Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation
Authors: Zhiwang Zhou
Yuandong Pu
Xuming He
Yidi Liu
Yixin Chen
Junchao Gong
Xiang Zhuang
Wanghan Xu
Qinglong Cao
SHIXIANG TANG
Yihao Liu
Wenlong Zhang
LEI BAI
Venue: ICLR 2026 Poster (2026)
Keywords: AI for Science
Unified foundation model
Interpretable reasoning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. 
Omni-Weather integrates a radar encoder for weather generation tasks, followed by unified processing using a shared self-attention mechanism.
Moreover, we construct a Chain-of-Thought dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual quality.
Extensive experiments show Omni-Weather achieves state-of-the-art performance in both weather generation and understanding.
Our findings further indicate that generative and understanding tasks in the weather domain can mutually enhance each other.
Omni-Weather also demonstrates the feasibility and value of unifying weather generation and understanding.

Published as a conference paper at ICLR 2026
OMNI- OMNI-WEATHER: A UNIFIED MULTIMODAL MODEL FOR WEATHER RADAR UNDERSTANDING AND GENER- ATION
Zhiwang Zhou!.? Yuandong Pu Xuming Hc²d, Yidi Liu Yixin Chen Junchao Gong2., Xiang Zhuang24 2.4 Wanghan X Qinglong Cao Shixiang Tang², Yihao Liu Lei Bai
Wenlong Zhang
'Tongji University Shanghai AI Laboratory SShnggaii Jiao Tong University *Zhejiang University
*University Science and Technology of China UULA
Radar Sequence Understanding
Radar Inyersion
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Omtni-Weather
Radar Nowcasting
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This nowcasting exercise focuses on short-ronge precipitation.
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In the observation image. the precipitation extreme precipitation
large in the maximum level atserved...
Figure 1: Illustration of ABSTRACT
unified capabilities.
Weather modeling requires both accurate prediction and mechanistic interpreta- tion, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni- Weather, the first multi- modal foundation model that unifies weather generation and understanding within a single architecture. Omni-Weather integrates a radar encoder for weather gen- eration tasks, followed by unified processing using shared self-attention mecha- nism. Moreover, we construct a Chain-of-Thouueee dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual qual- ity. Extensive experiments show Omni- Weether achieves state-of-the-an perfor- mance both weather generation and understanding. Our findings further indi- cate that generative and understanding tasks in the weather domain can mutually enhance each other. Omni- Weather also demonstrates the feasibility and value unifying weather generation and understanding. The code and dataset publicly available https: / hub. com/ Zhouzone/ Omn Wea E ther
Equal Contribution.

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Published as a conference paper at ICLR 2026
INTRODUCTION
A significant trend in Al research is the rise of foundation models that unify generation and under- standing within a single architecture. Multimodal LLMs such as Intern VL Chen et all (20244), Uni- Gen [Tan et all (R025), and Lumina-omnily Pl (2025) demonstrate that perception and synthe- sis can be integrated seamlessly, achieving strong generalization across visual and textual domains. These advances highlight the opportunity to extend unified generationunderstanding paradigms to weather domain, where both predictive accuracy and interpretability are essential.
Recently, weather generation and understanding tasks have made notable progress. On the gener- ation side, nowcasting models such as PreDiff Gao all (2023) DiffCast et all (2024), and CasCast Gong al (2024) forecast convective evolution from radar sequences, supporting early warnings of hazards like flooding. Radar inversion methods He et all (20251)) further reconstruct radar observables from satellite channels, enabling precipitation monitoring in regions without radar coverage. On the understanding side, models such as RadarQA He (2025T) and WeatherQA Ma aLal (2022) generate diagnostic reports or identify severe weather impacts from atmospheric fields.
Despite these advances, unified architectures remain absent in the weather domain. As shown in Figure Z existing approaches are di- vided into two disjoint paradigms: model such as ClimaX Nguyen et all (2022) and Weath- erGFM Zhao et all (20224) excel at forecasting and downscaling but lack interpretation, while understanding models such as RadarQA Ha
Text Devoder
Decoder
FFN
FFN
Self Allentium
Self Allemiion
Text Encoder
Image Encoder
VAE Encoder
all (20257) and WeatherQA et (2024)
Texi Decoder
VAE Decoder
provide diagnostic reasoning yet cannot synthe- size physical fields. However, atmospheric sys- tems are inherently multiscale, shaped by storm genesis, intensification and decay, where ac- curate prediction is often accompanied by the need for mechanistic interpretation. Moreover, extreme events such rapid intensification of cyclones demand models that can not only predict hazardous outcomes but also explain the underlying drivers for actionable decision- making. Current studies isolate these linksgenerative nowcasting models do not understand radar observations, yet MLLMs do not predict radar variables. Bridging this gap with a foundation model that unifies generation and understanding therefore an urgent requirement for weather domain.
FFN
Stared Self Allention
Text Encoder
Image Encoder
VAB Enooder
Figure 2: Comparison between separated archi- tectures for weather understanding / generation (top) and unified framework with shared self- attention (bottom).
To this end, we propose Omni-Weather Weather, a unified multimodal foundation model for both weather generation and understanding. By consolidating these tasks within a shared backbone (Figure Q bottom), we further propose a Chain Thought dataset tailored for causal reasoning in generation tasks, which enables Omni-Weather to be finetuned with explicit reasoning supervision and to per- form thinking inference. Through this integration, interpretability, marking a step toward reasoning unified foundation models for weather.
bridges predictive accuracy with
The main contributions of this work can be summarized follows:
We introduce the first multimodal foundation model in weather that jointly addresses both generation tasks (e.g., nowcasting, inversion) and understanding tasks (c.g., diagnostic rea- soning, question answering) within a single model.
We demonstrate that training both generation and understanding tasks together provides complementary supervision signals, enabling Omni-Weathe to learn more transferable rep- resentations of storm evolution and improving performance on both sides.
We propose a Chain-of-Thoughe (CoT) dataset and explore its integration into weather gen- eration, enhancing perceptual quality and interpretability first step toward explainable generative modeling in weather domain.
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2
RELATED WORK
Weather generation models. Weather generation models aim synthesize physically consistent weather fields from historical or multi-modal observations Han et all (2024); Chen et all (2023): Gad
cLaL (2023); Yu all (2022);; Gal (2022); eal (2023). Examples include DiftSR He
(20255), which reconstructs composite radar reflectivity from satellite infrared and lightning inputs,
and CasCast Gong et all (2023D, which predicts precipitation evolution from past VIL sequences.
More recently, foundation scale approaches such as ClimaX Nguyen et al. (2023). FengWu Chee stal (2023)); Han et all (20244, extend transformer architectures to climate and weather forecasting. while WeatherGFM Zhao et all (2(02)) introduces in-context learning for generalist nowcasting and inversion. Despite their effectiveness in generation, these models do not address understanding or reasoning, leaving interpretability and evaluation largely unexplored.
Weather understanding models aim to interpret weather signals and provide human-readabe readable in- sights, often through natural language diagnostic reasoning. Early studies adapt pretrained lan- guage models such as ClimateBERT Wehersinke et all (2021)); Schımanski et all (2023) and Cli- mateNLP Krishnan & Anoop (202%) analyze textual weather reports, focusing tasks such as climate risk assessment, report classification, and domain adaptation. More recent work empha- sizes multimodal inputs, combining imagery with text. For example, WeatherQA ea (2024) takes 20 images of atmospheric parameters to predict regions impacted by severe convection, while RadarQA He (2125T) leverages both radar observations and numerical forecasts to generate expert-like quality assessment reports. These approaches demonstrate the feasibility of applying large language models to meteorology, but they remain limited to understanding tasks alone. par- ticular, existing models specialize in either textual analysis or visual reasoning without integrating predictive generation, leaving the connection between physical simulation and diagnostic interpreta- tion underexplored.
Unified multimodal models integrate visual understanding and generation within a single architec- ture, leveraging advances in LLMs and diffusion models Chen all (20225a M02ahia 20255);; Pi
all (2025); cL (20224); Zhuo of all (2025); Ning et all (21255). In addition to architectural unification, generalized domain prompt learning improves accessibility and transferability of scien- tific VLMs Ca all (2025). Cao all (2022) propose latent knowledge-guie
video diffusion to generate scientific phenomena from single initial frame. Transfusion Zhou et all (2022) unifies text prediction and image diffusion within a single transformer trained end-to-end on both modalities. LMFusion Shi all (2024) adapts pretrained text-only LLMs by freezing language modules and in- troducing parallel image-specific branches for efficient multimodal generation. MetaMorph Tong aal (2022)) employs iisual-Predicttv Instruction Tuning (VPiT) enable LLMs to jointly pre- dict text and continuous visual tokens from multimodal instructions. MetaQuery IPan et all (2025)) connects frozen MIL.Ms with diffusion decoders using learnable queries, enabling generation with- out compromising understanding capabilities. BLIP3-o Chen et all (20258) sequentially combines autoregressive modeling and diffusion to generate CLIP-alignes visual features, achieving state-of- the-art performance across modalities. BAGEL Deng al] (2025) scales unified modeling through pretraining on interleaved text-image-vider data, demonstrating emergent multimodal reasoning and manipulation abilities.
3 METHOD
In this section, we first introduce unified representation of weather generation and understand- ing tasks, where radar nowcasting, radar inversion, and radar image / sequence understanding are formulated under a consistent sequence-to-sequeeee paradigm. We then present Omni-Weather, a multimodal foundation model that integrates these tasks within a shared backbone, with detailed encoders, and multi-tase task training objectives. Finally, exposition of its architecture, modality-specine reasoning, including the construction of causal anno- we describe the integration of chain-offthouus tations and their incorporation in both training and inference, which not only enhances interpretabil- ity but also improves the perceptual quality of weather forecasts.
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3.1 UNIFIED REPRESENTATION OF WEATHER GENERATION AND UNDERSTANDING TASKS
Weather modeling encompasses a wide range of objectives, from predicting future radar fields Gad stall (2022) to generating textual assessments of forecast quality el (2025a). To systematically organize this diversity, we categorize the tasks into two groups: weather generation, which focuses on producing future or cross-modal meteorological fields; weather understanding, which requires generating natural-languaes
descriptions and evaluations. To support both categories, we leverage the SEVIR dataset Veillette et all (2020), which provides time-aligned radar and satellite sequences of severe weather events. Below, detail our task paradigm corresponding each category.
Weather Generation. Radar nowcasting aims to predict the short-term evolution of precipitation fields. Specifically, given ten VIL frames, the model generates the subsequent twelve frames, thereby forecasting the spatio-temporal dynamics of convective systems. Radar inversion focuses on trans- lating satellite observations into radar-derived quantities. In this task, two infrared channels (IR069 and IR IR1) are provided as input, and the objective is to reconstruct the corresponding VIL field, which requires a cross-modal mapping between satellite imagery and radar measurements.
Weather Understanding. Radar understanding tasks require the model to generate natural-language descriptions or structured assessments from radar observations and model forecasts. The input can be either a single VIL frame or temporal sequence of frames, while the output is expected to cover key aspects, including storm morphology, intensity, temporal evolution, and forecast quality (e.g.. misses or false alarms). This formulation follows RadarQA He all (2025a), where textual reports for frame and sequence are designed to support expert interpretation beyond traditional weather forecast metrics, which not only aligns more closely with real-world meteorological analysis, but also unifies image-level and sequence-level evaluation.
Formally, each task can be unified under a mapping and the target output. For example, in radar understanding. X corresponds to a predicted and observed VIL sequence pair, while Y is a textual assessment of its quality and dynamics. This unified formulation provides a consistent modeling view across generation and understanding tasks, enabling a single architecture learn from heterogeneous meteorological data.
Y. where X denotes the input data
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3.2
FOUNDATION MODEL FOR WEATHER GENERATION AND
UNDERSTANDING
Unified multimodal Model. Inspired by recent advances in unified multimodal foundation models such as Bagel-7B-Moo Deng et all (2025), we design Omni- Memi-Weether to handle both generation and understanding tasks within single architecture. Instead of training separate models for each ob- format. Given task-specific
jective, all tasks are expressed in consistent requence-to-sequees prompt Pt. radar sequence input 2t and target output ye (e.g., future radar sequence or radar assess- ment report), the model learns the mapping
yt Fe(p,.
(3.1)
where Fg denotes the shared transformer backbone. This formulation allows a single model to flexibly switch across tasks by conditioning on Pt while maintaining unified training.
Architecture. As shown in Figure B. Omni-Weather unifies generation and understanding within single backbone by embedding all task prompts through the text encoder, thereby ensuring shared textual space for conditioning across diverse tasks. In contrast, generation and understanding tasks have varying feature processing approches for vision modal. Specifically, For Radar Image Sequence Understanding, visual inputs (e.g., single VII. frame or twelve-frame VIL sequence) are encoded by the understanding encoder and concatenated with the corresponding text prompt; the fused tokens are subsequently processed by shared self-attention layers, and the text decoder produces latural-languag descriptions. For Radar Inversion, satellite channels are embedded by the generation encoder, fused in the shared self attention layers, and decoded by the VAE decoder to reconstruct the VIL field. For Radar Nowcasting tasks, ten input VIL frames are encoded by radar sequence encoder. Specifically, (2022) to provide motion-aware tokens that stabilize long-horizon dynamics and improve temporal coherence. Since directly forcing the backbone to learn multi-frame evolution with Gen Encoder proved less stable, conditioning the shared attention layers on EarthFormers temporally aggregated tokens preserves the unified pipeline while injecting reliable temporal structure. Conditioned on the fused representation, the VAE decoder outputs the forecast sequence of the next twelve VIL frames.
instantiate this temporal encoder with EarthFormer Gao et al
Weather from the pretrained Bagel-7B-Mo which pro- vides strong multimodal backbone trained on large-scale general data. Building on this foundation, we conduct domain-specine speccifcc supervised finetuning jointly across all weather tasks.
Training Objectives We initialize Omni-Weather
Formally, let r2))) be the modality- -specicccc encoder for task t. The model input sequence is defined as
X, [wu( (xx)); ke).
(3.2)
where are optional conditioning tokens (e.g.. temporal embeddings produced by the nowcasting encoder). The shared backbone produces contextualized tokens
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3.3 CHAIN-OF-THOUOEE CONSTRUCTION FOR WEATHER GENERATION REASONING
While unified training enables multi-tase learning across generation and understanding, the result- ing models still behave black boxes, lacking explicit reasoning. To enhance interpretability and
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Figure 4: Construction of our CoT data. First, we preprocess the raw SEVIR data to obtain high- quality input/ output frame pairs. Second, we carefully design prompts and leverage GPT-4o for attributes annotation. Third, the annotated attributes are incorporated into CoT prompts to generate CoT annotations, followed by a quality verification step to produce the final CoT dataset.
causal inference, we introduce Chain-of-Thougee (CoT) supervision an auxiliary instruction layer for generation tasks. The CoT explicitly captures causal and perceptual factors underlying me- teorological evolution, thereby guiding the model toward more structured reasoning about storm dynamics.
Chain-of- Thaoughf-Thought for Causal Reasoning in Weather. Our CoT formulation is tailored to the weather domain, where reasoning is framed as causal inference over storm dynamics. To opera- tionalize this, design taxonomy of causal elements with expert-defined keywords, refined via GPT-based annotation. The taxonomy adapted from RadarQA but restructured according to an- notation difficulty: causal factors (e.g., morphology, intensity and motion) relatively direct to extract, whereas outcome indicators (e.g., storm evolution patterns) require higher-level inference. For nowcasting, causal factors are first derived from the input VIL sequence, then combined with projected causal factors of the forecast frames to infer the more difficult outcome indicators describ- ing future storm behavior. For satellite-to-i to-radrr inversion, reasoning involves only causal factors, enabling a direct projection from satellite channels to single VIL. frame. Based on this structure, we construct CoT traces three-stage pipeline (Figure E) attribute annotation with GPT-4o, task- specific reasoning generation with GPT-o3, and automated verification for structural consistency, causal alignment, and terminology normalization. Detailed taxonomy design, reasoning procedures, and prompt template are provided in Appendix A.4.
Integration for unified framework. We incorporate CoT reasoning into Omni-Weather from two complementary perspectives. First, during the training phase, CoT serves auxiliary supervision, requiring the model to generate both intermediate reasoning text and the final prediction, which guides the backbone toward causal interpretability. Second, during inference, CoT is utilized as a reasoning prompt, concatenated with task-specific instructions and inputs to steer the model to- ward more structured and explainable outputs. This integration allows CoT not only enhance interpretability but also improve perceptual fidelity and qualitative consistency of generation tasks.
4 EXPERIMENTS
4.1 IMPLEMENTATION AND EVALUATION
We train our model end-to-end on a node with 8x H200 GPUs for 20k steps, using packed sequences and the Adam W optimizer Loshchiloy & Hutted (2017) with base learning rate of 2 x 10-1, weight

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Radar Inversion
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Figure 5: A set of qualitative results. We show two radar inversion examples with think traces, nowcasting case where Omni-Weather (with think trace) is compared against CasCast, DiffCast, and FarthFormer, and one example each of radar image and sequence understanding with attribute scores and textual evaluations.
surpasses all baselines.
decay of 0.05, and cosine decay scheduling with 2k-step warm-up. All images capped at resolution of 256 x 256, resulting in approximately 256 visual tokens per image.
For generation tasks, report pixel-level metries (e.g., CSI and CRPS) to evaluate radar accu- racy and perceptual metrics (c.g.. LPIPS and RadarQA score) to capture structural and semantic consistency. For understanding tasks, evaluation follows RadarQA protocols, considering both pre-
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Table 1: Quantitative results on Weather Generation and Weather Understanding tasks.The best results are highlighted in bold, and the second-bes results are underscored. Abbreviations: CSI- M CSI-Mean, R.S Radar Score, CSI-P4 CSI-Pool4, CSI-P16 CSI-Pool16, C-16 CSI@ 16, C-74 CSI@@S 74, C-160 C 160, C-181 CSI@ @ 181, C-219 CSI@219 Dyn. Dynamic Con- sistency, Cum. Cumulate Precipitation, H. Val. High. Value, R__. Rouge_ LL, B.S BertScore, Sharp. Sharpness. Metrics marked with 4 denote lower-is-better objectives, whereas metrics with- out such notation should be interpreted as higher-is-
Weather Generation
Rodar Nowcasting
Radar Inversion
Method
Method
CSI-M R.S CSIPACSI-PIGCRPS LPIPS
R.S RMSE
C-16 C-74 C-160C-181 C-219
Earthformer
0.389
1,92
0.401
0.387
0.037
0.729
0.322
UNet
1.75
0,821
0.222
0.370
0.180 0.153 0.079
0.375
Cascast
0.384
2.72
0.414
0.518
0.031
0.207
WeatberGFM
2.28
0.436
0.619
0.447
0.208
0.157 0.053
Omni Weather
0.384
2.69
0.427
0.539
0.026
0.746
0.179
2.42
0.514
0.622
0.469
0.263
0.221
0.118
Homn--Wather-Fiee thinking
0.353
2.86
0.421
0.542
0.028
0.751
0.166
2.51
0.507
0.621
0.473
0.277
0.230
0.129
Weather Understanding
Radar Sequence Understanding
Radar Image Understanding
Method
Overall Dyn.
Cum.
H. Val.
R_
B.S
GPT4
Overall
Miss
FAR
H. Sharp.
R. B.S
GPT4
Claude-somect44
20.79 20.79
20.79
21.78
0,287
0.745
5.73
32.79
32.56
34.19
24.77
46.05
0.368 0.743 5.18
Gemini-22 2.5 pro
27.59 28.34
26.72
22.47
0.254
0.739
5.77
21.40
31.16
29.65
29.30
40.58
0.348
0.741 5.63
GPT-5
49.50 36.63
35.64
30.69
0.213
6.85
56.05
21.74
32.79
40.81
0,702
RadurQA
66.17
53.31
80.52
67.67
65.35
69.19
78.60
0.512
Omni-Weather
61.79 64.05
45.19
67.29
0.446
0.810
7.48
64.30
92.21
88.72
91.4
91.74 0.543 0.760 6.03
dictionreference alignment and LLM-based external judgments. To ensure fair comparison, benchmark against the strongest available models: CasCast, DiffCast, and EarthFormer for nowcast- ing; WeatherGFM, UNet, and ViT for satellite-to-radat inversion; and GPT4-Score as well as the domain-specialized RadarQA for understanding. Full details of metrics and baselines are provided in Appendix A.
4.2 EXPERIMENTAL RESULTS
Currently, there exists unified model capable of simultancously handling both weather generation and weather understanding tasks. Existing approaches are typically specialized, such as Cascast or DiffCast for forecasting, understanding-only models such RadarQA for evaluation. In con- trast, Omni-Weather is designed as a single framework to integrate generation and understanding. While aiming for strong quantitative performance across generation and understanding tasks, our experiments further investigate how a unified framework supports mutual gains between these tasks, reveals trade-off's between perceptual reasoning and pixel-level accuracy, and leverages both scien- tific and gemeral-domain data for improved learning.
Omni- Mnni-Weather achieves superior performance in weather generation. As shown in Table Omni-Wan Weather improves both deterministic accuracy and perceptual quality in nowcasting. Com- pared with single-task baselines, our model reduces CRPS by over 15% and improves LPIPS by more than 25%, while maintaining similar CSI and SSIM. On the radar inversion task, Omni- Weather consistently surpasses both specialized (i.e., WeatherGFM) and generalist (i.e., UNet and ViT) models, achieving higher CSI scores across all thresholds, with gains up to 20% at high-value levels. Furthermore, when augmented with thinking inference, Omni-Weather achieves clear im- provements in perceptual quality, LPIPS decreases by nearly 10% while showing minor reductions on pixel-level metrics such as CSI-Mean. This highlights that explicit reasoning can enhance visual fidelity and interpretability with limited cost deterministic accuracy.
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Table 2: Training only Understanding (U), only Generation (G), or Joint (U+G). Frame / Se- quence tasks are evaluated in accuracy, GPT4- Score for understanding, CSI-M, RMSE for gen- eration.
Table 3: Effect of CoT finetuning and think- ing inference. t higher is better, lower is better. Training both with CoT finetuning and thinking inference achieves the best result in most metries. Abbreviation: R.S Radar-Score.
Undersianding
Setting
CoT FT Think Inf. CSL-M 1 CRPS 1 R.5 LPIPS 1 GPT4 Score
Accuracy 1 GPT4--ccore 1|
CSI-M
RMSE
0.347
0,023
2,423
0.182
Und-ooly
81,95 /4.34
5.78 6.03
0.042
2,032
0.213
4.21
Gen Gen-only
0.303 0.323 0.590/ 19.01
/
/
0.335
0,023
2.657
0.163
7.82
Joint (U-G) 66.65 39.58 7.48 6.03
0.338 0.347 1.514/17.11
Meai-re delivers strong results in weather understanding. Table also presents results for weather understanding tasks. Closed-source LLMs fail adapt this task, often achieving ac- curacies below 30%. While RadarQA serves as a competitive benchmark, Omni- Weather surpasses it: on radar image understanding, accuracy on key attributes (c.g., Miss and False Alarm) exceeds RadarQA by 20-25 points, and on radar sequence understanding, Dynamic Consistency improves by over 10 points with a 5% overall gain. These results highlight that Menn--eethhrr attains strong capability in understanding both weather sequences and single frames data.
Maai-ee demonstrates versatile qualitative performance across tasks. Figure Rillustrates Omni-Weathers outputs across both generation and understanding tasks. In the radar inversion task, Omni-Weather generates VII. fields with richer high-value structures and reasoning traces linking satellite cues to radar responses. In the radar nowcasting task, forecasts exhibit fine-grained storm details with improved spatial coherence, and the think traces offer interpretable accounts of storm evolution. For radar understanding tasks, Wenthre delivers expert-like outputs, combining attribute-level ratings with detailed textual evaluations that provide nomain-speciff insights.
4.3 ABLATION STUDIES AND ANALYSIS
Impact of joint training on generation and understanding. To examine whether generation and understanding tasks can benefit each other, we performed supervised fine-tuning on Bagel-7B-MoTT using only understanding data (U-only), only generation data (G-only), or both jointly (U+G), and evaluated on 200 randomly sampled validation examples per task. As shown in Table D. joint training improves performance across both understanding and generation: understanding achieves higher overall scores and better consistency, while generation gains in both accuracy and perceptual quality, These results indicate that unified training enables the model to both learn and interpret weather data more effectively, with generation and understanding tasks mutually enhancing each other.
Effectiveness of Mixed Scientific and General Data. We conducted an additional experiment by finetuning Bagel-7B-MoT on SEVIR alone versus SEVIR combined with 20k samples from the general metaquery IPan et all (20125) dataset. As shown in Figure K. the inclusion general data consistently improves model performance, particularly in deterministic metrics and perceptual quality indicators. These findings suggest that while scientific data anchors domain-speciff fidelity, general data provides auxiliary coverage of diverse patterns, enabling the model to learn more robust modal information for better representations.
Perceptual Trade-offs in Reasoning, We investigate the impact of CoT-annotated supervision and reasoning- based inference on radar nowcasting, evaluating 200 carefully sampled test cases. As shown in Table & reasoning introduces a clear trade-off: richer prompts yield more detailed gener- ations and improvements in perceptual image metrics (e.g., LPIPS and Radar-Score), while pixel- level measures such as CSI moderately decline. Beyond images, we also evaluate the generated textual explanations with a GPT4-Score, which assigns higher scores to CoT-enhanced outputs, con- firming gains in interpretability. A case study in Figure / further illustrates this effect, where rea- soning produces sharper storm structures and more coherent temporal evolution despite lower CSI, suggesting that it prioritizes semantic and structural fidelity over pixel-wise alignment. Qualitative comparisons of reasoning content provided in Appendix A.S.

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0.3471
Del goend
0,7400
0D231
0.8852
Target
w/ thinking
w/o thinking
<5 (5-
Figure 6: Effect of mixed data.
Figure 7: Case study with thinking.
5 CONCLUSION
We introduce Omni- Weather, a unified foundation model for weather generation and understanding. Through shared backbone, it supports both generation and understanding tasks. This design allows the model to surpass task-specific baselines and enables reasoning analysis. The reasoning ability further enhances interpretability and improves the perceptual quality of radar sequences. highlight- as a generalist foundation model for future weather applications.
ing the potential of Moreover, by coupling prediction with diagnostic explanations, Omni-Weather offers practical step toward event-level weather intelligence that is easier to audit and debug, rather than optimizing for metrics alone. We expect this unified formulation facilitate broader multi-task transfer and more scalable evaluation protocols across heterogeneous meteorological modalities.
Limitations. First, Omni- WeathrWethhrr cannot yet adapt to general-domain VAEs. Second, broader validation across diverse weather tasks, such as medium-range forecasting and typhoon track predic- tion, remains necessary. Third, reasoning traces may be imperfect and require stronger faithfulness guarantees (e.g., tighter alignment between textual rationales and generated/predicted fields). Ad- dressing these limitations will be crucial for advancing foundation models toward more robust and universally applicable weather intelligence.
ETHICS AND REPRODUCIBILITY STATEMENT
This work develops unified model for weather generation and understanding, aiming to improve interpretability and perceptual quality in meteorological forecasting. The datasets used (e.g.. SEVIR, RadarQA) are publicly available, and all experiments follow their licenses without involving sensi- tive information. We acknowledge that unified models may be misused without expert oversight; thus, our method is intended for research purposes and should be complemented by professional interpretation. To support reproducibility, provide detailed descriptions of tasks, model design, training objectives, and evaluation protocols, and we release a Chain-of-Thhuuhh dataset for causal reasoning at /stpss//anonyoouss 4open. science/r/cotttttc data E3D4/ along with its construction code. Upon acceptance, we will further release the complete codebase, model check- points, and data-progsssing scripts to ensure transparency and verifiability.
ACKNOWLEDGMENTS
This work is supported by Shanghai Artificial Intelligence Laboratory.
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A APPENDIX
A.1 OVERVIEW
This Appendix is structured as follows:
Sec.
Details of task paradigm and dataset.
Sec. Details of evaluation metrics.
Sec.
A
Data construction of Chain-of secin-of-thoouee thought dataset.
Sec.
Details about Chain-of-thouuge reasoning.
Sec.
More experimental results of neathWe
Sec.
Qualitative results Omni- Weather.
Sec. Usage of LLM.
A.2
MORE DETAILS ABOUT DATASETS AND TASK
A.2.1 DATA DETAIL
SEVIR.
The Storm EVent ImagRy (SEVIR) dataset is large-scale collection of temporally aligned weather observations covering the continental United States. It integrates multiple sensing modali- ties, including visible and infrared satellite imagery, lightning event records, and mosaics of Verti- cally Integrated Liquid (VIL) derived from NEXRAD radar. In this work, we focus the radar- based VIL product, which provides a spatio-temporal representation of convective storm structures. SEVIR contains over 20,000 storm events sampled between 2017 and 2020. each spanning 4-hour window at 5-minute resolution and covering approximately 384 km x 384 km regions. To support short-range forecasting tasks, sequences are typically arranged as input-output pairs, where a set of observed frames is used to predict future VII. evolution. The images are normalized to the range [0, 255] and evaluated against threshold-bant based metrics (e.g., CSI, HSS) following established proto- cols. This combination of multi-sensor coverage, temporal alignment, and standardized evaluation makes SEVIR a widely adopted benchmark for data-driven weather prediction.
RadarQA Dataset. RQA-70K from RadarQA is a large-scale forecast quality analysis dataset en- compassing four tasks: frame rating, frame assessment, sequence rating, and sequence assessment. RQA-70K is constructed through combination of human annotation and automated labeling. By integrating traditional forecasting metrics with expert knowledge, the dataset provides a comprehen- sive benchmark for the assessment of weather radar forecasting.
A.2.2 TASK DETAIL.
Weather Generation. For radar nowcasting, the model trained to predict the short-term spatio- temporal evolution of precipitation. Specifically, we use sequences of 10 observed VIL frames as input and require the model to generate the subsequent 12 frames. All frames are preprocessed to spatial resolution of 256 256, which balances coverage of mesoscale convective features with computational efficiency. This setting follows standard short-range nowcasting protocols but is tai- lored to emphasize fine-scale storm structures, ensuring that the model learns both spatial coherence and temporal continuity in convective system development.
Weather Understanding. Weather understanding tasks require the model produce natural- language descriptions and evaluations based on radar observations or forecast sequences. The input can be either single VII. frame or a sequence of frames, and the output is structured report cover- ing key meteorological aspects such as storm morphology, intensity, temporal evolution, and forecast quality (e.g.. hits, misses, or false alarms). Following the RadarQA benchmark, model responses are rated along multiple dimensions using a four-level ordinal scale (fair, poor, good, great), which are mapped to numerical values 14. Scores are then averaged across dimensions to obtain the Radar Score, providing comprehensive indicator of diagnostic quality. This task formulation bridges language modeling with -specffcc evaluation, enabling models not only to assess physical forecasts but also generate expert-like reasoning aligned with meteorological practice.
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A.3 EVALUATION PROTOCOLS AND METRICS
To enable a more comprehensive and accurate assessment, we employ a diverse set of evaluation metrics across different tasks. The detailed definitions of these metrics are provided below.
CSI. Critical Success Index (CSI) is widely used the evaluation for weather forecasting tasks. Formally, it is defined as:
TP
CSI TP+
(A.1)
where TP, PP, and FN denote the number of true positives, false positives, and false negatives, respectively. Following Cascast Gong et all (2023), we apply thresholds 16, 74, 133, 160, 181, and 219 to evaluate model performance across different VIL intensity ranges.
SSIM Wang et all (2003). Structural Similarity Index Measure (SSIM) is perceptual metric that quantifies the similarity between two images by comparing their contrast, luminance, and structure. Formally, it is defined as:
(2prl4y + C1) + C2) SSIM(Z,)) (u) + m + Ci)lo +
(A.2)
where Nlx and Hy are the means of x and y. o² and o are the variances, azy is the covariance, and Ct. C2 are small constants stabilize the division. Higher SSIM values indicate stronger similarity between the prediction and observation.
CRPS. Continuous Ranked Probability Score (CRPS) evaluates the accuracy of probabilistic fore- casts by comparing Cumulative Distribution Function (CDF) with observation I. Formally, it is defined as:
CRPS(F,)) -
(F(w) - (2y 2 x))
(A.3)
where 1/y 2 x is the indicator function.
LPIPS Zhang et all (2018). Learned Perceptual Image Patch Similarity (LPIPS) is perceptual metric designed to evaluate similarity between images a manner aligned with human judgment.
LPIPS leverages neural networks to compute differences in deep feature-based representations.
Rouge L Lin (20M44) Rouge widely used for evaluating the quality of generated text by measuring the Longest Common Subsequence (LCS) between a candidate and reference sequence. Rouge I. accounts for sentence-level similarity, capturing both content and fluency.
BertScore Zhang et al. (2019). BertScore is a learned mertic for evaluating text generation that leverages text embeddings from pretrained language models (c.g., Bert) to compute similarity between candidate and reference sentences.
GPT4-Score. GPT4-Score leverages the reasoning and understanding capabilities of GPT4 to assess generated outputs. Specifically, both the ground truth and the prediction are provided to GPT- 4. which evaluates the prediction based on overall accuracy, content richness, and fidelity to the reference.
Radar-Score. Radar-Score.RadarAAA RadarQA formulates radar understanding as a rating task, where model-generated diagnostic reports assessed along multiple meteorologically relevant dimensions, such as storm morphology, intensity, temporal evolution, and forecast quality. Each dimension is rated on a four- level ordinal scale (fair, poor, good, great), which are mapped numerical values to 4. The Radar Score is then obtained by averaging these ratings across all evaluated dimensions, producing a single interpretable measure that reflects the overall diagnostic quality of the models output.
A.4 DETAIL. OF CHAIN-OF-THOUGHT CONSTRUCTION
To enable causal reasoning over storm dynamics in the weather domain, carefully design CoT data construction pipeline to generate high-quality CoT data.
Data Preprocess. First, we extract the raw data from the SEVIR dataset and segment each event into three pairs of 10-frame inputs and 12-frame outputs. Second, we filter out samples with limited informative content and visualize the retained frames using SEVIR's colormap.
Attributes Annotation. For the constructed input / output frame pairs, we perform attributes an- notation by designing structured prompt, which encompasses four components: system prompt,
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attribute options, return format, and caution instructions. The prompt is then provided to large language model to generate structured JSON outputs that serve as inputs for the subsequent CoT annotation process.
System Prompt for Attribute Annotation
You meteorological expert. You will be given animated GIF containing the 10/12 LABEL frames to generate the final conclusions. Use the taxonomy below and produce complete "analysis label" covering all attributes, consistent with the LABEL sequence. Use only the exact option strings provided for each attribute; if an attribute is not apparent from the LABEL frames, set "choice" to "not apparent". "rationale", use probabilistic language when evidence is weak (e.g.. "likely", "possible"). Do not reference or speculate about INPUT frames.
Orientation & mapping (do not ignore):
Images are rendered with "origin= brrgin=''pperr'. Treat the top of the image as North (N), the bottom as South (S), the left West (W), and the right as East (E). Up North, Down South, Left = West, Right East. Diagonals: Up-Left= Northwest, Up-Right = North- east, Down-Left Southwest, Down-Right Southeast. Do not rotate flip the images. Determine 'Main motion direction' strictly using this mapping.
Definitions (used consistently throughout):
Convective cell: a contiguous region whose pixel value exceeds reflectivity/intensity threshold (>32 on 0255 scale; colorbar green and above). Very small isolated speck- les can be ignored when they are unlikely to influence scene-level judgment. Mainn convective system: the dominant connected system within the scene (the most spa- tially prominent/organized aggregate of cells). When multiple candidates exist, use the overall/agrregae pattern that best represents the sequence. Taxonomy (attributes and allowed (enumm Return strict JSON object in English with the exact schema: (schema_test
Attribute Options
"Morphology": (
"Spatial organization pattern at the start window (or across the input window);
"scattered many small isolated cells distributed broadly (low organization); "banded = elongated/narrow rainband aligned along main axis;
"blob-like one or a few dominant compact clusters with larger areal extent;
"spiral curved/spiraling rainbands (e.g., tropical cyclones/mesoseet vortices);
"layered = sheet-like, wide coverage with uniform texture and less distinct boundaries;
"bow-shaped an arced/bowing reflectivity segment (c.g., bow echo)."
"Max pixel level":
"Peak intensity represented by the maximum pixel value across the scene (0255). "Use the provided bins: no significant (031), very weak (3173), weak (74132), "moderate (133159), strong (160180) 80), very strong (181218), extreme (2 (219255)." Pixel intensity values map directly to the provided colorbar."
"Initial position of the main convective system":
"Coarse location the dominant connected system at t0 image coordinates;
"Centered / N S W / E/ four quadrants; or 'no clear main system' if dominance is
ambiguous."
"Main motion direction":
"Dominant displacement of the MAIN system (aggregate of cells above threshold >32)
acrOss frames.
"If multiple clusters move differently or displacement unclear, choose "no obvious motion"."
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"Main motion speed":
"Qualitative speed class based on normalized displacement per frame relative image
size:
nnerr-stationay/ slow moderate fast / very fast."
"Rotation center":
"Approximate sector of the rotation center if clear cyclonic/anticyclonic rotation is present (NNEVEISE/S/SW/WWW "otherwise no rotation' or 'location uncertain"."
"Change in number of convective cells":
"Trend in the count of distinct convective cells.
"Ignore tiny isolated speckles when appropriate."
"Morphological evolution of the main system": (
"Primary structural change of the MAIN system over time: elongation, shrinkage, ex- pansion, "merging, splitting, dissipation, generation."
"Intensity evolution":
"Overall trend reflectivity/briihhtess of the MAIN system: strengthening weaken- ing / roughly unchanged."
"Areal coverage evolution":
"Trend in the areal extent of above-threshold pixels (>32):
"expanding / roughly unchanged rapidly shrinking expaad then shrink shrink then gradually expand."
Organizzaionn evolution":
"Change connectednesssordering of the system: becoming connected (fragmented more coherent), becoming fragmented (coherent more broken).
"connected then gradually weakening (coherent loosening/fading), fragmented then becoming connected (consolidation), obvious change."
Return Format for Attribute Annotation
The output must be valid JSON (no markdown fences), with the following keys:
"sample_id". str.
"analysis "analysisjinput""
"annotations":
"Morphology":: ["choice": str, "rationale": str), "Max pixel level": ("choice": str, "rationale": str),
"Initial position the main convective system": ("choice": str, "rationale":
str).
"Main motion direction": ("choice": str, "rationale": str),
"Main motion speed": ("choice": str, "rationale": str),
"Rotation center": { ("choice": str, "rationale": str).
"Change in number of convective cells": ("choice": str, "rationale": str),
"Morphological evolution of the main system": ("choice": str, "rationale":
str).
"Intensity evolution": ["choice": str, "rationale": str).
"Areal coverage evolution": !"choice": str, "rationale": str), "Organization evolution": ["choice": str, "rationale": str)
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gglobal_rationalee
Additional Rules for Attribute Annotation
Additional rules:
Consider only INPUT frames (10..19); do not mention LABEL frames.
Cover all attributes above.
For each attribute, 'choice" must be one of the listed options verbatim, or the literal
string "not apparent" when evidence is insufficient.
Keeep explanations concise, using terms like "likely" "possibble" when appropriate.
Orientation & mapping (do not ignore): Images are rendered with 'origin= uuperr' Treat the top of the image North (N), the bottom as South (S), the left West (W), and the right as East (E). For diagonals: up-left=NNrthwest, up-right-Northeast, down- left=Southwest, wwwn-righ=SSoutheast. Do not rotate flip the images.
CCnniittncyy check for "Main motion direction". Verify that the apparent displacement the image (screen coordinates) matches the above mapping before choosing. If uncertain, choose "no obvious motion".
Defiinitin of "Main motion direction": Determine motion using the contiguous re- gions where pixel value 31 (i.c., colorbar green and above). Track the overall trajectory of these regions from to (or t10 tl for LABEL). multiple such regions exist, report their general/aggregate motion direction. If regions are too fragmented or exhibit multiple inconsistent directions, set direction "no obvious motion".
CoT Annotation. After obtaining the annotated attribute data, the large language model is prompted generate outputs a predefined sequence: task instruction, Temporal causal factor, perceptual causal factor, direct outcomes, and deep outcomes. The system prompt, cautions, return format, and instruction for each step are detailed as follows.
System Prompt for CoT Annotation
You are a senior meteorological nowcasting expert building a high-quality CoT dataset. Write in precise, fluent ENGLISH. For every step, produce flowing sentences only. Do NOT use bullet points, numbered lists, dashes, tables. Use the exact taxonomy key names ver- batim when referring to keywords; never rename invent new keys. Init thought must rely ONLY on INPUT annotations; Summary thought must rely ONLY on LABEL annotations. Keep the reasoning scientific and case-specific.
Instructions and Examples for each Step
INIT THOUGHT (INPUT-based; do not use LABEL):
Step Task instruction:
Explain in 23 sentences that this is short-range precipitation nowcasting task based on the last 10 VIL frames, and the goal is to forecast the next 12 frames.
Step 2 Temporal causal factor extraction (merged with temporal description):
Write single descriptive paragraph that uses ONLY the temporal causal factor keywords. Explicitly restate the choices for "Main motion direction". "Main motion speed", and "Ro- tation center" as observed from t0t9, and keep the prose strictly descriptive. Do NOT in- clude meta- sutmmarisssres such as this establishes premise, avoid invented terminology. mech- anisms, or evaluative language, and do not reference perceptual or outcome keywords. Use present/immediaaee past tense and clear compass terms.
Example (style only): "Across t0t9, 'Main motion direction' is east, 'Main motion speed' slow, and 'Rotation center' without rotational signatures."
rotation, the motion slow eastward translation
Step 3 Perceptual causal factor extraction (merged with spatial description):
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Write single descriptive paragraph that uses ONLY the perceptual causal factor keywords. Explicitly restate the choices for "Morphology", "Max pixel level", and "Initial position of the main convective system" as observed from t0t9, and keep the prose strictly descrip- tive. Do NOT include summarress such as this defines a baseline, avoid mechanisms or evaluative language, and do not reference temporal or outcome keywords. Use the present/immediate past tense.
Example (style only); "The 'Morphology' is bloblike, the "Max pixel level' is strong, and 'Initial position of the main convective system" is centered, yielding compact highintensity echoes near the center across t0t9."
Step 4 Direct outcomes (1stTier Outcome):
Analyze ONLY the direct outcome keyword "Intensity evolution". Write exactly ONE con- cise sentence that delivers the conclusion for "Intensity evolution", justified solely by the temporal and perceptual paragraphs (Steps 23). The sentence must explicitly name "Inten- sity evolution". Do NOT reference deep outcomes, mechanisms, grids, coordinates, any terms not present in the keyword taxonomy. Avoid generic phrases like "structural pressure" or "integration forces".
Example (style only): "For 'Intensity evolution', the strong yet steady baseline together with slow translation and no rotation supports an assessment that intensity remains roughly unchanged."
Step 5 Deep outcomes (2ndTier Structural Outcome):
Analyze ONLY the deep outcome keywords "Areal coverage evolution" and "Organization evolution". Write exactly TWO sentences "Areal coverage evolution" and one for "Organization evolution". Each sentence must de- liver a concise conclusion justified by the temporal and perceptual paragraphs (Steps 23) and the direct outcome (Step 4), and must explicitly name the corresponding deep outcome keyword. Do NOT introduce mechanisms here; do NOT add invented terminology or extra commentary.
single flowing paragraph: one sentence for
Example (style only): "'Areal coverage evolution' remains stable under slow translation and steady intensity; 'Organization evolution' shows no obvious change because the bloblike structure and absence of rotation provide no pathway to linearization or fragmentation. Final summary paragraph:
After completing Steps 15, write short paragraph (24 sentences) that integrates the INPUT- based reasoning across all steps into a coherent training target. Keep as flowing prose with no lists. Place it under "init thought.summary".
QUality Control. After obtaining the annotated CoT data, we perform a three-step quality control: Structure Check, Causal Alignment, and Terminology. Data that pass all steps are retained for training and included in the final CoT dataset.
Finally, we obtained 4,000 CoT annotations for radar nowcasting and 4,000 CoT annotations for radar inversion, which together form CoT dataset for generation tasks.
A.5 MORE DETAIL ABOUT REASONING
We provide qualitative comparisons between Omni-Wane Weather with and without CoT finetuning on both radar inversion and radar nowcasting tasks. Figures and present three representative cases for radar inversion and radar nowcasting. As shown, the CoT-finetuned model produces reasoning traces that not only accompany the generated radar fields with higher perceptual quality, but also deliver interpretable textual explanations grounded in storm dynamics. In contrast, the non-CoT model tends to generate thinking outputs that resemble post-hoc quality evaluations rather than causal reasoning, lacking direct connection to the generation process itself. This highlights that CoT supervision guides the model toward producing reasoning that is both explanatory and predictive, effectively bridging image generation with meteorological interpretation.
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Published as a conference paper at ICLR 2026
w/ cot finetune thinking
The task SEVIR IR.07 and IR069 infer radar VIL. In the central belt, IRJ07 (07 highlights minimum, signaling tall and thick copvective tawer. while 1R069 reveals collecated low, confirming adoquate upper-lexee level massture to sustain peecipitation The overlap farms blob shaped but embosded Teature moderate signals. consistent with multicel] cavironmentt Consequeill, the expected VIL high
cold
Inpus
broader weaker seattered over the central blob, with compact isolated merphology embculied broader plusdrccuuskct clustet, and the central organized not rodainng, supporting a comfident
show concgstrate
sumounded
main convelaive seciior built persistence smmightforwan advection.
he
wio thinking
w/o cot finetune thinking X
procipiation altribules. The the weather forcersting image is
given lask,
based on representation miid imagee has good performance
revcals that dynamic ovgeisicngy, the preficted direcioon aligns wyll with the obsetvod dynamics, and no artifads are forming dissipating Addicionalls. the scale consistent while the changes demonstrates great performance overal. though there is an underestimatinn of very heavy precipitation in the cast and imense precipitation in the south Based on this informaiion the content caplion can be refined accurate representation of consiitennyy stracture
image, predietion
thinking
the precipitation actributes,
emphasize under prediction. The being a key strengili and areas of improvement nated for specific precipitation levels. This ensurre that the evalusted image's strengths and limitations are clearly communicates to forocnsting community.
remains consistent, with oynamic consisiency
w/ cot finetune thinking
cold employs SEVIR
indicating tall thick convective In north sectoe, shows while IR069
1R069
collocated low. confitming liciicn upper-lev leva monslurc sustain procipalation. The overlap is compoct and blob-shiped, starding oui clearly from sum ounding warmer env invmmnnentt Radar cehoes verily this with concentrated high-re high-re/lecctvvit
Inpus
of
Conseguenlly
expocted VIL the noriil-ogntral sector
over this ssolated northem blob, yiclding, conpact selated morphology, identiiieed as the focus of strongest convection. The siricture is colicrent and stable, w without rolation, supporting confident noweasi g grounded in persistence and simple advection.
w/o cot finetune thinking X
wio Shicking
given
quality of wenther forecasting image was assessed based alignment dynamic corsiisenccy
observed
indicating
strong agreement with the observed dynamies, The predicted image maintained consistent scale for both newly farmed and dissipating convective cells and preserved the observed shape changes and organization Regarding cumallate though modkl under-prodl prodicted precipilaion valucs the casl and southeasi pegions, For value relash, perfenmance was rated as greal, most hightvaluc regions were sucvessfully telained; however, poct-prediction occurred, with signliean mistmllches moled in the caplern ancas. In summarry, the accurate sppresniation precipitation trends precipiaaiion
potterns without introdiicing
performaicce was assessed
thinking
evalusted image demompitaia underexinaaiions dynamee relention of lecalized over- pver-prediction.. Overall, the lorecasting image quality was robust.
with
w/ cot finetune thinking
The task uscs SE.VR IR107 and IRU669 to infer radar VII. A distinct cold IK 1K707 minimum apecars in the norther sector, indicating tall and dense conveetive lower, while against weaker surfoundings. Radar echoes align with this signature, showing compact. high emibediied in modest convective cluster. Cansequenlly the expected VIL is high within this northem blob. compact isplated morphology. and system remains steady and non- rotating, enabling confiieut advection.
Inpus
overlup blob-shaped revesls collocated low
level
concentrated, reflectivity core
sector identified as
convective focus. The based persistence simpic
wil drinking
w/o cot finetune thinking
X
In the given task, the quality of the weather forceasting world deservations. The e image demonstrated good performance several aspects, including high simularity observed precipilation the absence artifccss and slape
was assessed based on its alignment with ceal-
sccure prediction precipiaation Additinnlly,
spatial liatures evallpted iirage miguiiimned st strong dynamic consialency, with preducied conseelive syslem changlopsiies convective
tiniiin
for light paricularly the scale cumulativvly affecting precipitation sums specific regions, notably the center and north in terms of content cagtioning, detailed precipitation costriuuiion and cofveettvve system behavions were deseribed accurately. reinfarcing the evalupted image's relipbility summary. the evaluated image's overall performance was pronotineed, with system attributes,
matching obvervatins. However, minor discrepancies
for
pracipitation
high fidelity precipiaaion and
regional prediction
Figure 8: Radar Inversion Thinking Comparison.
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Published as a conference paper at ICLR 2026
w/ cot finetune thinking
This auk involoss sherteang proipiuation newesadtng that levengoe the gust rocent ton VII. frrmes to prodict the shbucquent Huilding the
indicating l'aese
transiation of conveciave systems the Maat motion speed is moderute: the Rotaton center classified as rotation. deseripoos prekanling comjact explssivv clatee lhe Mix posel level semaits shoogg (160) 181; litul possigg of the main convective system centered and (ben shifts custwand. Thesr perceptial deseripoes perpure neveementt the direct factors
stige the percestual
lempocal
Mosphoogy
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cutcome
morion mamigans the present convective grenth 1 he Areal coverage evulution is expanding as the bich like system spreads ststward. allowing precipitation
over broader These structural moughts complete the reasoning chain.
w/o cot finetune thinking
In the given rack. the guality of the wenther forecasting imouee nesessed based en its reprosemationn of peocipitation altpibutes predected direction
pertormance aligns with the observed crnamics, and attffess gresent. Adinsaually the scele both lecnning and distipating eniiectivv ecilis
analyais
of
prediction demonsrates iaage. while caniges shape remaini consiceci spalially For procipiition
underesimaiin pcecipiiation the east and incense precipiitioon the south, Based this intorsation the content caption can refined prelickonn The óverall sir sractue of the contert romiite consitintt wath dytamic comisicncy being a key strogghh and mize of levels. This ensures evaliated image's strengits limitattes are cleazly coimmuiicatis to the lotecasting community.
heavy
for
wia tiikiig
w/ cot finetune thinking
leverages most the Main motion weed moieerate, Recation center is no nimaiin These temporal desempturs now gude the percentua focors Within this semporal context. Max
mange procupitation twahvy frames, Ruildngg aukk objective, the Main metion dryccion
frames peedict following
procipiitionn the Inital posillon of the main convecusc e system it celter T These parcogtusi desergtton propalc the assessmant the direct system moiszains its blab Sice form while manshatingg eastward This support concausion the in extreme without signfiiant exparseet intenalty will remmin similat the
Morshology
"The moderate spred. with the ogre precipliation areo persising
sctinng the discussion vervociiee system outcomec Estending to strutural outeonies, the stahl: ren-mataiin atricture preserves its frames will
ay sileected peceipiation cast, precipstation
These
tendencies
distribuiion.
w/o cot finetune thinking
X
In given image. precipitarion pies is small seale. with mavamum procipiation lovel reaching exteeme: peecipltation distributdd
the procipittion trogulee, with scatteree patches some elungsted structares, evalstedd weather fonecasarn model demonsirates good performanct eentai eistribution and there are no igparent artifctts. The scale of both decoptiing and newly formed convective colls in the cumulase predicted values masching observed, data, however under prediction noted in the eas and the forfonnncce for, only pant the high-ssbu propipaation regions are acpuraiely prodictod with the most significant mikmatch occurning the coden ares. Dycail the wilisfictory cummiate procipitaiin prodiction but underpoffore ressining high-vaiue pescipilation regions.
central pegions,
directon
with
performance good. monty segions For high value
model shous strong dynimiee epneisencc
or
Bisku we thinking
w/ cot finetune thinking
task sovalves range precipitaiion
recent VIL
subsequent
twelve frames. Buildigg the lask objective the Stan motion direcoon from 10 19 east, the Main rictinn speed moderate, and
cocation, thomeeteiiiic iities powide the dysamic hackdrop for the forthooming drift without due Morphology hoopy belore unchanged becouse the persisiuut yarv strong Max gotel level, combined with moderate castward modio and the abselie of significam
siaady
son. andlyii Within this semperal contest, the convective system
handea,, Max pexal level staya wry streng. and Intial postioo the
fheves
sunveds outcomes. Estending to structuaa cutcemes, Arcal coverge evelution expenniing is the binded sy system elongates exstwcadd procupitition footprat Organization into more conerent because modemie, con-cnaaiionll motion allows didivauui convective clemsrts
therehy
maintaaiing an strength, leading
contectied
w/o cot finetune thinking
assessed based alsemment with observed data several attibuucs. The evaliated image demoratrates utes good pereemancc in terins of the dicnbutio of precpptation, Which prccipiation level predictted is extreme, mutching the obsetved perk preciplation valuce, Addtinnllyy the evaluated image shape peciptation areas, notabl: weaknessss. The lalse alarm snte for intaise pltiiin bich, with peodiction errrs, lie cvaluated inage diecctinn inll. y, the fulee aliem ruc vary heavy pecciptation significint. liscegpancies
the quality weather forecasting
comsisency
perspecive cousing
overepeedictonn peimarily in direction.
challleges accirately distingpsshngg high precipitation lovels summury, the evaluated maage stuws good overall performance with securnee distbbuu and singggis
precipitation prod
parscularlly
very heavy presipiution.
Limning wi dirking
Figure 9: Radar Nowcasting Thinking Comparison.
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Published as a conference paper at ICLR 2026
A.6 MORE EXPERIMENT RESULTS
All ablations in this section are conducted on the SEVIR test set with 200 sequences under the now- casting task. We report CSI-mean, CSI-pool4-mean, CSI-pool16-mean, SSIM, and PSNR. Higher values are better for all metrics.
Radar Sequence Encoder vs. VAE Encoder To validate the effectiveness of the proposed radar sequence encoder, we compare it against vanilla VAE encoder. As shown in Table Ml the radar se- quence encoder achieves consistent improvements across all CSI metries, SSIM, and PSNR, demon- strating that modeling temporal radar sequences brings substantial gains.
Table Comparison of radar sequence encoder and VAE encoder.
CSI-mean
CSI-poo14-mean
CSI-pooll6-mean
SSIM
PSNR
VAE encoder
0.2358
0.2912
0.4356
0.7528
21.42
Radar sequence encoder
0.3471
0.4003
0.5390
0.7621
23.22
Mixing General and Weather Data We further explore the impact mixing general-purpose data ("gen") and weather radar data ("weather"). Here, the notation +
means that one general dataset with 30% proportion is combined with 70% weather data, while
"0een300-- means two general datasets each contributing 30% combined with 70%
weather data. As reported in Table EJ using a single general dataset at 30% ratio achieves the best balance and outperforms other settings.
Table 5: Results under different generation data mixing strategies.
CSI-mean CSI-pool4-mean
1--epn
SSIM
PSNR
Igen30%-++eatthr7799
0.2501
0.2994
0.4261
0.6866
19.67
1gen709+-weatherr335 0.1386
0.1726
0.2859
0.6187
16.66
Igen50%-+weathee509 0.2478
0,2956
0,4174
0.6823
19.15
2gen30%<+weaaher7708 0.1091
0.1345
0.2274
0.6013
16.64
CFG Setting Lastly, we examine the choice of the classifier-free guidance (CFG) parameter. As shown in Table E setting CFG- 2 yields better overall performance compared with CFG- 1, espe- cially on CSI metrics, SSIM, and PSNR. Therefore, we adopt CFG= 2 as the default configuration.
Table 6: Ablation on CFG settings.
CSI-mean
CSI-pool4-mean
CS1-pool16-mean
SSIM
PSNR
CFG=2
0.2501
0.2994
0.4261
0.6866
19.67
CFG=1
0.1824
0.2208
0.3305
0.5123
17.33
A.7
MORE QUALITATIVE RESULTS
We provide additional qualitative examples and radar understanding tasks. Figures IM Io illustrate diverse cases, complementing the quanti- tative results in the main paper and highlighting the models ability to capture storm dynamics and generate interpretable assessments.
on radar nowcasting, radar inversion,
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Published as a conference paper at ICLR 2026
Radar Inversion
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Figure 10: Qualitative Result 1.
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Published as a conference paper at ICLR 2026
Radar Inversion
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Figure 11: Qualitative Result 2.
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Published as a conference paper at ICLR 2026
Radar Inversion
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Figure 12: Qualitative Result 3.
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Published as a conference paper at ICLR 2026
A.8 USAGE OF LLM
Large language models were employed as an auxiliary tool to support manuscript preparation, in- cluding grammar checking, sentence refinement, and clarification of technical descriptions. All Al-suggested text was carefully reviewed and revised by the authors to ensure accuracy, clarity, and scientific integrity.
26


Reviews:
[{"review_id": "qcL87GJDsu", "reviewer": "Reviewer_naQj", "summary": "The paper introduces Omni-Weather, a unified multimodal foundation model that brings weather generation and understanding into the same architecture. The authors also create a chain-of-thought dataset tailored for causal reasoning in generation and use it for finetuning and \"thinking\" inference. They show strong (often SOTA) results across nowcasting, radar inversion, and radar understanding, and provide evidence that training generation and understanding together lets the two enhance one another. Ablations further indicate that mixing scientific and general data boosts performance, especially on deterministic and perceptual metrics.", "questions": "(1) It is mentioned that there is a quality verification step to produce the final CoT dataset, including causal alignment, structure checks, etc. Is there human/expert validation at any point during the dataset generation or evaluation?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) This paper introduces a multimodal foundation model that unifies weather generation and understanding within one architecture, using modality-specific encoders, and takes a step toward reasoning-capable unified foundation models for weather.\n\n(2) They present experiments and ablations with useful insights, showing how generation and understanding tasks can mutually enhance each other.\n\n(3) They demonstrate strong results across nowcasting, radar inversion, and radar understanding, often matching or exceeding state-of-the-art models.", "weaknesses": "(1) As mentioned in the limitation section by the authors, the model cannot yet adapt to general-domain VAEs.\n\n(2) It would strengthen the paper to include a small human-validation study with weather experts. In particular, having domain experts rate the generated reports/explanations, and comparing those ratings to the LLM-based judge.\n\n(3) Results are centered on SEVIR-style radar nowcasting, satellite-to-radar inversion, and RadarQA understanding, and generalization to other weather tasks is not demonstrated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NqmbNATW3T", "reviewer": "Reviewer_dM4g", "summary": "This paper proposes OmniWeather, a weather understanding and forecasting model that aims to perform three main tasks: (i) radar inversion (ii) radar understanding (iii) radar nowcasting. The authors finetune the multimodal Bagel on their CoT dataset, and benchmark their method against unimodal models for these three tasks.", "questions": "Apart from the main issues flagged in the Weaknesses section, I have other minor comments/questions/suggestions.\n\n1. The current description of CoT data annotation and Figure 4 are cluttered and hard to follow. The authors should consider simplifying it, or replacing it with a figure that reads top-to-bottom.\n2. Why do the authors use the word \"causal\"? For example, the prompt in Appendix A.4 asks the model to extract \"Temporal causal factor, perceptual causal factor\" without any sufficient explanation of what this means. How do we trust that the model knows the true \"causal\" factors for explaining these weather phenomenon?\n3. Lines 193-197 do not add any substantive value in explaining the problem setup and should either be replaced by a more complete mathematical description of the problem setup or omitted entirely.\n4. There are insufficient architectural details about the VAE used in the radar inversion task, and these details should be added to the manuscript.\n5. The clarity in Figure 3 could be improved. In particular, it is unclear how the tokens from the different modalities are combined in the model architecture.\n6. Line 214: modal -> model\n7. The authors need to add more details about how many data samples are used for training. While the authors mention that they generate 4000 CoT samples for radar nowcasting and 4,000 CoT annotations for radar inversion, the authors should also clarify the number of samples used from RadarQA, and the general metaquery data.\n\n\nOverall, I think this is a substantial and promising paper marred by some fixable issues. I would be willing to raise my score if the authors can satisfactorily address my concerns.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The authors train a single unified model that can reason across both images and text, and effectively produce interpretable forecasts. As far as I know, this is the first work that combines weather generation and understanding in the same model.\n2. The proposed model achieves strong results on all three considered tasks.\n3. The ablations are interesting, and shed light on the different steps of the model pipeline.", "weaknesses": "1. My primary concern is that the paper overstates its scope and significance. The definition for a foundation model from [1] is \"A foundation model is any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks;\". While the paper certainly achieves impressive results in unifying different modalities, labeling the model a foundation model feels premature given that it is fine-tuned for only three task types on a limited data regime. I would recommend the authors to soften the claims in the introduction and abstract. I would also recommend a title change that better reflects the scope of the tackled problem. For example, a title with some combination of the words \"unified multi-task model for short-range weather understanding and generation\".\n\n2. The work is missing several important citations and discussions related to short-range/medium-range weather forecasting, e.g.  GenCast [2], Stormer [3], Pangu-Weather [4], Aurora [5], Prithvi WxC [6]. These are the canonical exemplars readers associate with large-scale weather pretraining/foundation model claims. Even if the focus is nowcasting, the paper should explicitly contrast goals, data scope, and evaluation scales with these systems. The authors should also compare their model against the important now-casting work [7] to better situate progress within the nowcasting literature.\n\n\n3. From my understanding, the authors use GPT-4o (Appendix A.4) to annotate radar data and identify important phenomenon from the images. I am concerned that this process might be error-prone and introduce mistakes that might propagate into the training process. Do the authors benchmark 4o annotations against a gold standard (for example, expert human)? How reliable is this data annotation process?\n\nThe manuscript also needs a precise description of the quality-control (QC) stages—currently “Structure Check, Causal Alignment, and Terminology” are named but not operationalized.\n\n\n### References\n[1] Bommasani, Rishi. \"On the opportunities and risks of foundation models.\" arXiv preprint arXiv:2108.07258 (2021).\n\n[2] Price, Ilan, et al. \"Gencast: Diffusion-based ensemble forecasting for medium-range weather.\" arXiv preprint arXiv:2312.15796 (2023).\n\n[3] Nguyen, Tung, et al. \"Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.\" Advances in Neural Information Processing Systems 37 (2024): 68740-68771.\n\n[4] Bi, Kaifeng, et al. \"Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast.\" arXiv preprint arXiv:2211.02556 (2022).\n\n[5] Bodnar, Cristian, et al. \"A foundation model for the Earth system.\" Nature (2025): 1-8.\n\n[6] Schmude, Johannes, et al. \"Prithvi wxc: Foundation model for weather and climate.\" arXiv preprint arXiv:2409.13598 (2024).\n\n[7] Ravuri, Suman, et al. \"Skilful precipitation nowcasting using deep generative models of radar.\" Nature 597.7878 (2021): 672-677.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jubAO0Ls6C", "reviewer": "Reviewer_rnQS", "summary": "This paper introduces Omni-Weather, a multimodal foundation model designed to address a significant gap in radar modeling: the separation of generation (numerical prediction) and understanding (textual interpretation). The authors propose a single architecture that unifies these two capabilities, arguing that they are mutually beneficial. The model's core contributions are its unified architecture, the introduction of a novel Chain-of-Thought (CoT) dataset for causal reasoning in weather, and its demonstration of strong performance on both task categories.", "questions": "- Why are so many different encoder/decoder's used? E.g., why are separate single-frame radar and multi-frame radar encoder needed?\n- *\"In the radar nowcasting task, forecasts exhibit fine-grained storm details with improved spatial coherence\"*... I'm not sure how the authors identify \"improved spatial coherence\" in Fig. 5?\n- Is there anything special about the extra Metaquery data that would make it particularly useful or do you think your model would benefit from any other extra, not radar specific, data? Table 5 seems to explore this a bit, but it's unclear what the two possible \"gen\" datasets are and why adding the 2nd \"gen\" dataset is so detrimental to performance.\n- Why not report CRPS for radar inversion task?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- A multimodal model is a great direction towards briding the gap between numerical prediction tasks and high-level textual interepretations/analyses.\n- The framework is well motivated and is at the forefront of such multimodal models in this weather/radar domain.\n- Clearly writing\n- Evidence that joint training/multimodality provides complementary supervision signals and better scores in some areas that just a single modality.", "weaknesses": "1. The considered data is exclusively radar. Weather in the title makes it sound overly general. As the authors point out, there would be signifcant challenges in even just extending this framework to more general weather-related tasks/dataset. Thus, I suggest writing OMNI-Radar and replacing most occurrences of weather with radar in the text. Similarly, the term \"foundation model\" in the title feels premature; this needs to urgently be renamed and the text revised to accurately reflect the true contributions of the work.\n2. Lack of clarity/details in some places. For example:\n- Unclear how encoders are trained and what their specific designs are (beyond high-level descriptions like \"VAE decoder\")\n- What's high-value retaining/matching?\n- eq 3.4 feels very abrupt... did some related sentences go missing?\n- $\\lambda_t$ is poorly explained/introduced. Multiplying both loss terms in Eq. 3.4 by $\\lambda_t$ doesn't make sense. Please correct. Also, please explain how it was tuned (same for $n_t$).\n- This claim should be toned down: *\"On the radar inversion task, Omni-Weather consistently surpasses both specialized... and generalist... models, achieving higher CSI scores across all thresholds, with gains up to 20% at high-value levels.\"* given that it's not true for the RMSE metric.\n- How's the CRPS computed? How many ensemble members are used?\n- Fig. 3: Full prompts should be included in appendix. Same for exact versioning of GPT models used\n- I'm confused by the \"CFG Setting\" (classifier free guidance) paragraph. There's no reference to CFG, not even diffusion, anywhere else... did the authors use it but forgot to mention it in the main text?\n3. No discussion of the complexity of the model, especially when compared to the \"generation-only\" baselines\n4. More comprehensive evaluations would be useful. E.g.:\n- Human expert evaluation of \"understanding\" outputs would be really useful and strong contribtuion. Are the explanations at the level of a meteorology expert? How useful are they actually? Are the textual outputs given by the model consistent with the numerical nowcasts (e.g., in fig. 4)? With the current results, it's hard to judge how scientifcally useful the \"understanding\" part of the model actually is.\n- How's the RMSE in Table 2 computed? A more comprehensive ablation (e.g. like the part of table 1 that's about radar nowcasting) would be more useful.\n5. While the paper is at the forefront of multimodal modeling for weather/radar, it's not there all by its own. The paper misses some important references and contextualization. In particular, 1) this paper is only *one* of the first multimodal models in this weather/radar domain [1], 2) There's been a benchmark proposed in this space, which includes SEVIR (the only weather dataset used on this paper) [2]. It would have been nice to use it here, but at least it should be discussed.\n\nMinor:\n- VIL should be explained before using its abbreviation form.\n- cascast is misspelled in Fig. 5\n\n[1] Aquilon: Towards Building Multimodal Weather LLMs; Varambally et al. 2025 (https://openreview.net/forum?id=KVxOwEYAF4)\n\n[2] CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting; Li et al. EMNLP 2025 (https://arxiv.org/abs/2409.19058)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ewCOBVdcrF", "reviewer": "Reviewer_3h1v", "summary": "This paper presents Omni-Weather, a multimodal foundation model that unifies weather generation and understanding within a single architecture. Unlike existing models that separately address forecasting or diagnostic reasoning, Omni-Weather integrates radar and text modalities through a shared self-attention backbone and a Chain-of-Thought (CoT) dataset to enable causal reasoning in weather modeling. The model achieves state-of-the-art results on both weather generation (e.g., nowcasting, radar inversion) and understanding (e.g., RadarQA tasks), demonstrating that generative and interpretive capabilities can reinforce each other.\n\nThe contributions are:\n1. Introduction of the first unified multimodal foundation model for weather that jointly handles generation (forecasting, inversion) and understanding (diagnostic reasoning, QA) tasks within a single framework.  \n2. Construction of a weather-specific Chain-of-Thought (CoT) dataset for causal reasoning in generation, improving interpretability and perceptual quality of outputs.  \n3. Empirical results showing Omni-Weather surpasses strong baselines (e.g., CasCast, DiffCast, WeatherGFM, RadarQA) in both pixel-level and perceptual metrics, with reasoning further enhancing visual fidelity and explainability.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces the first unified multimodal foundation model for weather generation and understanding, representing a novel and impactful problem formulation.  \n2. The Chain-of-Thought dataset for causal reasoning in weather generation is promising, enabling interpretable forecasting and bridging the gap between prediction and explanation.  \n4. The experiments are comprehensive, covering both pixel-level and perceptual evaluations with clear comparisons to strong baselines.  \n5. The paper is well-written and clearly structured.\n6. The demonstrated mutual benefit between generation and understanding tasks highlights significant scientific insight with implications for broader multimodal foundation model research.", "weaknesses": "1. The claim of a “foundation model for weather” seems overstated, as the model’s scope is limited to a single variable (radar VIL precipitation) rather than encompassing multiple atmospheric variables such as temperature, pressure and wind.  \n2. The proposed model only addresses short-range nowcasting (approximately one hour ahead) and is restricted to the SEVIR dataset covering the continental US, limiting its generalization and global applicability.  \n3. The Chain-of-Thought (CoT) dataset used for training is entirely LLM-generated, with no human expert validation or meteorological review to ensure physical correctness, as GPT-series models are not fine-tuned as meteorologist experts.\n4. The CoT generation pipeline relies on GPT-4o for attribute annotation and GPT-o3 for reasoning synthesis, producing synthetic causal narratives that may not reflect authentic meteorological reasoning. An ablation or qualitative comparison between LLM-generated CoT reasoning and human meteorologist-written reasoning would clear the confound of whether the improvements stem from genuine interpretability or stylistic mimicry of GPT.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)