Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles. Existing retrieval-augmented generation systems treat historical logs as a static corpus and retain records without estimating their diagnostic value, failing to report early risk.
arXiv:2607. 28126v2 Announce Type: replace Abstract: Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles.
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:2609.22231v1 Announce Type: new Abstract: Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories...
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.
The paper introduces candidate‑fate accounting, an audit framework for transparent sensor diagnostic pipeline search that records every candidate, including invalid, pruned, or skipped ones, and assigns a terminal fate to each. It enhances traceability by hashing repeated observations, flagging illegal candidates, and documenting budget rationales. Experiments on three bearing‑diagnostic datasets demonstrate that the framework uncovers 30–41 omitted candidates and verifies complete accounting while preserving competitive performance.
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides a structured framework that uses paired evidence, protected-slice monitoring, and trace-level localizability to decide whether to promote, flag, or reject memory interventions. The authors also present DCR, a metric for measuring failure localizability, and D$^2$ACCI‑Eval, a reusable artifact for gate replay, demonstrating significant performance gains on three public benchmarks and highlighting the importance of traceable, statistically grounded diagnostics.
The paper introduces D$^2$ACCI, a dual-loop diagnostic protocol designed to improve evidence-preserving memory in large language model agents. It provides stage-level diagnostic traces, protected-slice monitoring, and a graded observability metric (DCR) to localize failures within the memory pipeline. Applied to MemStack and evaluated on three benchmarks, the protocol achieves high accuracy and demonstrates statistically significant gains from specific memory interventions.
The paper introduces LUMIN, a lightweight network for manufacturing anomaly detection, and PSP, a four‑stage adaptive memory bank sampling pipeline that eliminates backbone forward passes. PSP achieves near‑random construction speed, 341× faster than FPS, while parallel similarity computation and stratified pixel sampling cut inference time by 20× with minimal accuracy loss. Experiments on five benchmarks show that LUMIN and PSP reach state‑of‑the‑art sampling accuracy with dramatically reduced latency and memory usage.
arXiv:2609.14976v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate memory across sessions, creating sparse but high-impact risks: stale facts, conflicting updates, cross-user leakage,...
Industrial anomaly detection (IAD) requires identifying fine-grained deviations from normal visual patterns. Multimodal large language models (MLLMs) can improve recognition accuracy by comparing query images with references at inference time, but these benefits rely on additional retrieval and processing.
CF-YOLO introduces a real‑time detection framework for camouflaged micro‑defects on industrial components, combining a Context‑Perception Aggregation Module (CPAM) that fuses large‑kernel macro‑texture cues with small‑kernel boundary details, and a Feature Additive Refinement Module (FARM) that globally refines fine‑grained anomaly representations. The authors also release the Copper Tube Defect Dataset (CTDD), a benchmark of 1,847 images with 4,898 annotated defect boxes. Experiments show CF‑YOLO outperforms baseline detectors such as YOLOv11 by 2.2% in mAP@50 and 3.9% in Precision while preserving real‑time speed.
Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics proposes a decision-support framework that transforms return notes into a condition factor and a signal‑quality score. These metrics guide how deeply to inspect returned assets and how to allocate them for recovery, balancing labor constraints. In synthetic benchmarks across IT decommissioning, aircraft maintenance, and consumer‑electronics returns, the keyword‑based implementation outperforms a structured‑feature comparator by improving net recovery value and lowering inspection costs, with the greatest economic benefit observed in the aircraft scenario.