Daily edition · Information Retrieval
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66 articles · 4 sources · 66 papers ·
Top topics: AI Research · AI · Information Retrieval
arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience…
arXiv:2609.23766v1 Announce Type: cross Abstract: Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send…
arXiv:2509.11353v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in information systems, including being used as second-stage rerankers in information retrieval pipelines, yet…
arXiv:2505.13994v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems empower large language models (LLMs) with external knowledge, yet struggle with efficiency-accuracy trade-offs when…
arXiv:2609.24430v1 Announce Type: new Abstract: Generative recommendation has emerged as an active research direction, where items are commonly represented by semantic IDs (SIDs): discrete codes generated token by…
arXiv:2609.23849v1 Announce Type: new Abstract: Sequential recommendation must operate under long-tailed item distributions and popularity-driven concentration, often forcing practitioners to trade short-list accuracy…
arXiv:2609.22227v1 Announce Type: new Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the tokenizer is trained…
arXiv:2609.23053v1 Announce Type: cross Abstract: A RAG system can hand you the right answer and cite a source it did not actually use. Models output these unfaithful citations via post-rationalization: they write the…
arXiv:2609.24101v1 Announce Type: cross Abstract: Automated biomedical evidence synthesis depends on retrieving published studies, but the biomedical literature is systematically skewed toward positive findings. Deeper…
arXiv:2609.22706v1 Announce Type: cross Abstract: Identity association in multi-object tracking (MOT) is vulnerable to partial occlusion, truncated detections, and fluctuating confidence scores. Existing motion-dominant…
arXiv:2609.23718v1 Announce Type: new Abstract: Industrial mobile feed systems rely on a retrieval-ranking pipeline to serve large-scale, heterogeneous, and fast-changing content under strict latency constraints.…
arXiv:2609.24152v1 Announce Type: new Abstract: Visual search on large e-commerce catalogs must serve both "similarity" queries that ask for items resembling an uploaded image and "modifier" queries that comprise an…
arXiv:2609.18148v2 Announce Type: replace-cross Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,…
arXiv:2609.22100v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce distracting…
arXiv:2606.28358v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using…
arXiv:2609.23115v1 Announce Type: new Abstract: Disruptive innovation in the EU is not sufficiently competitive; this weakness puts at risk the social benefits that its citizens take for granted. This report argues…
arXiv:2609.23111v1 Announce Type: new Abstract: Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training larger…
arXiv:2609.22529v1 Announce Type: cross Abstract: International law provides the normative framework through which states coordinate action, regulate armed conflict, and protect human rights, yet its texts remain…
arXiv:2609.23307v1 Announce Type: new Abstract: This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incoming job descriptions are…
arXiv:2511.04473v3 Announce Type: replace-cross Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions…
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