Daily edition · Information Retrieval
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34 articles · 4 sources · 34 papers ·
Top topics: AI Research · AI · Information Retrieval
arXiv:2609.19153v1 Announce Type: cross Abstract: Empirical legal scholarship increasingly treats judicial text as data, and much of it still runs on sparse, interpretable pipelines -- TF-IDF features and linear…
arXiv:2606.23915v2 Announce Type: replace-cross Abstract: Practice often treats automatic metrics for attribution in LLM retrieval-augmented generation as interchangeable. We audit eight automatic scorers -- lexical,…
arXiv:2609.19844v1 Announce Type: cross Abstract: AI-generated RTL verification plans can satisfy a provider schema yet fail at the boundary to trusted execution. We present SecTB-RTL, an auditable framework covering 31…
arXiv:2609.20050v1 Announce Type: new Abstract: A coding agent halfway through an issue has already read much of what a retriever ranks highest. Relevance is scored per passage, but sufficiency belongs to the set: a…
arXiv:2609.16814v3 Announce Type: replace-cross Abstract: While Large Language Model (LLM)-based Natural Language Inference (NLI) systems achieve high accuracy, their decision-making processes lack auditable structures.…
arXiv:2609.19158v1 Announce Type: cross Abstract: Knowledge graphs are usually integrated into question answering by encoding a retrieved subgraph with a graph neural network and fusing it with the language model in the…
arXiv:2609.19456v1 Announce Type: cross Abstract: Retrieval-augmented systems increasingly rely on vector indexes that may retain deleted items in their search graph. Existing deletion interfaces can prevent deleted…
arXiv:2609.19942v1 Announce Type: cross Abstract: In extractive document question answering whose questions were generated from the passages that contain their answers -- so that retrieval recovers 92-99.8% of what any…
arXiv:2609.19622v1 Announce Type: new Abstract: Multi-hop retrieval-augmented generation (RAG) requires evidence that remains relevant to a query while introducing enough novelty to bridge semantic gaps. Dense retrieval…
arXiv:2609.20175v1 Announce Type: new Abstract: The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow range of information…
arXiv:2509.01184v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction serves as a cornerstone of recommender systems. Despite the strong performance of current CTR models based on user behavior…
arXiv:2609.19482v1 Announce Type: new Abstract: Algebraic Retrieval lets AI agents compose search strategies at query time. Relevance criteria, eligibility constraints, and ranking preferences can be expressed together…
arXiv:2609.19601v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is increasingly used to ground large language model (LLM) outputs in scientific literature. However, in open-ended literature…
arXiv:2606.09046v2 Announce Type: replace-cross Abstract: Knowing how often a language model fails does not explain where its errors concentrate. When auditors examine many explanations, the strongest observed pattern…
arXiv:2609.19244v1 Announce Type: cross Abstract: Conversational LLM agents increasingly rely on Web search, yet the end-to-end lifecycle of agentic search remains poorly understood. We present the first study of Web…
arXiv:2609.20303v1 Announce Type: cross Abstract: Classical philosophical corpora pose three compounding challenges for language resources: they exist in several languages without parallel alignment, their vocabulary is…
arXiv:2609.06027v2 Announce Type: replace-cross Abstract: Search-augmented LLM agents are increasingly used for consumer decisions, making them vulnerable to Generative Engine Optimization (GEO) poisoning. Existing…
arXiv:2506.17277v2 Announce Type: replace Abstract: The retrieval stage of retrieval-augmented generation (RAG) for scientific question answering depends on how documents are segmented and how chunks are represented in…
arXiv:2609.19585v1 Announce Type: cross Abstract: In mental health care, reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of biological,…
arXiv:2609.20563v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong potential for producing context-rich text embeddings for retrieval. Most existing methods either treat embedding…
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