Daily edition · Information Retrieval
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54 articles · 4 sources · 54 papers ·
Top topics: Information Retrieval · AI · AI Research
arXiv:2510.23224v2 Announce Type: replace-cross Abstract: The rapid digitization of histopathology slides has opened new opportunities for computational tools in clinical and research workflows. Content-based slide…
arXiv:2607.19020v4 Announce Type: replace-cross Abstract: Clinical prediction models deployed in intensive care units may require model updating when data distributions shift, yet unconstrained adaptation can alter…
arXiv:2605.31291v2 Announce Type: replace Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operational constraints…
arXiv:2606.23843v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rather than…
arXiv:2609.14978v1 Announce Type: new Abstract: Transformers have shown promising performance in LLMs due to their outstanding scalability, several studies have investigated the scalability of Transformers for…
arXiv:2606.02737v2 Announce Type: replace Abstract: Dense retrieval compresses a passage into a single vector, but this compression is positionally skewed: early content dominates the embedding, and retrieval degrades…
arXiv:2609.14572v1 Announce Type: cross Abstract: Teaching abstract theoretical computer science (TCS) concepts such as algorithm analysis and complexity theory is challenging because students must handle formal proofs…
arXiv:2609.13205v1 Announce Type: new Abstract: Sparse long-context inference requires efficient token retrieval in both prefill and decode. Existing methods often use different retrieval strategies for the two stages,…
arXiv:2609.13489v1 Announce Type: new Abstract: RAG systems commonly retrieve a fixed number of documents (top-k) to ground generation, but this static approach is brittle: simple queries suffer over-retrieval (adding…
arXiv:2609.15830v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded,…
arXiv:2606.18181v3 Announce Type: replace Abstract: Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas that lack applicable laws.…
arXiv:2509.00303v4 Announce Type: replace-cross Abstract: In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations.…
arXiv:2601.05549v2 Announce Type: replace Abstract: Retrievers are a key bottleneck in temporal Retrieval-Augmented Generation (RAG) systems: failing to retrieve temporally relevant context can degrade downstream…
arXiv:2609.13647v1 Announce Type: cross Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource monitoring, traffic…
arXiv:2609.14579v1 Announce Type: new Abstract: RAG systems are routinely evaluated using synthetic question sets generated from the target document corpus. While this practice provides a useful check on overall…
arXiv:2609.14016v1 Announce Type: new Abstract: TF-IDF and BM25 are two of the most widely used methods for scoring query-document relevance, yet neither has a standard probabilistic derivation that justifies it as a…
arXiv:2609.13678v1 Announce Type: new Abstract: Generative recommendation reformulates sequential recommendation as autoregressive generation by encoding items into semantic tokens, enabling improved scaling capability…
arXiv:2609.15094v1 Announce Type: new Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user population, particularly…
arXiv:2406.11720v2 Announce Type: replace Abstract: Neural re-rankers typically score query-document pairs independently, neglecting cross-document context within the retrieved candidate set. We propose Graph Neural…
arXiv:2609.13486v1 Announce Type: new Abstract: Recent work has shown that fine-tuning decoder-only large language models (LLMs) for retrieval yields strong first-stage retrievers, with effectiveness improving as…
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