Daily edition · Information Retrieval
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29 articles · 4 sources · 27 papers ·
Top topics: Information Retrieval · AI Research · AI
Most application search setups force a trade-off: settle for simple text filters, or manage a separate search cluster like Elasticsearch with complex synchronization pipelines. In a new video course o
arXiv:2610.05220v2 Announce Type: replace Abstract: Carousel interfaces allow a recommender system to directly observe how far a user has browsed. This signal distinguishes displayed but unclicked items from items that…
arXiv:2610.26932v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent,…
arXiv:2610.27344v1 Announce Type: new Abstract: Early detection of cognitive decline in older adults remains challenging due to the resource demands of standard neuropsychological assessments. Chatbot-based Intelligent…
arXiv:2610.27850v1 Announce Type: new Abstract: Recommender Systems (RSs) can be evaluated for many aspects, two of them being recommendation relevance and fairness. These two aspects can be evaluated either in…
arXiv:2511.18354v2 Announce Type: replace-cross Abstract: AI agents, not humans, are becoming the web's primary consumers, yet the web still ships content structured for human browsing, with no native way for publishers…
arXiv:2610.28569v1 Announce Type: new Abstract: Modeling lifetime user behavior sequences is essential for capturing long-range preferences in industrial recommendation, yet 100K-length user sequences make target-aware…
arXiv:2610.27232v1 Announce Type: new Abstract: Generative recommendation predicts items by autoregressively generating semantic identifiers (SIDs), whose tokens are shared across semantically related items. With this…
arXiv:2610.28568v1 Announce Type: cross Abstract: Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely determines GR quality,…
arXiv:2609.31045v2 Announce Type: replace Abstract: Learning user preferences from long behavioral sequences is fundamental to large-scale advertising, recommender systems, and conversational agents. In industrial…
arXiv:2610.27114v1 Announce Type: cross Abstract: Retrieval-augmented QA can fail even when the correct answer is in the retrieved evidence. This can happen when a competing answer satisfies some, but not all, of the…
arXiv:2610.27580v1 Announce Type: cross Abstract: Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an…
arXiv:2610.28340v1 Announce Type: cross Abstract: Reliable retrieval-augmented generation (RAG) requires recognizing when neither retrieved documents nor parametric knowledge supports an answer. We introduce…
arXiv:2610.28435v1 Announce Type: new Abstract: Multimodal rerankers are usually dedicated models: a vision-language model (VLM) is trained on retrieval data to score the candidates of a query. Scoring options is not…
arXiv:2610.27829v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages. Existing visual retrievers typically…
arXiv:2610.27195v1 Announce Type: new Abstract: Dense bi-encoders and late-interaction retrievers are usually treated as alternatives, yet they fail on different queries: one vector captures what a text is about,…
arXiv:2610.27019v1 Announce Type: cross Abstract: Cardinality estimation is essential for query optimization, but most existing learned estimators focus on a single predicate type, such as string LIKE clauses or…
arXiv:2610.26902v1 Announce Type: new Abstract: Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation…
arXiv:2610.26951v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has…
arXiv:2604.12036v4 Announce Type: replace-cross Abstract: We study the problem of constructing a decision tree that identifies an unknown hypothesis from a given ground set, under both average- and worst-case cost. The…
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