Topic - Edition
For years, organizations have been told to accept a series of tradeoffs when it comes to their technology stacks: If you want more performance, add more infrastructure. If you want stronger security, prepare for…
Web applications built with modern JavaScript frameworks often face a critical hurdle: search engine crawlers struggling to render and index dynamic content. When launching our suite of calculation utilities at…
arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedicated to retrieving…
arXiv:2606.07235v3 Announce Type: replace Abstract: Answering questions about long multimodal documents requires distributing a fixed evidence budget across relevant facets in text, tables, figures, and slides while…
arXiv:2610.02387v1 Announce Type: cross Abstract: We present SOLO, an index for approximate nearest-neighbor search in general metric spaces whose serving path contains no ranking heuristic of any kind: a query is…
arXiv:2609.29630v1 Announce Type: cross Abstract: Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based…
arXiv:2609.38021v2 Announce Type: replace-cross Abstract: This report audits evaluation of a long-term-memory retrieval chain on the 500 LongMemEval-S development questions. Its strongest historical reader lane scores…
arXiv:2608.29480v2 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) demonstrate growing music-understanding capabilities, but whether their responses are grounded in acoustic evidence remains…
arXiv:2610.02510v1 Announce Type: cross Abstract: Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dialogue on…
arXiv:2610.02749v1 Announce Type: new Abstract: Efficient vector retrieval requires both a corpus geometry that supports retrieving the right documents through vector similarity, and a query encoder that can embed…
arXiv:2610.02572v1 Announce Type: new Abstract: Recent work on neural sparse retrieval has demonstrated strong relevance by leveraging Large Language Models (LLMs) for semantic term expansion. However, learned models…
arXiv:2610.03147v1 Announce Type: cross Abstract: Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference. Although…
arXiv:2610.02875v1 Announce Type: cross Abstract: Multilingual encoders can exhibit reduced retrieval effectiveness when queries and relevant documents differ in language, despite strong same-language performance. We…
arXiv:2610.02673v1 Announce Type: cross Abstract: A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observations scattered across…
arXiv:2610.03130v1 Announce Type: new Abstract: Biological literature retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks. However, many curated knowledge…
arXiv:2606.01782v2 Announce Type: replace Abstract: LLM-based re-rankers produce a rankings through repeated local comparisons (listwise, pairwise or pointwise), requiring many sequential model calls. We study how…
arXiv:2610.02600v1 Announce Type: new Abstract: In instruction-guided generative recommendation, LLM-based recommenders need to balance two goals: responding to the user's current request and aligning with the…
arXiv:2506.06557v3 Announce Type: replace Abstract: An ultrametric space or infinity-metric space is defined by a dissimilarity function that satisfies a strong triangle inequality in which every side of a triangle is…
arXiv:2609.38743v2 Announce Type: replace-cross Abstract: LLM agents rely on retrieval tools to access external knowledge, yet visual agentic search remains severely bottlenecked by standard single-step retrievers. In…
arXiv:2610.03651v1 Announce Type: cross Abstract: Dense-retrieval services must switch among embedding-prefix dimensions and index bit rates as latency, quality, and memory budgets change. Tuning a quantizer separately…
