Topic - Edition
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:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval offers an efficient…
arXiv:2609.30205v2 Announce Type: replace Abstract: Large language model (LLM)-based clinical assistants are increasingly being integrated into electronic health record (EHR) systems, transforming how clinicians…
arXiv:2609.39189v1 Announce Type: new Abstract: Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal…
arXiv:2609.37574v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used to enrich user queries in information retrieval (IR) so that a standard retriever such as BM25 can bridge…
arXiv:2608.03756v2 Announce Type: cross Abstract: A common task in legal Information Retrieval (IR) is to find relevant legal sources from case-law collections. While legal practice often requires pinpoint citations…
arXiv:2610.00170v1 Announce Type: new Abstract: We study commercial intent inference that runs entirely on the user's device, under three constraints frozen before the work began: the downloaded payload under 3 MiB,…
arXiv:2610.00052v1 Announce Type: new Abstract: We evaluate six language-model configurations on requests for six distinct random integers from 1-49. Across 1,200 attempted calls using four English prompt variants,…
arXiv:2610.01767v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance retrieval quality with computational cost. This cost is incurred during…
arXiv:2610.00923v1 Announce Type: new Abstract: Multimodal RAG retrieves text, tables, images, and videos, but choosing a retrieval granularity does not determine how much context to retain within each item. Coarse…
arXiv:2610.00971v1 Announce Type: cross Abstract: Moving a workflow developed and tested at one HPC site to another rarely succeeds without some amount of trial and error. Package managers rebuild software environments,…
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…
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: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: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: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: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:2610.01705v1 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: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:2610.01139v1 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…
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