The paper introduces Task‑Aware Diffusion Refiner (TADR), a conditional diffusion model that refines imputed data for edge anomaly detection in IIoT pipelines, addressing the bias of standard MSE‑trained imputers. TADR is trained against a frozen downstream detector’s loss landscape, requiring no extra back‑propagation through sampling, and achieves a 0.338 F1 improvement over the unrefined detector on the SMD dataset. The method’s effectiveness depends on the presence of continuous, redundant sensor channels and is less beneficial for telemetry data dominated by one‑hot command signals.
arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.
The paper investigates why per-layer pruning of IoT intrusion detectors can cause severe class-level failures. On the CICIoT2023 dataset, a two-layer convolutional detector pruned at 80% sparsity loses 16 accuracy points and half its macro‑F1, with 17 of 34 classes heavily damaged. The failure is traced to the first layer’s weight starvation, and the authors show that protecting those 192 weights or pruning globally, as well as recomputing normalization statistics, can prevent or repair the collapse.
arXiv:2605. 24251v2 Announce Type: replace Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints.
arXiv:2610.02552v1 Announce Type: cross Abstract: Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most adversaria...
The paper introduces Meta‑Diff, a method that learns a reusable source‑to‑target relation in a compact task space, enabling rapid adaptation of a diffusion model to new targets with minimal or no target data. By encoding each distribution into a low‑dimensional coordinate and transporting a source coordinate to its predicted target, the approach avoids costly fine‑tuning and achieves significant performance gains on CIFAR‑100 and Office‑Home benchmarks. Experiments show that when task identity is preserved, the relation generalizes across tasks, and the source‑target shift is manageable, Meta‑Diff can outperform target‑only modeling and full fine‑tuning. whyItMatters:"The method offers a practical solution for few‑shot adaptation in data‑scarce settings, such as healthcare, by reducing computational cost and improving reliability compared to traditional retraining approaches."
arXiv:2608. 15277v1 Announce Type: cross Abstract: Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality.
arXiv:2609.39067v1 Announce Type: new Abstract: Elastic Compute Cloud (EC2) Spot is 60% to 90% cheaper than On-Demand but can be reclaimed on just a 2-minute notice; for expensive multi-node training...
arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architec...
arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.
arXiv:2601.01480v3 Announce Type: replace-cross Abstract: Traffic forecasting systems rely on fixed sensor networks that frequently exhibit contiguous blackouts. Such outages are usually treated as i...
CTRL‑FM is a flow‑matching framework that preserves observed sensor data while inferring missing values during long‑horizon node outages. It treats structural guidance as an inference‑time decision, selecting among uncontrolled, graph‑dominant, temporal‑dominant, and balanced policies based on self‑supervised proxy outages and local reliability estimates. Experiments on PeMS08, METR‑LA, Air‑36, and AQI show that CTRL‑FM outperforms existing graph, diffusion, and flow‑based methods, especially under 30% and 60% node outages, and degrades gracefully up to 75% sensor loss.
arXiv:2608. 01725v1 Announce Type: cross Abstract: Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon.