Topic - Edition
arXiv:2609.23766v1 Announce Type: cross Abstract: Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators cannot send…
arXiv:2609.22375v1 Announce Type: cross Abstract: Introduction. Open collaborative knowledge graphs such as Wikidata increasingly ground agentic artificial intelligence, information retrieval, and language modeling…
arXiv:2606.08151v4 Announce Type: replace Abstract: Modern large language model (LLM) agents do not simply need longer contexts; they need decision-relevant evidence at the moment of action. We study decision-aware…
arXiv:2609.22601v1 Announce Type: cross Abstract: The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized information…
arXiv:2512.21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to laypersons remains…
arXiv:2609.23307v1 Announce Type: cross Abstract: This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incoming job descriptions…
arXiv:2609.24122v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) is hard to monitor in production: exhaustive relevance labels do not exist for non-stationary multi-million-passage corpora that…
arXiv:2609.25192v1 Announce Type: new Abstract: Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, authoritative source…
arXiv:2609.25405v1 Announce Type: cross Abstract: Modern information retrieval increasingly employs both embedding and generative models to handle complex queries. However, current serving systems suffer from low…
arXiv:2609.26250v1 Announce Type: new Abstract: Rerankers are increasingly evaluated through downstream language-model answers. This raises a retrieval-measurement question: if only the reader's answer interface…
arXiv:2609.25189v1 Announce Type: new Abstract: Generative search systems rank products and services for consequential decisions, and publishers can cheaply make candidate text look relevant. Yet evidence status is not…
arXiv:2608.25922v2 Announce Type: replace-cross Abstract: GNN-based Knowledge Graph Question Answering (KGQA) pipelines process queries through four discrete stages: entity linking, subgraph retrieval, GNN reasoning,…
arXiv:2609.25408v1 Announce Type: cross Abstract: Online A/B experiments are the decision standard for user engagement, but traffic and readout time limit how many conversational-AI changes can be tested. We ask whether…
arXiv:2609.26171v1 Announce Type: new Abstract: Leaks such as the Panama and Paradise Papers expose large networks of offshore entities, and they invite an obvious question for network learning. Can the relations these…
arXiv:2608.18752v3 Announce Type: replace Abstract: Statutory retrieval is necessary for citation-grounded legal question answering, but remains underexplored for Greek. We introduce GreekBarRetrieval, a public…
arXiv:2609.22770v2 Announce Type: replace Abstract: We study a parameterized hybrid ranker that fuses a dense embedding list and a sparse lexical list. The method has a small, explicit parameter vector: a dense prior…
arXiv:2609.26780v1 Announce Type: cross Abstract: Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what,…
arXiv:2609.23354v2 Announce Type: replace Abstract: Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents…
arXiv:2609.03450v2 Announce Type: replace Abstract: A model that inherits one-line memories may pull one archived source record before acting; a directive in the store can steer that pull: a pointer, a criterion or…
arXiv:2609.25991v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) gives large language models (LLMs) access to external knowledge, but its conventional retrieve-concatenate-generate pipeline makes…
32 items available
