Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
cs.AI, cs.CL, cs.IR, cs.MA
Submitted: 2026-06-17
Updated: 2026-10-11
Comments: Accepted at the EMNLP 2026; 16 pages, 9 figure
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
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 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.
Sources
- Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluation
- Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
- Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions
- Augmented Language Models: a Survey
- WebGPT: Browser-assisted question-answering with human feedback
- Bridging Protocol and Production: Design Patterns for Deploying AI Agents with Model Context Protocol
- Measuring short-form factuality in large language models
- Over-Searching in Search-Augmented Large Language Models
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