arXiv Machine Learning
Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents
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arXiv AI
Jul 23
Do Data Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
arXiv AI
Sep 1
Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs
The paper evaluates how modern large language models use internal web search to answer factual questions. Using 783 static queries and 288 dynamic queries, the authors find that enabling retrieval improves accuracy on static questions but hurts confidence calibration. On dynamic queries, models often retrieve but still achieve less than 70% accuracy, mainly due to poor query formulation and source selection, indicating that internal web search works better as a quick verification tool than a full information‑retrieval system.
arXiv Machine Learning
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Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States
arXiv:2609.23371v1 Announce Type: cross Abstract: Long-context language models interface with external knowledge through raw natural language. In retrieval-augmented systems, this creates a persisten...
arXiv AI
Jul 21
Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
arXiv Machine Learning
Sep 3
Multi-Agent Retrieval-Augmented Generation for Efficient Cloud Knowledge Base Search in Telecom SNOC Environment
The paper introduces Athena for Cloud Knowledge Base, a fully offline multi‑agent Retrieval Augmented Generation (RAG) system tailored for Vodafone Idea’s Telecom SNOC environment. It combines dense E5 Large V2 embeddings, BM25 sparse retrieval, and Knowledge Graph expansion, fusing results with Weighted CombSUM, cross‑encoder reranking, and MMR to produce a diverse evidence set. The framework further verifies each chunk with an LLM before response generation, discarding weak evidence and falling back to multi‑chunk evaluation when necessary, achieving an MRR@10 of 0.910 and an EM of 78.4% on a 4,200‑document corpus.
arXiv Computation and Language
Aug 27
Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG
Corpus2Skill is a retrieval architecture that transforms an enterprise knowledge base into a hierarchical skill directory, enabling an LLM agent to navigate from high-level summaries to specific documents and backtrack when necessary. On an enterprise customer‑support benchmark, it outperforms single‑shot dense, hybrid, hierarchical‑retrieval, and agentic RAG baselines in answer quality and grounding, with a moderate cost tradeoff. An eleven‑dataset study shows that corpus navigation excels on single‑domain corpora with a recoverable topical taxonomy but is less effective on open‑domain factoid pools or homogeneous‑tabular corpora, providing a design guideline for knowledge‑grounded systems.