arXiv AI
Jul 22
M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples
arXiv:2606. 04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in text.
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arXiv AI
Jul 20
Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering
arXiv:2604. 04593v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically close to the query but describe clinically distinct conditions.
arXiv AI
Jun 12
ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
arXiv AI
Jul 1
One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG
arXiv:2606. 31156v1 Announce Type: cross Abstract: RAG systems retrieve documents optimized for answering one query at a time.
arXiv AI
Jun 30
ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
arXiv:2606. 29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language.
arXiv AI
Jul 21
Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG
arXiv:2607. 17742v1 Announce Type: new Abstract: Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability.
arXiv AI
Jun 8
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
arXiv:2605. 06647v2 Announce Type: replace-cross Abstract: Retrieval-augmented agents are increasingly the interface to large knowledge bases, yet most treat retrieval as a black box: they issue exploratory queries, inspect snippets, and reformulate until evidence emerges.
arXiv AI
Jun 6
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
arXiv AI
Jun 12
Uncertainty-Aware Hybrid Retrieval for Long-Document RAG
arXiv:2606. 13550v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence.
Hugging Face Trending Papers
Jun 29
ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol language. In low-resource subdomains, generator fine-tuning can over-specialize and degrade general capability, making query-side retriever adaptation an attractive alternative.
arXiv AI
Jul 29
Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.
arXiv AI
Jun 30
Schema-First Retrieval: Embedding Catalogs for Natural Language Analytics
arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.