SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting

• URL: http://arxiv.org/abs/2605.00126v1
• Date: Thu, 30 Apr 2026 18:27:08 GMT
• Title: SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
• Abstract summary: Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees.<n>We introduce SPLICE, a modular framework coupling latent generative imputation with distribution-free, online-adaptive prediction intervals.<n>A pooled JEPA encoder trained on nine feeds transfers to four unseen domains, matching or exceeding per-dataset oracles with only a quick bridge fine-tuning.
• Score: 0.5076419064097734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes), a modular framework coupling latent generative imputation with distribution-free, online-adaptive prediction intervals. A JEPA encoder maps daily load segments into a 64-dimensional latent space; a conditional latent bridge with four sampling modes generates candidate gap trajectories; an hourly-conditioned decoder maps back to signal space; and Adaptive Conformal Inference (ACI) wraps the output with coverage-guaranteed prediction bands. The flow-matching variant achieves comparable quality to DDIM in 5--10 ODE steps (5-10x speedup). On thirteen load datasets (nine proprietary, three UCI Electricity, ETTh1), SPLICE achieves the lowest mean Load-only MSE (0.056), winning 9/12 non-degenerate datasets at 91-day gaps and 18/32 across all gap lengths vs. five established baselines, and produces the best CRPS (0.161, -18.3% vs. the strongest competitor). ACI delivers 93--95% empirical coverage, correcting under-coverage failures of up to 7.5 pp observed with static conformal prediction. A pooled JEPA encoder trained on nine feeds transfers to four unseen domains, matching or exceeding per-dataset oracles with only a quick bridge fine-tuning.

Related papers

• On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability [57.03393882933515]
  We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and 51B parameters of n-gram embedding tables held off the accelerator.<n>On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs.
  arXiv  Detail & Related papers  (2026-08-31T06:35:07Z)
• A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning [0.0]
  Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance.<n>We show that self-supervised pretraining with task-aware fine-tuning yields features that are both sharp near onset and robust across prediction horizons.
  arXiv  Detail & Related papers  (2026-07-18T07:27:04Z)
• A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids [0.0]
  Hybrid CNN-LSTM IDS exploits CNN-based spatial feature extraction and LSTM-based temporal sequence modeling.<n>Model achieves a real-time inference throughput of 27,800 flows/s on GPU and 0.082 ms/sample CPU latency in FP32, with INT8 quantization providing an additional 3.1 x speedup at 0.3% accuracy loss.
  arXiv  Detail & Related papers  (2026-06-23T21:44:25Z)
• Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study [48.83701310501069]
  This paper investigates whether a single recipe is sufficient for heterogeneous architecture pools, or whether the optimal pairing varies across structurally diverse models.<n>We conduct a systematic empirical study of all $3 times 6 = 18$ combinations of six classifiers paired with three loss functions: Cross-Entropy (CEL), Negative Log-Likelihood (NLL), and the recently introduced genetically evolved NGL loss across the base models presented in LEMUR heterogeneous architecture pool on six image classification datasets.<n>Our results confirm that no single pairing is universally optimal. Cross-Entropy with Adam or AdamW is the most robust choice across
  arXiv  Detail & Related papers  (2026-06-18T20:51:42Z)
• Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories [7.992399976910451]
  Clin-JEPA is a multi-phase co-training framework for predictive (JEPA) pretraining on EHR patient trajectories.<n>On MIMIC-IV ICU data, three independent evaluations support the framework.
  arXiv  Detail & Related papers  (2026-05-11T16:54:23Z)
• Near-Policy: Accelerating On-Policy Distillation via Asynchronous Generation and Selective Packing [44.26853590985694]
  Near-Policy Distillation (NPD) is an asynchronous approach that decouples student generation from training.<n>NPD achieves a 8.1x speedup over on-policy baselines and outperforms SFT by 8.09%.<n>Our method enables openPangu-Embedded-1B to reach a state-of-the-art score of 68.73%, outperforming the substantially larger Qwen3-1.7B.
  arXiv  Detail & Related papers  (2026-05-07T09:50:53Z)
• PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence [14.663411219312827]
  PanLUNA is a compact FM that processes EEG, ECG, and PPG within a single shared encoder.<n>PanLUNA matches or exceeds models up to 57$times$ larger: 81.21% balanced accuracy on TUAB abnormal EEG detection.
  arXiv  Detail & Related papers  (2026-04-05T22:35:42Z)
• RG-TTA: Regime-Guided Meta-Control for Test-Time Adaptation in Streaming Time Series [0.0]
  Test-time adaptation (TTA) enables neural forecasters to adapt to distribution shifts in streaming time series.<n>We propose Regime-Guided Test-Time Adaptation (RG-TTA), a meta-controller that continuously modulates adaptation intensity based on distributional similarity to previously-seen regimes.
  arXiv  Detail & Related papers  (2026-03-29T18:57:34Z)
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels [49.35636088613484]
  Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces.<n>We introduce LeModelWorld, the first JEPA that trains stably end-to-end from raw pixels using only two loss terms.<n>With 15M parameters trainable on a single GPU in a few hours, LeWM plans up to 48x faster than foundation-model-based world models.
  arXiv  Detail & Related papers  (2026-03-13T19:48:14Z)
• Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine [11.630090481307342]
  This paper investigates lightweight, explainable, and hardware-efficient jamming detection using the Convolutional Tsetlin Machine (CTM)<n>CTM formulates Boolean logic clauses over quantized inputs, enabling bit-level inference and deterministic deployment on FPGA fabrics.<n>We benchmark CTM against a convolutional neural network (CNN) baseline under identical preprocessing and training pipelines.
  arXiv  Detail & Related papers  (2026-03-07T20:54:10Z)
• Do Generative Metrics Predict YOLO Performance? An Evaluation Across Models, Augmentation Ratios, and Dataset Complexity [43.338311770275745]
  We present a controlled evaluation of synthetic augmentation for YOLOv11 across three single-class detection regimes.<n>We benchmark six GAN-, diffusion-, and hybrid-based generators over augmentation ratios from 10% to 150% of the real training split.<n>For each dataset-generator-augmentation configuration, we compute pre-training dataset metrics under a matched-size bootstrap protocol.
  arXiv  Detail & Related papers  (2026-02-20T03:02:36Z)
• HQP: Sensitivity-Aware Hybrid Quantization and Pruning for Ultra-Low-Latency Edge AI Inference [0.0]
  Hybrid Quantization and Pruning (HQP) framework designed to achieve synergistic model acceleration.<n>HQP framework achieves a peak performance gain of 3.12 times inference speedup and a 55 percent model size reduction.
  arXiv  Detail & Related papers  (2026-02-02T18:17:45Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Physics-Inspired Modeling and Content Adaptive Routing in an Infrared Gas Leak Detection Network [19.83756107644484]
  We present a physics-edge hybrid gas dynamic routing network (PEG-DRNet) for detecting infrared gas leaks.<n>PEG-DRNet achieves superior overall performance with the best balance of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-12-29T06:28:20Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• FlowTS: Time Series Generation via Rectified Flow [67.41208519939626]
  FlowTS is an ODE-based model that leverages rectified flow with straight-line transport in probability space.<n>For unconditional setting, FlowTS achieves state-of-the-art performance, with context FID scores of 0.019 and 0.011 on Stock and ETTh datasets.<n>For conditional setting, we have achieved superior performance in solar forecasting.
  arXiv  Detail & Related papers  (2024-11-12T03:03:23Z)

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.