Topic - Edition
arXiv:2606.10907v2 Announce Type: replace-cross Abstract: When a conversational assistant recommends a brand to a user with no recent observed engagement, that user's same-name Google search rises $+4.3$ percentage…
arXiv:2608.29899v2 Announce Type: replace-cross Abstract: Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K…
arXiv:2609.00604v1 Announce Type: cross Abstract: The growing complexity of cyber-physical attack surfaces in advanced manufacturing has made cyber threat intelligence analysis increasingly difficult. Although large…
arXiv:2608.29249v2 Announce Type: replace-cross Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by Large Language Models (LLMs). While often…
arXiv:2608.02880v3 Announce Type: replace Abstract: As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck. Most current skill…
arXiv:2609.00470v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical attack surface:…
arXiv:2609.00035v1 Announce Type: new Abstract: An LLM agent calling a production API cannot distinguish a query that matched nothing from a query the server did not understand. Both return HTTP 200 with a parsable…
arXiv:2609.00319v1 Announce Type: cross Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose a single answer…
arXiv:2609.01456v1 Announce Type: new Abstract: Composed image retrieval (CIR) retrieves a target image from a reference image and a text modification. This paper studies metadata-available CIR reranking, where a fixed…
arXiv:2609.00986v1 Announce Type: new Abstract: Industrial recommender systems typically rely on cascaded retrieval, pre-ranking, ranking, and reranking stages, whose separately optimized models limit scaling, fragment…
arXiv:2608.27394v3 Announce Type: replace-cross Abstract: Retrieved scientific literature can serve as inspiration for both human and AI scientists. Inspiration can take different forms: prior work may directly suggest…
arXiv:2609.00667v1 Announce Type: new Abstract: Large vision-language models used as listwise rerankers must jointly process visual tokens from tens of candidates per query, making token pruning essential for practical…
arXiv:2408.09199v2 Announce Type: replace Abstract: In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such…
arXiv:2609.01240v1 Announce Type: new Abstract: Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challenging: recommendation…
arXiv:2609.01030v1 Announce Type: new Abstract: Price extraction from websites is a key task for market monitoring, price comparison, and business analytics in e-commerce. Existing approaches can be broadly divided into…
arXiv:2608.30130v2 Announce Type: replace Abstract: Retrieval-augmented language models can fail to respect negative constraints when the retriever supplies evidence about concepts the user explicitly excluded. Beyond…
arXiv:2609.00313v1 Announce Type: new Abstract: Scientific discovery depends on finding prior literature that shapes what comes next. Existing retrieval systems optimize for relevance and popularity, often favoring…
arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a vital yet…
arXiv:2609.00913v1 Announce Type: new Abstract: Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user…
arXiv:2609.01316v1 Announce Type: new Abstract: Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations that plain OCR…
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