Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines

• URL: http://arxiv.org/abs/2607.24692v1
• Date: Mon, 27 Jul 2026 17:34:09 GMT
• Title: Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines
• Abstract summary: Inference systems increasingly combine a fast path that returns predictions within the application's latency deadline together with a higher-accuracy slow path.<n>We show that shaped workload attacks can exploit contention at shared resources along the slow path to push benign users' predictions past their latency deadlines.<n>We demonstrate accuracy collapse in a two-tier edge-cloud multi-object tracking pipeline in autonomous driving.
• Score: 31.515415657058906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference systems increasingly combine a fast path that returns predictions within the application's latency deadline together with a higher-accuracy slow path that runs higher-compute methods on stronger, remote hardware, so its results can be returned on time and combined with the fast path predictions. Across several application domains, we abstract this inference architecture as a fast path, a slow path, and a coordination layer with two functions: a router that invokes the slow path and a merger that decides whether to incorporate its returned predictions. In this work, we show that this new coordination layer exposes a new attack surface: shaped workload attacks, e.g., Yo-Yo bursts, can exploit contention at shared resources along the slow path to push benign users' slow-path predictions past their latency deadlines. The merger then discards those predictions, while the fast path continues to return timely outputs. We refer to the resulting loss of slow-path accuracy benefits as accuracy collapse. We demonstrate accuracy collapse in a two-tier edge-cloud multi-object tracking pipeline in autonomous driving. In simulation, approximately 4,000 burst-shaped requests increase benign p99 latency from 92ms to 2s, nearly eliminating the benefit of the slow path's cloud inference, reducing object tracking quality by 7.0 HOTA points on average. We further find that accuracy degradation can significantly vary (2.0-18.7 HOTA points), depending on the video intervals that are targeted in the attack, and that certain rare classes (e.g., stop signs) lose nearly half of their pre-attack prediction accuracy. These results show that workload attacks can degrade prediction quality without needing either access to model weights or victim data, and motivate research on attacks and defenses for routing, merging, scheduling, and resource isolation in these emerging inference pipeline architectures.

Related papers

• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry [16.577608323279662]
  UNRIO is an uncertainty-aware radar-inertial odometry system.<n>It estimates ego-velocity directly from raw mmWave radar IQ signals.
  arXiv  Detail & Related papers  (2026-04-15T07:43:06Z)
• Towards Scaling Law Analysis For Spatiotemporal Weather Data [50.25336689856158]
  We extend neural scaling analysis for autoregressive weather forecasting from single-step training loss to long rollouts and per-channel metrics.<n>We find strong cross-channel and cross-horizon heterogeneity: pooled scaling can look favorable while many channels degrade at late leads.<n>We discuss implications for weighted objectives, horizon-aware curricula, and resource allocation across outputs.
  arXiv  Detail & Related papers  (2026-04-06T18:14:53Z)
• Trajectory Imputation in Multi-Agent Sports with Derivative-Accumulating Self-Ensemble [16.79253001706658]
  We propose MIDAS (Multi-agent Imputer with Derivative-Accumulating Self-ensemble), a framework that imputes multi-agent trajectories with high accuracy and physical plausibility.<n>Experiments on three sports datasets demonstrate that MIDAS significantly outperforms existing baselines in both positional accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2024-08-20T14:08:16Z)
• Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting [8.970625329763559]
  This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS) data. We have developed a deep auto-encoder model and a phased framework approach to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data as input. We have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-10-29T09:15:22Z)
• Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
  We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting" Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
  arXiv  Detail & Related papers  (2023-10-02T17:13:16Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer [0.9786690381850356]
  We introduce a model called PRediction Transformer (PReTR) that extracts features from the multi-agent scenes by employing a factorized-temporal attention module. It shows less computational needs than previously studied models with empirically better results. We leverage encoder-decoder Transformer networks for parallel decoding a set of learned object queries.
  arXiv  Detail & Related papers  (2022-03-17T12:52:23Z)
• Semantics-STGCNN: A Semantics-guided Spatial-Temporal Graph Convolutional Network for Multi-class Trajectory Prediction [9.238700679836855]
  We introduce class information into a graph convolutional neural network to better predict the trajectory of an individual. We propose new metrics, known as Average2 Displacement Error (aADE) and Average Final Displacement Error (aFDE) It consistently shows superior performance to the state-of-the-arts in existing and the newly proposed metrics.
  arXiv  Detail & Related papers  (2021-08-10T15:02:50Z)
• LTN: Long-Term Network for Long-Term Motion Prediction [0.0]
  We present a two-stage framework for long-term trajectory prediction, which is named as Long-Term Network (LTN) We first generate a set of proposed trajectories with our proposed distribution using a Conditional Variational Autoencoder (CVAE) and then classify them with binary labels, and output the trajectories with the highest score. The results show that our method outperforms multiple state-of-the-art approaches in long-term trajectory prediction in terms of accuracy.
  arXiv  Detail & Related papers  (2020-10-15T17:59:09Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.