arXiv — NLP / Computation & Language · · 3 min read

Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents

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Computer Science > Artificial Intelligence

arXiv:2606.18947 (cs)
[Submitted on 17 Jun 2026]

Title:Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents

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Abstract:Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injection, cost, latency, and generation behavior behind a single model-provider boundary. This coupling makes grounding hard to inspect, tune, reuse, or port, and can trigger Search-Induced Verbosity that breaks strict output contracts. We present Decoupled Search Grounding (DSG), a vendor-agnostic boundary that moves grounding outside the reasoning model through an MCP-compatible gateway, exposing provider routing, source-aware context rendering, configured fallback, retrieval-depth control, and exact plus semantic caching as first-class controls. Across five frontier models on SimpleQA, FreshQA, and HotpotQA, native search leads on recency-sensitive FreshQA, but DSG exposes a stronger frontier when control matters: on SimpleQA it nearly matches native accuracy (86.1% vs. 87.7%) at 91% lower search cost, preserves concise answer contracts, and reaches a 99.4% warm-cache hit rate with 68% lower latency. Deployed as a shared production grounding layer for large-scale agentic workloads with interchangeable models, DSG matches or slightly exceeds native-search accuracy on an e-commerce query-understanding (QIU) workload while cutting search cost by over 98%. Real-time grounding is best treated as an optimizable interface boundary, not a fixed model feature.
Comments: 15 pages, Figure 8
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.18947 [cs.AI]
  (or arXiv:2606.18947v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2606.18947
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Emmanuel Aboah Boateng [view email]
[v1] Wed, 17 Jun 2026 11:30:39 UTC (2,155 KB)
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