Daily edition · Information Retrieval
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30 articles · 4 sources · 29 papers ·
Top topics: Information Retrieval · AI · AI Research
How to Track Product Launch Clicks with JavaScript (And Where to List Your Side Projects) Every developer who launches a side project or micro-SaaS wants to know where their referral traffic comes from. Here is a quick,…
arXiv:2610.01118v1 Announce Type: cross Abstract: A long-term conversational assistant must recall the right memory at the right moment, yet the memory that matters most is often not similar to what the user says now.…
arXiv:2610.00817v1 Announce Type: cross Abstract: Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data…
arXiv:2610.01064v1 Announce Type: cross Abstract: Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key…
arXiv:2610.00964v1 Announce Type: new Abstract: For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-stage retrieval designed…
arXiv:2610.01270v1 Announce Type: cross Abstract: Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head operating only on…
arXiv:2610.01553v1 Announce Type: new Abstract: Patent search requires processing documents routinely exceeding tens of thousands of tokens. Most neural retrieval approaches operate on truncated inputs, limiting their…
arXiv:2610.01463v1 Announce Type: new Abstract: Thematic corpora, such as social media communities, contain unstructured text describing data that could be made structured. These include, for example, personal…
arXiv:2610.01533v1 Announce Type: cross Abstract: Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent…
arXiv:2610.02202v1 Announce Type: cross Abstract: What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in…
arXiv:2610.00791v1 Announce Type: cross Abstract: Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirements. These…
arXiv:2610.01139v1 Announce Type: new Abstract: Semantic IDs (SIDs) compress item embeddings into discrete code sequences used in generative retrieval. We ask whether a multilingual encoder is sufficient for…
arXiv:2609.38646v2 Announce Type: replace Abstract: Generative recommendation (GR) has emerged as an alternative to embedding-based retrieval, building on the success of generative models in language and vision. We are…
arXiv:2610.01705v1 Announce Type: new Abstract: LLM-based personal agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user…
arXiv:2610.00369v1 Announce Type: new Abstract: Laurito et al. (PNAS 2025) showed that large language models choosing between two descriptions of the same product, paper or film prefer the description written by a…
arXiv:2610.00587v1 Announce Type: new Abstract: The corpus of worldwide news is important for natural language processing, knowledge graphs, large language models, and other technical efforts. Additionally, this corpus…
arXiv:2605.25007v2 Announce Type: replace Abstract: Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate…
arXiv:2609.38353v2 Announce Type: replace Abstract: Long-term memory lets LLM agents recall past interactions and remain consistent across sessions, but memory systems are hard to compare because they often vary in…
arXiv:2610.02057v1 Announce Type: new Abstract: Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions…
arXiv:2610.00253v1 Announce Type: new Abstract: Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile describes the system on…
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