Daily edition · Information Retrieval
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27 articles · 4 sources · 27 papers ·
Top topics: Information Retrieval · AI Research · AI
arXiv:2609.10758v1 Announce Type: cross Abstract: Multilingual large language models (LLMs) are increasingly used for open-ended text generation, yet their behaviour in low-resource languages remains poorly understood.…
arXiv:2609.11052v1 Announce Type: new Abstract: Industrial recommender systems use cascaded stages with different objectives, feature spaces, and latency constraints. Optimizing pre-ranking and ranking separately can…
arXiv:2609.10856v1 Announce Type: cross Abstract: Large language models are becoming the first point of contact for consumer search in domains where the stakes are material and the law is explicit. Existing audits show…
arXiv:2609.11115v1 Announce Type: cross Abstract: Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code,…
arXiv:2609.11632v1 Announce Type: new Abstract: Federated recommendation enables collaborative model training while keeping user interaction data on local clients. A central problem in federated recommendation is how to…
arXiv:2609.11460v1 Announce Type: cross Abstract: Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal…
arXiv:2609.11646v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) pipelines often provide no reliable inference-time signal of whether retrieval succeeded; on ambiguous or out-of-scope…
arXiv:2609.11808v1 Announce Type: new Abstract: Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. Existing compression methods retain a subset or…
arXiv:2601.18570v2 Announce Type: replace Abstract: Federated recommendation systems commonly protect user privacy by keeping user parameters on local devices, while exchanging item parameters for collaborative model…
arXiv:2609.11190v1 Announce Type: cross Abstract: AI shopping assistants increasingly redirect consumer discovery, creating an urgent need for tools that support seller-side competitive decision-making. We present a…
arXiv:2609.10750v1 Announce Type: new Abstract: LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present a production skill…
arXiv:2603.24204v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated superior performance in listwise passage reranking task. However, directly applying them to rank long-form documents…
arXiv:2608.27912v2 Announce Type: replace Abstract: Deep-research agents answer complex user questions through an iterative sequence of search steps, where the agent autonomously formulates sub-queries to retrieve the…
arXiv:2609.11209v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved documents, but longer…
arXiv:2609.11758v1 Announce Type: cross Abstract: Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has…
arXiv:2609.11390v1 Announce Type: new Abstract: State-of-the-art retrieval-augmented generation (RAG) methods exploit document structures to acquire sufficient evidence, but often incur substantial token costs. To…
arXiv:2604.04036v2 Announce Type: replace Abstract: Novice math teachers often encounter students' mistakes that are difficult to diagnose and remediate. Misconceptions are especially challenging because teachers must…
arXiv:2609.10862v1 Announce Type: cross Abstract: This report presents results from Project Qualia, an ongoing effort to determine whether experiential similarity between songs, a structure not captured by genre or…
arXiv:2609.11572v1 Announce Type: new Abstract: Although large language models (LLMs) and retrieval-augmented generation (RAG) have advanced open-domain question answering (QA), they remain unreliable when documents…
arXiv:2608.29652v3 Announce Type: replace Abstract: Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline.…
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