Search: “industry”

arXiv:2509.18919v2 Announce Type: replace Abstract: The pretraining-finetuning paradigm is a crucial strategy in metallic surface defect detection for mitigating the challenges posed by data scarcity.

arXiv:2605.26884v1 Announce Type: new Abstract: In this paper, we address the problem of detecting small, dense, and overlapping objects, a major challenge in computer vision. Our focus is on reviewin

arXiv:2605.24823v1 Announce Type: new Abstract: Manufacturing has passed through four widely recognized paradigms - mechanization, electrification, programmable automation, and Smart Manufacturing - e

arXiv:2605.12845v1 Announce Type: cross Abstract: Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF

arXiv:2605.09533v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly employed in enterprise question-answering (QA) systems, requiring adaptation to domain-specific knowledg

arXiv:2511.10047v2 Announce Type: replace Abstract: Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this p

arXiv:2610.06880v1 Announce Type: cross Abstract: Machine learning classifiers for bearing fault detection produce scalar confidence scores that conflate confident errors with genuinely ambiguous pred

arXiv:2609.31690v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) solutions are essential for building trust in AI technologies and their integration in real manufacturing line

arXiv:2609.29626v1 Announce Type: new Abstract: Recursive AI, the prospect of AI taking an increasingly complete role in building and improving AI, is a crown jewel of AI for AI. Although recursive se

arXiv:2609.23582v1 Announce Type: new Abstract: Maintenance work orders must often be processed offline on embedded hardware, yet downstream software requires predictable structured output. We present

arXiv:2609.23385v1 Announce Type: cross Abstract: Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation

arXiv:2609.13789v1 Announce Type: new Abstract: In multi-channel paid user acquisition, early and accurate prediction of user retention at the channel level is crucial for optimizing budget allocation

arXiv:2609.07165v1 Announce Type: new Abstract: Learning-by-Demonstration (LbD) enables intuitive robot programming by capturing expert skills, which is crucial for agility in high-mix, low- volume ma

arXiv:2609.03773v1 Announce Type: cross Abstract: Parametric computer-aided design (CAD) modeling is difficult to evaluate with a single metric. Existing CAD benchmarks often emphasize synthetic or CA

arXiv:2609.01240v1 Announce Type: cross Abstract: Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challe

arXiv:2608.28175v1 Announce Type: new Abstract: Bin-picking is a cornerstone of modern manufacturing, yet achieving complete bin clearance without manual intervention remains a critical challenge. Whi

arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in

arXiv:2408.07490v4 Announce Type: replace Abstract: In unsupervised image anomaly detection, reconstruction-based methods learn normal patterns for data reconstruction, but often undesirably reconstru

arXiv:2608.05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, wh

arXiv:2505.10300v2 Announce Type: replace-cross Abstract: Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through

arXiv:2607.13460v1 Announce Type: new Abstract: We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degr

arXiv:2607.08014v1 Announce Type: new Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models witho

arXiv:2412.08949v4 Announce Type: replace Abstract: Knowledge distillation (KD) has been widely studied in unsupervised image Anomaly Detection (AD), but its application to unsupervised multimodal AD

arXiv:2607.02611v1 Announce Type: new Abstract: Work-related Musculoskeletal Disorders (WMSDs) require continuous ergonomic assessments. While Rapid Entire Body Assessment (REBA) is a gold-standard ob

arXiv:2601.19128v2 Announce Type: replace Abstract: In terrestrial laser scanning (TLS)-based mechanical, electrical, and plumbing (MEP) point cloud segmentation, safety-critical components such as re

arXiv:2606.31820v1 Announce Type: new Abstract: Large-scale capacitated vehicle routing problems (CVRPs) are commonly addressed using cluster-first route-second (CFRS) approaches that split a routing

arXiv:2602.20551v3 Announce Type: replace Abstract: Verbal-prompted segmentation is inherently limited by the expressiveness of natural language and struggles with uncommon, instance-specific, or diff

arXiv:2606.04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane network

arXiv:2606.01542v1 Announce Type: cross Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems. Documents are split into overlapping chunks, embedde

arXiv:2605.27461v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have shown promising manipulation capabilities, yet their practical impact is often limited by the reliability dem

arXiv:2605.27486v1 Announce Type: new Abstract: Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection m

arXiv:2605.21832v2 Announce Type: replace Abstract: Modern recommender systems rely heavily on ID-based collaborative filtering: each item is represented by a unique ID embedding that accumulates coll

arXiv:2602.10450v2 Announce Type: replace-cross Abstract: Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirement

arXiv:2605.24219v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination

arXiv:2605.18025v1 Announce Type: new Abstract: While Large Language Models have achieved remarkable integration in various vertical scenarios, their deployment in the telecommunications domain remain

arXiv:2605.13172v1 Announce Type: cross Abstract: Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benc

arXiv:2605.06713v1 Announce Type: cross Abstract: Agentic AI systems can plan, call tools, inspect code, interact with web applications, and coordinate multi-step workflows. These same capabilities ch

arXiv:2602.19171v2 Announce Type: replace-cross Abstract: Parametric CAD sequences are reusable because dimensional and geometric constraints govern how parameter changes propagate. Existing CAD gener

arXiv:2601.14505v2 Announce Type: replace-cross Abstract: In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversari

arXiv:2404.11277v2 Announce Type: replace-cross Abstract: In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for ind

arXiv:2603.15262v2 Announce Type: replace Abstract: Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based

arXiv:2604.24678v1 Announce Type: cross Abstract: Large language models (LLMs) perform strongly on general-purpose code generation, yet their applicability to enterprise domain-specific languages (DSL

arXiv:2604.19451v1 Announce Type: new Abstract: Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while

arXiv:2604.18627v1 Announce Type: new Abstract: Ensuring human safety is of paramount importance in warehouse environments that feature mixed traffic of human workers and autonomous mobile robots (AMR

arXiv:2610.13806v1 Announce Type: cross Abstract: Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not

arXiv:2610.05031v2 Announce Type: replace Abstract: Large language models are increasingly used as integration layers above specialized tools, but a stronger component does not necessarily produce a s