41172