Daily edition · Information Retrieval
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41 articles · 4 sources · 40 papers ·
Top topics: Information Retrieval · AI · AI Research
arXiv:2609.28589v1 Announce Type: new Abstract: Industrial recommendation systems typically operate as a \emph{cascade} of retrieval, pre-rank, and fine-rank, but these stages are usually trained and served as separate…
arXiv:2609.29933v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts.…
arXiv:2609.29803v1 Announce Type: new Abstract: Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Although large language…
arXiv:2609.30001v1 Announce Type: cross Abstract: Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next…
arXiv:2609.29721v1 Announce Type: cross Abstract: Text-to-video retrieval usually represents a video clip by a single embedding. This embedding often loses important relations between people. E.g., an interaction "Anna…
arXiv:2605.25749v2 Announce Type: replace Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in exploring…
arXiv:2609.29145v1 Announce Type: cross Abstract: A generative search answer can cite a supported passage yet omit a source relationship that changes its interpretation. We specify a claim-gated audit of the…
arXiv:2609.29819v1 Announce Type: cross Abstract: In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power.…
arXiv:2606.23057v2 Announce Type: replace Abstract: This exploratory study measures brand inclusion across five industries, 50 brands and 250 queries, each put five times to GPT-5.2, Gemini 3 Flash and Perplexity…
arXiv:2609.05059v2 Announce Type: replace Abstract: Repeated-query audits must distinguish recovery of a collected set from completeness of possible outputs. We apply sample-based rarefaction to 4,500 responses from 50…
arXiv:2609.29973v1 Announce Type: new Abstract: Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction…
arXiv:2512.14277v2 Announce Type: replace Abstract: The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of generating structured queries,…
arXiv:2609.29282v1 Announce Type: new Abstract: While graph-based and hypergraph-based Retrieval-Augmented Generation (RAG) significantly mitigate hallucinations in Large Language Models (LLMs), existing structure-based…
arXiv:2609.29649v1 Announce Type: new Abstract: Oblique retrieval, as exemplified by OBLIQ-Bench, asks a retriever to find documents whose relevance is determined by a latent attribute (an implicit stance, an analogous…
arXiv:2609.29180v1 Announce Type: new Abstract: Recent advances in generative modeling have reshaped recommender systems by formulating recommendation as a next-item generation problem. Existing retrieval approaches…
arXiv:2609.28980v1 Announce Type: new Abstract: LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set,…
arXiv:2609.29474v1 Announce Type: cross Abstract: Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge ---i.e., the…
arXiv:2609.29695v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) supports multi-hop reasoning by organizing corpora into structured graphs. However, graph reachability often captures…
arXiv:2507.00938v4 Announce Type: replace Abstract: Foundation models now enable autonomous agents to interact with real-world websites, but existing benchmarks emphasize general-purpose browsing, underrepresent…
arXiv:2609.29652v1 Announce Type: new Abstract: Reasoning-intensive retrieval remains difficult for small models. Compact public ColBERTs are usually trained on general-purpose corpora and underperform reasoning-tuned…
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