GateFormer is a vision‑transformer model that forecasts intra‑hour global horizontal irradiance (GHI) from sky images by decomposing the forecast into a trajectory prediction and a delta prediction, then adaptively weighting these two outputs. It incorporates recent GHI measurements into the visual token stream, enabling self‑attention between image patches and irradiance data. On multiple benchmarks (NREL‑TSI, Chilbolton, SIRTA) GateFormer achieves the lowest final‑horizon root‑mean‑square error, outperforming state‑of‑the‑art transformers and demonstrating significant gains during rapid irradiance ramps.
By David Hamlyn, Sunny Chaudhary, Jonathon Hare, Tasmiat Rahman
GateFormer is a dual‑decoder Vision Transformer that forecasts intra‑hour global horizontal irradiance (GHI) from sky images by embedding observed GHI into the visual token stream and predicting both a future GHI trajectory and the final‑horizon change. The two predictions are adaptively weighted in prediction space, with magnitude‑ and direction‑supervision to reduce remaining error. On multiple benchmarks, GateFormer achieves the lowest final‑horizon RMSE, outperforming state‑of‑the‑art transformers and showing significant gains especially during positive ramp events.
arXiv:2608. 00675v1 Announce Type: cross Abstract: Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against.
By Alexander Scheinker
The paper introduces SolCloudLLM, a large language model–based framework that fuses sky‑image patches with time‑series data through bidirectional multimodal fusion for short‑term solar forecasting. Experiments on the SIRTA and SKIPP'D datasets show that SolCloudLLM outperforms existing baselines, achieving up to a 25.4% reduction in mean squared error, especially under cloudy conditions and in few‑shot scenarios.
By Ken Chen, Maneesha Perera, Wei Wang, Sachith Seneviratne, Hansani Weeratunge, Saman Halgamuge
arXiv:2606. 12987v1 Announce Type: cross Abstract: Action-conditioned world models let an autonomous vehicle predict future camera scenes from its own planned controls, enabling planning and simulation without real-world rollouts, but at compact, trainable scale the futures are ambiguous and the field's standard distortion metrics actively mislead: they reward a blurry regression mean over a realistic prediction.
By Ruslan Sharifullin, Benjamin Jiang, Kai Xi Chew
Triangular Resampling (TR) is a post‑training technique designed to reduce long‑horizon error accumulation in motion diffusion models by extending rollout‑based training to partially denoised states. TR uses a shared denoising threshold to replay multi‑step triangular denoising without gradient tracking, replacing states below the threshold with noise‑matched ground truth while retaining predictions above it. The method, applied to HumanML3D motion generation, achieves state‑of‑the‑art FID AUC, reducing it by 40.9% and the FID degradation slope by 55.3% compared to matched post‑training without replay.
By Kunhang Li, Yiyi Cai, Xiangyue Zhang, Fangyuan Tu, Yuhan Wu, Zhixiang Wang, Kaipeng Zhang, Haiyang Liu
The paper introduces TRACK, a training‑free trajectory routing method that accelerates video diffusion by selectively switching between large and small models during denoising steps. A calibration process generates a disagreement score map, guiding the selection of the appropriate model at each step to maintain quality while reducing computational cost. Experiments on Wan 2.1, Cosmos 3, TurboDiffusion, and FastVideo show speedups ranging from 1.95× to 2.73× with comparable quality and diversity.
By Mustafa Munir, Huy Vu, Shreyas Misra, Rohit Jena, Sajad Norouzi, Ali Taghibakhshi, Anis Ahmad, Anjul Patney, Pavlo Molchanov, Nima Tajbakhsh
The paper introduces a dual‑framework Vision Transformer (ViT) method for estimating diffuse horizontal irradiance (DHI) from sky images in data‑scarce settings. It combines physics‑based atmospheric parameters, a sequential pipeline that first predicts synthetic global horizontal irradiance (GHI), and a dual‑parallel architecture that processes raw and physics‑informed overlay‑enhanced fisheye images. Tested on Chilbolton Observatory data, the approach achieves RMSE within 2–6 W/m² of a ViT trained on a full dataset, demonstrating a scalable, cost‑effective solution for solar resource assessment when long‑term sky imagery is limited.
By David Hamlyn, Sunny Chaudhary, Tasmiat Rahman
arXiv:2607. 15849v1 Announce Type: cross Abstract: Autoregressive video diffusion models have enabled the generation of arbitrarily long videos by removing conditioning on future frames, thus greatly improving computational efficiency.
By Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris, Efstratios Gavves
arXiv:2608. 09971v1 Announce Type: cross Abstract: Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-range skill matches or exceeds that of the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES).
By Minjong Cheon
The study trains a convolutional LSTM on ten annual Earth Observation variables to forecast desertification dynamics in Ceará, Brazil, achieving modest improvements over persistence and capturing 53‑69% of the regional degradation trend. While the model slightly outperforms simple baselines one to four years ahead, it explains only a small fraction of cell‑level variance, indicating that EO data are more reliable for regional trends than for individual pixel predictions. The authors also demonstrate that elevation and EVI channels degrade forecast skill, and use Monte Carlo dropout to quantify uncertainty in future degradation maps.
By Filipe Moura Da Silva, Cl\'aubio Landney Lima Bandeira
arXiv:2609.23686v1 Announce Type: new
Abstract: Patch-based autoregressive time-series forecasting often ties input representation, learned transitions, and recursive execution to one patch length. W...
By Ziang Li, Yue Huang, Guoxu Zhou, Na Han, Jie Wen, Lunke Fei, Xiaozhao Fang