arXiv AI
Jun 2
Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems
arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.
Positional task conditioning for scalable defect detection across product families in large product catalogs
The paper presents a method called Positional Task Conditioning (PTC) to improve defect detection in large product catalogs. By breaking detection into focused sub‑tasks and reinforcing task identity at prompt boundaries, PTC reduces context length and isolates error types, boosting F1 scores from 52% to 87%. The approach outperforms rationale‑based distillation across multiple models, achieving near‑state‑of‑the‑art performance at up to 98% lower cost and is deployed in several countries handling over 10 million product families.
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arXiv Machine Learning
Aug 11
From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection
arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.
arXiv AI
Aug 28
PACEShop: Evaluating Personalized, Actionable, Compositional, and Evidence-grounded Shopping Assistants
PACEShop introduces a new evaluation framework, PACE, for shopping assistants that emphasizes personalized, actionable, compositional, and evidence‑grounded responses. The benchmark dataset contains 22,625 records with structured personas, auditable evidence pools, and detailed defect annotations, while PACEJudge offers a training‑free protocol for assessing these dimensions. Experiments demonstrate that generic judges miss key diagnostic fields, whereas PACEJudge improves evaluation across persona alignment, cross‑component consistency, grounding, and defect localization without retraining.
arXiv AI
Jun 9
Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.
arXiv Machine Learning
Sep 3
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
The paper investigates how combining soft prompts via task arithmetic can reduce reliance on confounding variables in classification models. It introduces Hybrid Prompt Arithmetic (HyPA), which merges task prompts with linearized confounder prompts to counteract spurious correlations. Experiments across multiple benchmarks show that HyPA consistently improves the robustness‑performance trade‑off under distribution shift, and analysis of hidden representations suggests it mitigates confounding by diminishing the influence of confounder signals.
arXiv AI
Jun 26
Diagnosing Task Insensitivity in Language Agents
arXiv:2606. 26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak.
arXiv Computer Vision
Aug 25
Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing
arXiv:2608.21967v1 Announce Type: new Abstract: Automated visual inspection in manufacturing aims to replace slow and inconsistent manual checks, but its economic value depends on whether its decisio...
arXiv AI
Jul 14
Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs
arXiv:2607. 10666v1 Announce Type: cross Abstract: Deploying AI-based visual inspection in manufacturing is hard because requirements change often, new defect types appear, and large labeled datasets are rarely available.
arXiv AI
Jul 17
CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models
arXiv:2607. 14396v1 Announce Type: new Abstract: Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values.
arXiv Machine Learning
Jun 11
DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
arXiv:2606. 11616v1 Announce Type: new Abstract: High-quality training data is essential for the success of machine learning models.
arXiv AI
Aug 11
ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs
arXiv:2607. 28126v2 Announce Type: replace Abstract: Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles.
Hugging Face Trending Papers
Jun 1
Monitoring Agentic Systems Before They're Reliable
Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the failure landscape. At this maturity level, task-level error detection may be infeasible: structural failure modes mask the signal that task-level monitors are designed to detect.