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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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| <span>arXiv</span> | |
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| <h2 class="title is-3">Abstract</h2> | |
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| 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. | |
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| The missing layer is knowledge compilation: a configurable ingestion pipeline where LLMs | |
| enrich documents with context no chunk contains alone—navigation hierarchies with | |
| claim-evidence leaves, entity graphs, timelines, mind maps, ontologies, wikis, and memory. | |
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| 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—the mechanism we measure—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%. | |
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| 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. | |
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| Other artifacts—ontology, graph, wiki, timeline, mind map, memory—pay off in | |
| deployment (governance, integration, onboarding, temporal orientation, continuity) where | |
| accuracy benchmarks cannot see them. | |
| </p> | |
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| 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. | |
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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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