mlsanityData to 14 Oct 2026

Context-Aware Search and Retrieval Over Erasure Channels

arXiv 2507.11894 ↗First posted 16 Jul 2025cs.IR cs.IT math.ITcited by 0

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

This paper introduces and analyzes a search and retrieval model that adopts key semantic communication principles from retrieval-augmented generation. We specifically present an information-theoretic analysis of a remote document retrieval system operating over a symbol erasure channel. The proposed model encodes the feature vector of a query, derived from term-frequency weights of a language corpus by using a repetition code with an adaptive rate dependent on the contextual importance of the terms. At the decoder, we select between two documents based on the contextual closeness of the recovered query. By leveraging a jointly Gaussian approximation for both the true and reconstructed similarity scores, we derive an explicit expression for the retrieval error probability, i.e., the probability under which the less similar document is selected. Numerical simulations on synthetic and real-world data (Google NQ) confirm the validity of the analysis. They further demonstrate that assigning greater redundancy to critical features effectively reduces the error rate, highlighting the effectiveness of semantic-aware feature encoding in error-prone communication settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 27 references that are in the map. First posted on arXiv 16 Jul 2025.

Builds on

12 of 27 references.

References · 12

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  3. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  4. 2022Semantic Communications for Future Internet: Fundamentals, Applications, and Challengescited by 95
  5. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generationcited by 90
  6. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Basescited by 77
  7. 2022Less Data, More Knowledge: Building Next Generation Semantic Communication Networkscited by 62
  8. 2020From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problemscited by 52
  9. 2021What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligencecited by 50
  10. 2024Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generationcited by 16
  11. 2023Semantic Importance-Aware Communications Using Pre-trained Language Modelscited by 15
  12. 2023Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language modelcited by 15

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  1. 2026Context-Aware Search and Retrieval Under Token Erasure ↗
  2. 2025SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
  3. 2024RARe: Retrieval Augmented Retrieval with In-Context Examples
  4. 2026Towards Semi-Automatically Comparing Keyword-Based and Semantic Search Accuracy ↗
  5. 2024Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
  6. 2024Assessing "Implicit" Retrieval Robustness of Large Language Models
  7. 2024Retrieval is Accurate Generation
  8. 2025AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation