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  <title>Context as Infrastructure: Knowledge Compilation for Retrieval in Agentic Systems with Harnesses</title>

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          <h1 class="title is-2 publication-title">Context as Infrastructure: Knowledge
            Compilation for Retrieval in Agentic Systems with Harnesses</h1>

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            <span class="author-block"><sup>1</sup>InfiniFlow</span>
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                <a href="https://arxiv.org/abs/2610.41172" target="_blank"
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                  <span>arXiv</span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Retrieval-augmented generation is becoming agentic. A search harness writes queries,
            reads results, reformulates, and decides when to stop. This largely solves accuracy: a
            harness with only BM25/grep and MiniMax M3 answers 85% of BrowseComp-Plus and 91% on
            multi-hop FRAMES in our runs, without compiled or vector indexes. It exposes a context
            problem: an undisciplined loop repeats queries, rereads documents, and fills the window
            with unused passages.
          </p>
          <p>
            The missing layer is knowledge compilation: a configurable ingestion pipeline where LLMs
            enrich documents with context no chunk contains alone&mdash;navigation hierarchies with
            claim-evidence leaves, entity graphs, timelines, mind maps, ontologies, wikis, and memory.
          </p>
          <p>
            Compilation is not a better retriever: under a competent harness, structural artifacts
            gave no detectable accuracy gain on FRAMES, and a PageIndex-style outline degraded at
            scale. It is a supply layer. A navigation tree whose leaves are claim-evidence
            pairs&mdash;the mechanism we measure&mdash;cuts query tokens by 91% when evidence
            suffices and adds 1.9% when it does not, a 38% average reduction; this implies a 2%
            break-even sufficiency rate and a measured rate near 43%.
          </p>
          <p>
            A decision gate cuts latency from 140 s to 30 s, though substitution's token saving is
            bought with retrieval rounds and carries its own latency cost.
          </p>
          <p>
            Other artifacts&mdash;ontology, graph, wiki, timeline, mind map, memory&mdash;pay off in
            deployment (governance, integration, onboarding, temporal orientation, continuity) where
            accuracy benchmarks cannot see them.
          </p>
          <p>
            We describe RAGFlow's pipeline and harness (open source at
            <a href="https://github.com/infiniflow/ragflow" target="_blank">github.com/infiniflow/ragflow</a>),
            which turns the loop into five disciplined stations and keeps the context entering the
            model the smallest sufficient set; together they form an agent-native database: a store
            that hands the agent compiled, governed context instead of a ranked list of passages.
          </p>
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<section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@misc{ragflow2026context,
  author        = {{InfiniFlow}},
  title         = {Context as Infrastructure: Knowledge Compilation for Retrieval
                   in Agentic Systems with Harnesses},
  year          = {2026},
  eprint        = {2610.41172},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2610.41172}
}</code></pre>
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