mlsanityData to 14 Oct 2026

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

arXiv 2606.18947 ↗First posted 17 Jun 2026cs.AI cs.CL cs.IRcited by 1

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 1 more
Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 11 of its 39 references that are in the map. First posted on arXiv 17 Jun 2026.

Builds on

11 of 39 references.

References · 11

  1. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  2. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  4. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  5. 2023Augmented Language Models: a Surveycited by 403
  6. 2024Measuring short-form factuality in large language modelscited by 373
  7. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 17
  8. 2026Over-Searching in Search-Augmented Large Language Modelscited by 11
  9. 2025Auto-Eval Judge: Towards a General Agentic Framework for Task Completion Evaluationcited by 8
  10. 2026Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Studycited by 2
  11. 2026Bridging Protocol and Production: Design Patterns for Deploying AI Agents with Model Context Protocolcited by 2

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Citing papers per year: 2026: 1.

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  1. 2026Practical Online KV Cache Compaction for LLM Agents: An Empirical Study
  2. 2026OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
  3. 2026Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization
  4. 2026LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents
  5. 2025Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents
  6. 2026Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search
  7. 2026TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems
  8. 2025An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents