Search: “enterprise”

arXiv:2608.01042v1 Announce Type: cross Abstract: Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could se

arXiv:2607.23326v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) has driven transformative adoption across enterprises. However, deploying these models in real-world sett

arXiv:2607.20065v1 Announce Type: new Abstract: Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explainable, and gove

arXiv:2607.11019v2 Announce Type: replace Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineerin

arXiv:2606.30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is

arXiv:2606.06545v1 Announce Type: cross Abstract: Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) int

arXiv:2606.02109v1 Announce Type: new Abstract: Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approac

arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba

arXiv:2605.15665v1 Announce Type: new Abstract: Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and

arXiv:2605.15505v1 Announce Type: new Abstract: In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication

arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Exis

arXiv:2604.24978v1 Announce Type: new Abstract: Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We

arXiv:2604.19457v1 Announce Type: new Abstract: Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy mem

arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc

arXiv:2507.03336v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly tasked with invoking enterprise APIs, yet they routinely falter when near-duplicate tools vie for the

arXiv:2604.09608v1 Announce Type: new Abstract: While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive i

arXiv:2610.07258v1 Announce Type: cross Abstract: Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester

arXiv:2610.02122v1 Announce Type: cross Abstract: Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on t

arXiv:2610.01833v1 Announce Type: new Abstract: Enterprise AI agent skills evolve as tool APIs, models, and specifications change, yet final-output evaluation can miss process-level behavioral drift.

arXiv:2609.35645v1 Announce Type: cross Abstract: Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition

arXiv:2609.24220v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, present

arXiv:2609.18605v1 Announce Type: cross Abstract: As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and fin

arXiv:2609.17775v1 Announce Type: new Abstract: Enterprise guideline documents mix narrative text, complex tables, and embedded images, and converting them into structured work artifacts still takes t

arXiv:2609.13334v1 Announce Type: cross Abstract: Enterprise AI agents often succeed in a demonstration and then stall once they must operate day after day. An industry report estimates that most pilo

arXiv:2609.04377v1 Announce Type: new Abstract: Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and

arXiv:2607.17883v2 Announce Type: replace-cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output tha

arXiv:2608.23569v1 Announce Type: new Abstract: State-of-the-art Natural Language to SQL (NL2SQL) models report execution accuracy exceeding 89 percent on established benchmarks such as Spider and BIR

arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, a

arXiv:2608.20661v1 Announce Type: new Abstract: Enterprise adoption of large language models in finance is constrained less by fluency than by trust: in Financial Planning and Analysis (FP&A) and othe

arXiv:2608.11584v1 Announce Type: new Abstract: Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requireme

arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse c

arXiv:2606.28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different

arXiv:2601.20408v2 Announce Type: replace-cross Abstract: Enterprise LLM deployment faces a critical scalability challenge: organizations must optimize models systematically to scale AI initiatives wi

arXiv:2606.05836v1 Announce Type: new Abstract: Large language models have substantially advanced Text-to-SQL systems, yet applying them to enterprise-scale databases remains challenging. Real-world d

arXiv:2605.24183v1 Announce Type: cross Abstract: We introduce AvalancheBench, a benchmark for evaluating enterprise data agents through latent world recovery. AvalancheBench improves on existing benc

arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings

arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software

arXiv:2605.17380v1 Announce Type: new Abstract: We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents oper

arXiv:2604.26382v1 Announce Type: cross Abstract: Most enterprise document AI today is a pipeline. Parse, index, retrieve, generate. Each of those stages has been studied to death on its own -- what's

arXiv:2604.11623v1 Announce Type: new Abstract: We introduce Context Kubernetes, an architecture for orchestrating enterprise knowledge in agentic AI systems, with a prototype implementation and eight

arXiv:2609.11286v1 Announce Type: new Abstract: Synthetic relational data is normally produced by a model trained on a real dataset, and its quality is measured as the distance to that dataset. This p

arXiv:2609.10494v1 Announce Type: new Abstract: Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet a

arXiv:2609.09402v1 Announce Type: cross Abstract: Enterprises managing large X.509 certificate inventories face a prioritization problem: deterministic analysis tools that precisely identify standards

arXiv:2609.04641v1 Announce Type: new Abstract: Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise schemas exhibit graph- like, semi-structured,

arXiv:2609.00572v1 Announce Type: cross Abstract: Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite pers

arXiv:2606.19319v2 Announce Type: replace-cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively