The article presents a reference framework for autonomous extract‑transform‑load (ETL) pipelines to consolidate Salesforce data during mergers and acquisitions. It details a partitioned bulk extraction, declarative mapping, rule‑based and statistical quality gates, master data management with deterministic and blocked fuzzy matching, constrained loading with error capture, and change data capture protected by a data‑health circuit breaker. The prototype was evaluated on synthetic scenarios, achieving high F1 scores for account and contact matching and preventing load failures and data loss.
arXiv:2607. 18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls.
arXiv:2606. 17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring.
AegisFlow is a multi‑agent AI framework that automates the detection and remediation of failures in data pipelines, using a Watchdog agent for telemetry and a Repair agent that generates, tests, and deploys code patches via large language models. It employs a non‑intrusive Parallel Shadow Patching approach based on the MAPE‑K loop to validate patches in digital twin environments. Experiments across five failure scenarios show a 98.1% reduction in mean time to repair—from 170 minutes to 3.2 minutes—and a 92% patch success rate, freeing up 98% of data engineering on‑call time for innovation.
Analog-DB is an open‑source, versioned database that stores analog integrated circuit designs in a process‑neutral topology, reusable testbenches, and machine‑readable datasheets using a domain‑specific language. The database’s parameterization scheme and schema‑governed contracts enable AI design agents to discover, compose, and retarget circuits across multiple process kits. In a case study, a coding agent used the released artifacts to size op‑amp cores for a chopper instrumentation amplifier, uncovering defects that a sizing‑only baseline missed.
Sharing analog integrated circuit designs remains difficult: foundry non-disclosure agreements restrict the process details a design depends on, and the testbenches behind published results are rarely...
arXiv:2606. 26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing.
arXiv:2506. 01584v2 Announce Type: replace-cross Abstract: Developing machine learning (ML) systems for real-world deployment requires navigating context-dependent trade-offs among accuracy, fairness, stability, and other objectives.
arXiv:2608. 03311v1 Announce Type: cross Abstract: Enterprise compliance management requires rapid adaptation to evolving regulatory frameworks (e.
arXiv:2607. 16388v1 Announce Type: cross Abstract: Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans.
arXiv:2607. 26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines.
The paper introduces a synthetic data generator that creates fully consistent, fictional enterprises—complete with workforce, customers, sales, support, and communication records—without relying on any real dataset. It validates realism through a five‑axis scorecard, an adversarial detector, and soundness checks, achieving a mean realism score of 99.1 across 23 generated companies. A second generator produces relational databases from business questions, ensuring qualifying rows and exact labels, and is available as a hosted service and containerized simulators.
arXiv:2608. 08618v1 Announce Type: cross Abstract: Industrial device commissioning requires engineers to manually extract hundreds of protocol-specific parameters from heterogeneous PDF manuals and transcribe them into supervisory control systems, a time-intensive, error-prone workflow.