MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation
Authors: Rongsheng Wang
Minghao Wu
Hongru Zhou
Zhihan Yu
Zhenyang Cai
Junying Chen
Benyou Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Microscale Simulation
Video Generation
Benchmark
Text-to-Video Dataset
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ-on-chip systems, and disease mechanism studies, while also showing potential in education and interactive visualization. In this work, we introduce **MicroWorldBench**, a multi-level rubric-based benchmark for microscale simulation tasks. MicroWorldBench enables systematic, rubric-based evaluation through 459 unique expert-annotated criteria spanning multiple microscale simulation task (e.g., organ-level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimensions (e.g., scientific fidelity, visual quality, instruction following). MicroWorldBench reveals that current SOTA video generation models fail in microscale simulation, showing violations of physical laws, temporal inconsistency, and misalignment with expert criteria.
To address these limitations, we construct **MicroSim-10K**, a high-quality, expert-verified simulation dataset. Leveraging this dataset, we train **MicroVerse**, a video generation model tailored for microscale simulation. MicroVerse can accurately reproduce complex microscale mechanism. Our work first introduce the concept of **Micro-World Simulation** and present a **proof of concept**, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale simulations of biological mechanisms.

Published as a conference paper at ICLR 2026
MICROVERSE: A PRELIMINARY EXPLORATION To- WARD A MICRO- WORLD SIMULATION
Rongsheng Wang Minghao Wu'
Hongru Zhou
Zhihan Yu!
Zhenyang Cail Junying Chen
Benyou Wang
'The Chinese University of Hong Kong, Shenzhen PPeking Union Medical College Hospital
3 *Shenzhen Loop Area Institute
ABSTRACT
Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phe- nomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ-on-chip systems, and disease mechanism studies, while also showing potential in education and inter- active visualization. In this work, we introduce Micro WorldBench, a multi-level rubric-based benchmark for microscale simulation tasks. MicroWorldBenne en- ables systematic, rubric-based evaluation through 459 unique expert- annotaedd criteria spanning multiple microscale simulation task (e.g., organ-level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimen- sions (e.g., scientific fidelity, visual quality, instruction following). MicroWorld- Bench reveals that current SOTA video generation models fail in microscale simu- lation, showing violations of physical laws, temporal inconsistency, and misalign- ment with expert criteria. To address these limitations, construct MicroSim- 10K, a nigh-quality, expert-verified simulation dataset. Leveraging this dataset, we train MieroVerse, video generation model tailored for microscale simula- tion. Micro can accurately reproduce complex microscale mechanism. Our work first introduce the concept of Aliero-Worke Simulation and present a proof of concept, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale sim- ulations of biological mechanisms. Our data and code are publicly available at http:://gtttciancceeeeeeeeeeeeen
INTRODUCTION
World models LeCun (2022); Bruce et al. (2024); Lu et al. (2024) have been extensively studied for their ability to simulate environments and agent interactions. They offer a unified computational framework for perceiving surroundings, controlling actions, and predicting outcomes, thereby ducing reliance on real-world trials. This not only robotics engines Luo & Du (2024); [u et al. (2024) engines and reinforcement learning planners Hafner et al. (2020); Agarwal et al. (2025). but also enhances decision makingg supports safe exploration, and enables scalable learning.
Recently, video generative models have demonstrated strong potential to acquire commonsense knowledge directly from raw video data, ranging from physical laws in the real world to embod- ied behavioral patterns Brooks al. (2024), laying the foundation for their as real-world sim- ulators. For example, prior work Luo & Du (2024) employs vidco-guided goal-conditioned explo- ration, grounding large-scale video generation model priors into continuous action spaces through self-supervision, enabling robots to master complex manipulation skills without explicit actions or rewards; and other works Lu (2024) leverage video generation models for embodied decision-
"Equal Contribution.
1 "orresponniig author.

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Published as a conference paper at ICLR 2026
Alveoli Demonstration
Craste medical
animation of bummmnn
lag ahvoal wia detalled capilaries end blod Sow:
Cell Division
Create cimplified
cell diviaios,
DNA Replication
Create simple animation DNA curning into XNA strand through palymerase
Prompt
Real Video
Sora
Vco3
Figure 1: Failure cases of Sora and Veo3 on Microscale Simulation. Although Sora and Veo3 generate results that appear visually correct, their violations of physical laws are particularly evident.
making, allowing agents to imaginatively explore their environment with high generative quality and consistent exploration.
Despite tremendous progress in video generation for natural scenes and human- centered do- mains AleenAl (2024): Google DeepMind (2025); Kong et all (024);; Wan et al. (2025); Yang et al.
2024), research efforts have remained predominantly focused the macroscopic scale. This suc- cess has not translated effectively to the microscopic scale, where current state-of-the-art models fail to produce physically plausible or biologically meaningful dynamics, shown in Figure [ Microscopic simulation, which tracks the interactions of atoms, molecules, and cells to uncover underlying mechanisms, is crucial for applications in materials science, biomedical research Dario et al. (2000), education Romme (2002), and interactive visualization White (1992). The failure of existing models, primarily due lack of incorporated biomedical knowledge, highlights a critical gap despite the strong potential of microscale simulation for generating clinically realistic dynamics fields like drug discovery and disease modeling. To address this, we aim to explore the potential of educational microscale simulations of biological mechanisms.
In this work, we introduce Micro WorkdBench, a multi-level rubric-basee benchmark for microscale simulation tasks comprising 459 real-world tasks that span organ-level, cellular, and subcellular pro- cesses. These tasks were jointly selected from large candidate pool by LLMs and domain experts for their diversity and relevance, with each task paired with self-contained, objective evaluation criteria specifying the essentials for valid simulation. Our extensive experiments across a broad spectrum of video generation models reveal that while most maintain superficial visual coherence and adhere to prompts, they perform poorly in microscale settings, consistently failing to generate biologically plausible dynamics. These failures indicate that current models, trained predominantly on human-scale videos, lack grounding in microphysical principles and knowledge.
To mitigate the gap, we introduce Micro Verse, video generation model tailored for microscale simulation. Micro Verse is built on Wan2. 1 Wan et al. (2025) model and trained with MicroSim-105% the first microscale dataset containing 9,601 expert-verified scenarios. Unlike human-scale datasets, MicroSim-108K emphasizes physical plausibility and biological fidelity across diverse microscale mechanisms. On MirroWWrl, MicroVerse surpasses original model by more than +2.7 in scientific fidelity, highlighting the importance of domain-specift data.
Our contributions are summarized as follows: (i) We introduce the concept of Micro-World Sim- ulation and present proof of concept, which includes a clear objective, dedicated benchmark, a training dataset, and a tailored model. (ii) We propose Mirdeehrl..ench, the first rubric-based benchmark specifically designed for evaluating microscale simulation in video generation; (iii) construct MicroSim- 10K, a large-scale, expert-verified verified dataset microscale simulation vidcos: (iv) We introduce MicroVerse fine-tuned video generation model built upon MicroSim-10Kk achiev-
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Task Description
Models
Generated Video
Rubric Criteria & Weights Weights
Criterion
Task
Hrythracytes Hieonaeee
Sora
Create a microscopic blood flow scone with rod blood cells, glooose molecules,, stucttuees
mederutee aanoszale smoller
optake
Ve03
regaliciu
005
accunielly
No
Requirement
The blood vesul
Wan2.2
look
+1.
semi-transgarent biconcave red blood colls and crystalline
smooth usbrokznn
Yes-1
glucess flowing through clastic vessels. Use realistic lighting highlight microscopic
CogVideo
Glacoes mnoogresrtic as eryeask not hydhaasd gyelle poolessicc
0.5
dynumics, and ensure visuals clearly demonstrate insulin- driven glucuse uptake comprehension.
Hunyun
Aced Soer 42%
Figure 2: Illustration of Micro WorldBench Evaluation Process. A MicroWorldBench example con- sists of generated microscopic video and of task specifp evaluation criteria written by experts. A MLLM--bseed scoring system rates responses according each criterion.
ing competitive performance MicloWorkdBench by reducing violations of scientific constraints and improving temporal and spatial consistency.
BINR A RUBRIC-BASED BENCHMARK FOR MICROSCALE SIMULATION
Generic Evaluation Fails to Capture Microscale Simulation Dynamics Existing evaluation meth- ods for video models often rely on generic scoring rules or high-level principles ManciplesHHung et al. (2024); Zheng al. (2025): He et (2024), which are insufficient for microscale simulation. Such meth- ods overlook the need for fine-grained microscopic simulations, resulting in misaligned outcomes and failing capture deficiencies in physical plausibility and biological fidelity. In this work, the proposed Rubric evaluation addresses this gap by introducing task-specifer criteria with differenti- ated weights. Rubrics highlight the most critical dimensions identified by experts and ensure that evaluations emphasize substantive shortcomings rather than being diluted by aggregate scoring.
In this section, we introduction the core structure of the rubric-based benchmark, covering task selection (Sec. (2.]). prompt design (Sec. 2.2). and rubric construction (Sec. 2.3). and describe the methodology for model evaluation (Sec. 2.4h.
2.1 TASK CHOICE
Biological systems are inherently hierarchical, encompassing levels from society, body, organ, and tissue to cell, organelle, protein, and gene Qu et al.) (20TT). Given constraints of practicality impact and data availability, in this work we focus three representative levels as principled sampling of this hierarchy. Importantly, this choice does not discard existing scientific frameworks, but rather reflects consensus based selection of the most representative and tractable scales.
Organ-level simulations are essential because they connect microscale behaviors with macroscopic physiological functions. Dynamic processes such cardiac contraction or vascular deformation are directly related to medical diagnosis, surgical planning, and edu- cation. A benchmark that evaluates these dynamies provides direct path toward clinically relevant applications.
2. Cellular-level simulations are central to biology and medicine, as cell migration, pro- liferation, and interaction underpin processes such as tissue growth, wound healing, and immune response. Accurate modeling at this level enables researchers and students to vi- sualize and understand the driving forces of health and disease, creating opportunities for both discovery and pedagogy.
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3. Subcellular-Ievel simulations present the most fine fine-grained view, capturing biochemical and biophysical mechanisms that govern life at its foundation-fusse apoptosis, signaling cascades. Evaluating generative models at this level is particularly important, as these processes are both visually subtle and mechanistically complex, requiring high fidelity and physical plausibility.
2.2
PROMPT SUITE
Both the sampling process of diffusion- based video generation models and the development of expert-driven evaluation rubrics computationally expensive. To ensure efficiency, control the number of tasks while maintaining diversity and coverage. The construction follows a two-stage pipeline: (!) collecting tasks related to microscale simulation from YouTuuee; and (2) expert filtering to retain only scientifically meaningful tasks. The final suite contains 459 tasks: 238 at the organ level, 189 at the cellular level, and 32 at the subcellular level. The proportion of tasks consistent with the distribution of levels in the collected videos.
Collecting and Generating Prompts We retrieved over 8,000 YouTube videos using topic-specific queries related to organ-level. cellular-level, and subcellular-eev simulations. For each video, we collected metadata including titles and descriptions. This information was then provided GPT-4o, which generated tasks describing the microscale mechanism. Finally, we generated 8,162 tasks. The prompts used instruct GPT-4o refer to Appendix[]
Expert Filtering We filtered the generated tasks based on two criteria: (1) the diversity of the tasks, and (2) the practical relevance of the tasks. For diversity, we asked GPT-4o to classify each task into one of the following categories: Organ-level simulations, Cellular-level simulations, or Subcellular- level simulations. For practical relevance, we invited three biology experts, and each task had to receive agreement from at least two of the three experts. A task retained in Micro only if satisfied both criteria. Classification prompts are in Appendix
2.3 RUBRIC CRITERIA
As shown in Figure 2 each MicroWorldBench example includes a task instruction and rubric cri- teria, drafted by LLMS and refined by experts. These criteria evaluate scientific fidelity, visual quality, and instruction following. Scientific fidelity emphasizes mechanistic accuracy rather than visual realism. An LLM-based grader then scores the output, providing a standardized, interpretable assessment.
Due to limited expert availability and efficiency concerns, we adopt a collaborative approach where LLMs generate initial rubric drafts and experts perform revision and validation. This method not only improves the efficiency of rubric construction but also ensures broader coverage and more comprehensive consideration despite the small number of experts.
Stage 1: Rubric Drafts Generation For each task, GPT-5 generates a set of fine-grained criteria:
P = (a,, d,, St, w.) w where a, denotes the evaluation dimension, d, is the description of the i-th criterion, S; +1, -1 the polarity indicating whether the point contributes (+1) or deducts (-1), and w, E (0. 1] is the weight reflecting its importance (e.g., W; 1.0 for core scientific require- ments, w/ 0.5 for key but secondary requirements, and w,;
for auxiliary or presentational).
The score for each task defined as: S = EN S; W1 To ensure comparability across tasks, we normalize it: Snmm ΣN wet ensuring a maximum of 100 and preventing minor positives from offsetting severe scientific errors."
x 100 where Σ n is the maximum score from positive criteria,
Stage 2: Expert Revision and Validation Domain experts refine the L.M-ggeneraed rubric through the following actions:
Deleting or filtering criteria: Experts retine the criteria by modifying or removing d, that are redundant, irrelevant, or scientifically trivial.
Adjusting weights: When the weight of certain criteria does not align with the scientific validity of the task, experts modify the corresponding weight w,.
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Table 1: Performance comparison of different video generation models MicroWorkdBench. Bold
indicates the best performance.
Model
Average 1
Organ-level
Cellular-Ievel
Subcellular-Ieee
Open-Source Video Generation Models
Hunyuan Video
23.2
23.1
23.8
19.4
CogVideok
43.5
39.9
47.0
38.6
Wan2. 1-T22-1.B
49.4
45.9
51.7
52.4
Wan2.2-T12VVSBB
51.6
46.6
53,9
49.5
Wan2.1-T2V-14B
54.8
55.7
54.4
52.8
Wan2.2-T2V-A14B Vilcr-Verse-..33 (Ours)
53.8
56.3
$2.0
53.3
50.2
47.6
51.7
53,3
Commercial Video Generation Models
Sora Veo3
50.7
55.9
46.1
55.0
77.2
77.5
76.9
78.2
Table 2: Performance comparison of different video generation models on MicroWorldBench (dimension-werl scores). Bold indicates the best performance.
Model
Average
Scientific Fidelity
Visual Quality 1
Instruction Following
Open-Source Video Generation Models
Hunyuan Video
23.2
15.6
48.2
23.4
Cog VidcoX-SS5
43.5
37.4
64.1
38.6
Wan2. 1-32--1.3
49.4
40.3
71.8
50.1
Wan2.2-T12VV5B
51.6
40.7
82.7
47.0
1-
54.8
42.7
86.0
53.8
Wan2-2-T2V-A14B
53.8
37.8
92.8
55,4
MicroVerse--133B (Ours)
43.0
49.3
Commercial Video Generation Models
Sora
50.7
35.3
96.4
37.9
Voo3
77.2
65.7
97.0
77.0
Supplementing criteria: If the automatically generated criteria fail to cover essential scien- tific dimensions, experts can introduce new tuples (a,, (cy,dj,oo 8j, wy)-
We invited three experts to participate in the revision and validation process. Each expert first in- dependently reviewed and modified the evaluation criteria, including adjusting weights, removing redundant items, and supplementing any missing dimensions. All modifications were documented with clear rationale to ensure transparency. The proposed changes from experts were then aggre- gated, and conflicts were resolved through discussion, majority voting. For more analysis on expert revision and validation, refer to the Appendix
2.4 EVALUATION RESULTS AND ANALYSIS
Settings We evaluated video generation models on microscopic simulation tasks using World Bench, including open-source models (e.g., Wan2.1 1 Wan et al. (2025). Hunyuan Video Kong et al. (0244) and commercial models (e.g., Sora (penAA] (0024), Veo3 Google DeepMind] (2025)). Infer- conducted once per model under default settings to ensure fairness and consistent resolu- tion. Rubric evaluation employed ALM-as-a-Judge Zheng et al. (2023), with GPT-5 serving as the Judge. The configurations and sampling details in the Appendix
Overall Results As shown in T the performance of different models varies significantly across organ-level, cellular-level, and subcellular- tasks. Although commercial closed-soures models, such as Veo3, substantially outperform spen-source models in overall scores, their advantage is mainly confined the visual quality dimension rather than scientific fidelity.
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Clpps
RhallCip Clips 9,801
12,796
Step? Remaining Clips 26,841
Step1 Remaining Clips 33,535
Expart
Subrilees Filtered 14,647
Original Clips
67,853
Black foeders flteed
6,604
Classriir Filtered 34,318
Figure 3:
Overview our data filtering pipeline, Each stage applies specific filters and shows the volume of data removed and retained.
Visual Quality vs. Scientific Fidelity Table shows that nearly all models achieve high scores in visual quality (80-97), yet their scientific fidelity lags far behind (most open-source models score only 15-43). This result demonstrates that current models often generate videos that "look right" but fail to strictly adhere to physical and biological laws.
Performance Differences Across Hierarchical Tasks Both advanced open-source models (e.g., Wan2.2-T2V-A144 and top commercial models (Sora, Veo3) exhibit lower performance on cellu- lar and subcellular tasks compared organ-level simulations. This may be attributed to the higher requirements for physical and biological consistency in these tasks, as well as the scarcity of mi- croscale training data that can capture complex dynamics.
Scale Effects in Open-Source Models Within the Wan series, increasing model size from 1.3B to 14B mainly improves visual quality, while scientific fidelity shows little significant growth. This suggests that expanding model parameters alone is not sufficient to solve the core scientific fidelity challenges in microscale simulation.
MICROVERSE: TOWARD MICROSCALE SIMULATION VIA A EXPERT-VERS VERIFIED DATASET
The results Micro WorhdBench indicate that current models remain limited in their ability to model microscale mechanism governed by physical and biological principles. Most large-scale video datasets-suue as Intern Vid Wang et al. (2023b). UCF101 Soomro et al. (2012). and Open Vid- IM Nan al. (2024) primarily consist of natural scenes or human activities. offering little rele- vance to microscopic processes. To address this challenge, we propose a new microscale simula- tion models, termed MicroVerse. which explicitly incorporate physical grounding and fine-grained biological dynamics. A key prerequisite for developing such models is the availability of domain- specific data that accurately capture microscopic processes with physical fidelity.
3.1 DATA CONSTRUCTION, MICROSIM-I
Collecting videos from YouTube We used the official YouTube API to search for videos related to microsimulation and filtered them based on the following criteria: (1) resolution at least 720p; and (2) licensed under Creative Commons. These requirements ensure that the collected videos are suitable and freely available for training. In total, we obtained 12,848 relevant videos.
Splitting videos After obtaining the videos, we segmented them into multiple semantically consis- tent and short clips. We used OpenCLIP Ilharco et al. (202I) for video segmentation: whenever the similarity between adjacent frames fell below 0.85, split was made. In total, 67,853 clips were generated. Since not all clips were related to microsimulation,, we trained a classifier based on VideoMAE Tong al. (2022) to filter them. The model achieved accuracy of over 92%, signifi- cantly improving the quality of the dataset. With the help of the classifier. 34.318 clips were filtered out. For details of the clip classification model related to microsimulation. refer the Appendix L.

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Automatic and expert filtering To improve the quality and physical consistency of the clips, first applied OpenCV to detect black borders and used EasyOCR to detect subtitles in order to filter out those affecting semantic representation, retaining 12,194 clips. Experts then reviewed the data, removing meaningless or physically inconsistent clips, resulting in 9,601 clips.
Generating captions We leverage multimodal LLM (GPT-4o) generate detailed captions. Due to context limits, we uniformly sampled frames per clip as visual input. To minimize hallucina- tions, we supply the video title and description.
Prompt MILM Generate Video Caption
The provided images are sampled from video clip (8 evenly spaced frames). This clip is taken from video with the following metadata:
Video Titl: { (Video Title;; Video Description: {Video Description)
clip, together the title and description. please generate clear, detailed, accurate description what shown. Focus on subject, explains the scene and actions, and emphasizes visible details, textures, and fine structures.
Using the visual content
3.2 DATA STATISTICS
3.2.
FUNDAMENTAL ATTRIBUTES
600
Frequency
Frequency
500
(5672)
18.5% (1778)
400
300
22.4% (2148)
100
0
20
30
50
70
Organ Level
Celldirr Level
Suboe Level
Seconds
Words
(a) Distribution of Categories
(b) Distribution Video Duration
(c) Distribution of Caption Length
Figure 4: Distributions of fundamental video attributes in the MicroSim-10K.
MicroSim- 10K is the first large-scale dataset dedicated to microscale simulation, comprising 9,601 high-quality video clips. As shown in Figure 4 all clips have a resolution of least 720p and duration of 5-60 seconds, ensuring that each captures complete and coherent microscopic process. The dataset spans diverse biological mechanisms across organ, cellular, and subcellular levels, of- fering broad coverage of key scenarios. Each clip is paired with a detailed caption generated by multimodal LLM and validated by experts, with an average length of around 150 words, providing precise semantic alignment for model training.
3.2.2 POPULARITY AND RELEVANCE
To capture the educational and communicative value of microscale simulations, MicroSim- 1OK re- tains metadata such as views, likes, and comments. shown in Figure the videos in MicroSim- 10K have been widely viewed, with many reaching hundreds of thousands of views, and they have received substantial likes and comments, reflecting strong popularity and broad accessibility across both scientific and public communities.
3.2.3 REALISM AND DISTRIBUTION
We compare its distribution with real-world microscopy videos using Fréchet Video Distance (FVD). Using the method described in Section 3.1, we collected 377 real biological videos from YouTube and obtained 643 video clips after preprocessing. As shown in Table the FVD between MicroSim- 10K and real biological videos 123.9. This result indicates that our expert-verified MicroSim-10K

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5000
1200
4000
Frequency
Frequency
Frequency
3000
3000
2000
400
1000
1000
0 11lla ell
2000
4000
6000
8000
100 200 300 400 500 600 700
Numbers (million)
Numbers
Numbers
(a) Distribution of Video Views
(b) Distribution of Video Likes
(c) Distribution of Comments
Figure 5: Distributions of video popularity indicators in the MicroSim- 10K.
already lies remarkably close the real microscopy distribution in terms of visual statistics and structural descriptors, effectively bridging the gap between simulated and real experimental data.
Table 3: FVD comparison across models (lower is better). The FVD between MicroSim-10k and real biological vidcos is 123.9, indicating a close distributional alignment.
Data
FVD vs. MicroSim-10K 1 FVD vs. Reo-eel-Bllloica--OOO
MicroSim-10K
0
123.9
643-Rcal Sacaal--oooggca-CCCC
123.9
Commercial Models
Veo3
42.6
118.1
Sora
116.9
136.3
Open-Source Models
Wan2.1-TTV-11.
83.0
158.9
Wan2.2-T12VV5B
77.6
153.3
Wan2.1-T2V-1453
53.3
137.6
Wan2.2-T2V-A14B
65.8
132.2
3.3 TRAINING MICROVERSE
For training, we fine-tune the Wan2.1 model. A text prompt P is encoded sequence: P (Po. PI.....PPl Pm), while the target video V decomposed into T frames. Each frame is mapped into the latent space via a VAE Kingma & Welling (203)) encoder, yielding the sequence: L transformed into embeddings E using CLIP text encoder, and
(lo, .1,....47) IT). The text input the latent sequence I. is processed by a Diffusion Transformer (DiT) Peebles & Xie (2023).
The training objective is to predict the latent representation of the video through a denoising diffu- sion process. At timestep f, the loss function is defined as:
(I) where L, is the noisy latent representation at timestep f, E denotes the injected noise, Eg is the model's noise prediction, is the current diffusion timestep, and E is the text embedding.
c = B co(L,, E|I
During fine-tuning, with probability defined by the 10%, the text conditioning is entirely masked, enabling Classifier-Free Guidance (CFG) Ho & Salimans (2022) training. This mixture of uncondi- tional and conditional training improves the generation quality of the model during inference.
4 EXPERIMENTS
Experiment Settings We train MicroVerse using 8 NVIDIA H200 GPUs, fully fine-tuning all pa- rameters of Wan2.1-T2V-LL 1.3BBB Wan et al. (2025) with a learning rate of le-5 and a batch size of 8. The training process is designed to improve the model's capability to generate microscopic sim- ulation videos conditioned on text prompts. We conducted a comparative with other models on Micro Berhh Additional training details are provided in the Appendix D
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Human Evaluation To evaluate alignment with human preferences, conducted a human study comparing MicroVerss with Sora and Veo3. The evaluation included 60 samples across three levels of microsimulation (20 samples per level), all soureed from the 20 most popular microsimulation videos on YouTube. Model outputs were randomly shuffled, and three evaluators independently selected the preferred result based on instruction fidelity and visual clarity, marked tie. The final results were reported as preference ratios.
4.1 RESULTS OF OUR MICROVERSE
Improvement in Scientific Fidelity Table that MicroVerse achieves a significant improve- ment in Scientific Fidelity, reaching a score of 43.0 and outperforming all open-source models. This enhancement is attributed to the training on the physics- grouuddd MicroSim- 10K dataset, which en- ables the model to better adhere to biological and physical laws. Although there is a slight decrease in Visual Quality (68.5) and Instruction Following (49.3), this does not affect our core objective: advancing scientific fidelity.
Breakthrough in Subcellular-Leve Tasks subcellular- tasks, MicroVerse achieves a score of 53.3, surpassing all open-source models. This demonstrates that dataset enables VerroVerse make notable progress microscale sim- ulation tasks where existing models typically struggle.
According Table
the highly challenging
4.2 ANALYSIS
Scaling Results We identify two main limitations in the performance of the 1.3B model: first, the improvement in scientific fidelity is relatively modest when fine-tuning a small-pan paramerr model; second, there is slight decline in visual quality and instruction-followin capabilities. To address these issues, we scale the model parameters to 14B and employ a mixed-domain training strategy, combining MicroSim-101 10K with an equivalent amount of high-quality general-domain data randomly sampled from Open Vid Nan et al. (2024). As shown in Table 4 this dual scaling of both model capacity and data diversity significantly enhances performance across all dimensions, achieving state-of-the-ar results among open-source models. For more ablation studies on dataset filtering, dataset size, and training recipes, please refer to Appendix B
Table 4: Impact of Data Scale and Mixed-Domale Training on MecroVerse Performance. Bold indicates the best performance. FT indicates fine-tuning.
Data Size
Scientific Fidelity 1 Vismal Quality 1
Instruction Following
Base Model: 1-.3B Bascline
40.3
71.8
50.1
FT on MicroSim-100 -0K
9,601
43.0
68.5
49.3 53.8 (+3.7)
FT on General MicroSim- 10K
19,202
44.1 (+3.8)
74.4 (+2.6)
Base Model: Wan2.1-14B (Full-BlBBB (Full-Blooded Model)
Baseline
42.7
86.0
$3.8
FT MicroSim-10K
9,601
45.4
82.7
51.4
FT General MicroSim- 19,202
48.3 (+5.6)
87.7 (+1.7)
56.9 (+3.1)
Human Evaluation Results Figure shows the results of human evaluation. Compared with Wan2.1-1.3B models, Micro Verse performs excellently in the dimension of Scientific Fidelity. Its outstanding performance Scientific Fidelity further validates the effectiveness of MicroSim-10Kk In addition, the Cohen's Kappa coefficient among the three independent experts was above 0.80, indicating strong interrater agreement and confirming the reliability of the scoring process. More details on Cohen's 's Kappa coefficient can be found in Appendix G
Consistency among Judgers in Becc To ensure that MicroWorldBench's evaluation aligns closely with human judgment across all dimensions, we conducted human preference labeling on large set of generated videos. Specifically, we computed the consistency of evaluation tasks across different models well between the models and humans. Figure 6 shows the consistency relationships among different models and between the models and humans. For more analysis results on evaluation consistency, please refer to Appendix C

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100
Human
Gomin1
80
Score
60
Score
40
Quality
20
Inlteciie Pedleniaa
0
Scienlific
Visual Dimension
Following
Scientific
Visual
Following
Dimension
(a) Human Evaluation Result of (b) Consistency between LLMs and
(c)
Consistency across
different
MicroVerse and Wan2.1-1.3B
Humans
LLMs
Figure 6: Human Evaluation and Consistency Results.
5 RELATED WORK
World Model World models LeCun (2022); Bruce et al. (2024): Lu et al. (2024) have garnered significant attention. They simulate dynamic environments by predicting future states and estimat- ing rewards based on current observations and actions. Their ability to model state transitions has been extended to real-world scenarios through joint learning of policies and world models, improv- ing sample efficiency in simulated robotics (Sco et al. (2023), real-world robots Wu al. (2022). clinical decision Yang et al. 2025), and autonomous driving Wang et al. (202aa). For example, some work Du et al. 2023) explores long-horizon video planning by combining vision-language and text-to-vides models. Others Luo & Du (2024) focus on linking video models to continuous actions through coal-conditioned exploration. Recent works Lu al. (2024) also use video genera- tive models to let agents explore environments more effectively. MeW Yang et al. (2025) applies world modeling to medical image analysis and clinical decision decision-making.
Video Generation Video generation has seen rapid progress in the past two years. The release of Sora [OpenA] (2024) has ignited strong research interest in text-to-video generation, leading to breakthroughs in quality, coherence, and controllability Blattmann et al. 2023). Other commercial systems such Veo3, Kling. Hunyuan Video Kong al. (2024), and Hailuo HalloAAl (2024) have achieved impressive performance and are widely applied in video production, ad advertising, and edu- cation. With the technology maturing, comainn-specife models are emerging to address specialized needs. For instance, MedGen Wang et al. (2025) generates accurate, high-quality medical videos for health education, while AniSora Jiang et al. (2025) focuses on producing detailed and stylistically rich animated content. Despite these advances, the use of video generation for microscale simulation remains largely unexplored.
Rubric Evaluation Rubric-based evaluation has become standard approach for assessing LLMs on open-ended tasks, offering task- speciffc and interpretable criteria that improve grading consis- tency. HealthBench Arora al. (2025) scales this paradigm to 5,000 multi-turn conversations with 48k clinician- rubrics covering accuracy, safety, and communication. Building on this, Baichuan-M2 [Tam (2025) dynamically generates case-specific rubrics verifiable reward signals for reinforcement learning. enabling adaptive and context-aware supervision. Rubrics as Rewards (RaR) Gunjal al. (2025) further formalizes rubric-based RL and shows significant gains over Likert-style scoring. These efforts highlight rubric-guided evaluation and training as promising methodology for developing reliable, aligned, and LLMs.
6 CONCLUSIONS
Video generation excel at natural and human-centered macroscopic scenes but fail to capture faithful microscale dynamics. This work introduces Micro WorkdBech,, the first rubric-based benchmark for microscale video generation with 459 expert-curated tasks and well-defined rubric criteria. In addi- tion, we build MicroSim-100 10K and develop Micro Verse which demonstrate remarkable performance on microscale simulation tasks. By integrating physical constraints and expert supervision. Micro- Verse not only improves visual fidelity but also advances toward biologically meaningful dynamics, enabling applications in biomedical research, education, and interactive scientific visualization.
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LIMITATION
Our work aims to explore the potential of educational microscale simulations of biological mecha- nisms, rather than the reproduction of results observed in wet lab experiments. However, our current approach does not explicitly incorporate the underlying physical laws that govern biomedical mi- croscale dynamics, such as fluid mechanics in blood flow, diffusion-reaction equations molecular transport, or biomechanical constraints in cellular processes. This limitation restricts the applicabil- ity of the model in scenarios that require high-precision scientific simulation and prediction.
ETHICS STATEMENT
All data are publicly available, compliant with YouTube's terms, and we exclude personal/sensstive content. Captions were auto-generated (MLLMs) and manually verified to remove inappropriate/i dentifiable material. The dataset is intended solely and strictly for research purposes and should not be used for non-research settings. We do not own the copyright of these data and will only publicly release the URLs linked to the data instead of the raw data.
ACKNOWLEDGEMENTS
This work was supported by Major Frontier Exploration Program (Grant No. C10120250085) from the Shenzhen Medical Academy of Research and Translation (SMART), Shen- zhen Medical Research Fund (B2503005), the Shenzhen Science and Technology Pro- gram (JCYJ202208 1003300002,) NSFC grant 72495131, Shenzhen Doctoral Startup Funding Tianyuan Fund for Mathematics of National Natural Science Foun- dation of China (NSFC) (12326608), Shenzhen Science and Technology Program (Shenzhen Key Laboratory Grant No. ZDSY S22330622600 1302006)) the 1+1+1 CUHK-CCUIKSS))-GDSTC Joint Collaboration Fund, Guangdong Provincial Key Laboratory of Mathematical Foundations for Artifi- cial Intelligence (2023B 12122222 122000001), the International Science and Technology Cooperation Cen- ter, Ministry of Science and Technology of China (under grant 2024YFE0203000), and Shenzhen Stability Science Program 2023.
IRCBS2001008899330666
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A DATA FILTERING PIPELINE
As illustrated in Figure 7 our data construction process is designed to ensure high fidelity and se- mantic richness. We start with initial collection of 128K microsimulation videos. To guarantee data quality, we implement a rigorous cleaning protocol that filters out non-microsimulation sam- ples and eliminates temporal inconsistencies. Furthermore, utilize OpenCV crop black borders and employ EasyOCR to detect and remove hard-coded subtitles, thereby reducing visual noise. The preprocessed videos undergo human- cumaa--in-the-ii in-the-loop verification stage, where experts assess the con- tent for meaningfulness and consistency. In the final stage, we leverage Multimodal Large Language Models (MILMs) to automatically generate descriptive captions, resulting high-quality dataset suitable for downstream representation learning tasks.
Remove Non cotn-ccccreiimlaiioon
Removv Black
OpenCV
Gienerate
128K
Experts
MLLMS
Captions
Microsimulation Videos
Temporal
EasyDCR
Inconsistencies
Remove Subtitles
Figure 7: Overview of the data construction process.
To ensure absolute data purity and avoid potential data leakage, implemented rigorous three- level deduplication pipeline. As shown in Table 5 we found zero overlap at high similarity thresh- olds across source, text, and vision levels.
Table 5: Three-level deduplication pipeline results.
Deduplication Type
Method
Metric
Result
Source-level
Video matching
overlapping IDs
Text-level
Caption embedding
# pairs > 0.90 # pairs > 0.95
0
Vision-level
Frame embedding
0
Furthermore, we lowered the text-level and vision-level thresholds to 0.60 to help remove poten- tially overlapping tasks and ensure absolute data purity. Table 6 shows the task distribution after deduplication.
Table 6: Task distribution after deduplication.
Biological Level
Original
Post-Deduplication
Removed
Organ-level 238
233
-5
Collular-Ievel
189
182
-7
Subcellular-level
32
30
-2
Total
459
445
-14
We re-evaluated the model performance on the clean test set (445 tasks), as shown Table The performance scores remained highly stable, with only negligible variations (decimal-level drops), therefore we ultimately adopted the 459 tasks.
Regarding private models like Veo3, we acknowledge they may have seen YouTube data. However, Micro BocldWhord evaluates scientific fidelity, not just generalization. Even a model has seen similar data, generating simulation that strictly adheres to expert-weighted physical rubrics mains a valid measure of capability. We plan to expand the benchmark with specialized microscopy databases in the future broaden the domain distribution.
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Table 7: Performance comparison on original vs. clean test set.
Test Set
Average
Scientific Fidelity
Visual Quality
Instruction Following
Original (459 tasks)
50.2
43.0
68.5
49.3
Clean (445 tasks)
50.0
42.7
68.5
49.1
B ABLATION STUDY
In this section, we present series of ablation studies to evaluate the impact of different components and hyperparameters on the model's performance.
Ablation Study on dataset filtering. Filtered data (MicroSim-10K) yields more balanced and reliable improvements than using raw, uncleaned data, boosting scientific fidelity while avoiding major drops in visual quality and instruction following. Table summarizes the results.
Table Ablation study on dataset filtering.
Variant
Data Size
Sci. Fidelity
Vis. Quality
Inst. Following
Baseline Model (Wan2.1-1.31) FT on raw (uncleaned) data FT on MicroSim-10K
0
40.3
71.8
50.1
34,318
42.1
67.4
44.5
9,601
43.0
68.5
49.3
Ablation Study on dataset size. Increasing the size of high-quality training data yields steady gains in scientific fidelity and instruction following with only minor visual-quality tradeoffs. Table summarizes the results.
Table 9: Ablation study on dataset size.
Variant
Data Size
Sci. Fidelity
Vis. Quality
Inst. Following
Baseline Model (Wan2.1-1. FT on MicroSim-100 (50% sample) FT on MicroSim-100
40.3
71.8
50.1
4,800
41.9
69.1
48.4
9,601
43.0
68.5
49.3
Ablation Study on training recipes (CFG rate). Excessive CFG sharply degrades scientific fi- delity and visual quality. Table summarizes the results.
Ablation Study on training recipes (training steps). As shown in the training curves, the model converges rapidly and stabilizes around 5,000 steps. Therefore, we adopt this number of training steps as the default setting for reporting efficiency.
Ablation Study on training recipes (number of frames). We selected 81 frames as balanced trade-off:
It provides sufficient temporal duration to capture complete distinct biological events (c.g., cell division) while maintaining high temporal coherence and manageable computational costs.
Additionally, 81 frames is native, optimal temporal window for the Wan2.1 architec- ture Wan et al. (002)).
C ANALYSIS OF THE PROCESS OF EXPERT REVISION AND VALIDATION
All experts involved in our evaluation hold doctoral degrees (Ph.D.) and possess extensive research experience in cellular and molecular biology. Table [] provides the background information for each expert.
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Table 10:
Ablation study on CFG rate.
Variant
Data Size
Sci. Fidelity
Vis. Quality
Inst. Following
Baseline Model (Wan2.1-1.3B)
40.3
71.8
50.1
FT on MicroSim- 10K (CFG=5%)
9,601
41.5
70.2
49.8
FT on MicroSim- (CFG=10%)
9,601
43.0
68.5
49.3
FT on MicroSim-100 (CFG=15%)
9,601
38.2
63.1
51.4
Table 11: Background information of the experts involved in the evaluation.
Expert ID Field of Expertise
Years of Experience
Cell Biology/Genetics
12 years
2
Biochemistry Mctabolic Pathway Modeling
10 years years
3
Biochemistry Metabolic Pathway Modeling
We analyzed the frequency with which three experts employed the four types of rubric operations when handling different tasks. As shown in Figure all experts tended favor Adjust Weights, while Supplement was used relatively infrequently. Follow-up interviews with the three experts revealed that the Supplement operation is more cumbersome, it requires identifying additional evaluation criteria beyond those automatically generated by the LLM, which can introduce extra burden.
As detailed in Table 12. experts performed three specific types of interventions to ensure biological and physical processes were captured: (1) Filtering scientifically trivial criteria; (2) Adjusting weights (e.g., setting
1.0) to prioritize underlying scientific mechanisms over superficial visual quality; and (3) Supplementing missing physical constraints. As shown Table [2] only about 7.6% of the 459 tasks required experts to add missing scientific criteria. This indicates that GPT-5's initial rubric drafts already captured the essential mechanisms, while experts played a crucial role in refining the final outputs.
Furthermore, Table 13 shows that experts intervened more frequently in subcellular tasks to enforce strict physical constraints where GPT-5 is less reliable, ensuring scientific accuracy.
Furthermore, we conducted a correlation analysis between human experts and LLMs to validate the reliability of automated evaluation. Three domain experts independently evaluated subset of videos, and we quantified the scoring agreement between human-only panel and a panel including slightly improves the
GPT-5. As shown in Table 14] the inclusion of GPT-5 maintains inter-rater agreement (Fleiss' Kappa).
We also report the agreement across different biological scales in Table 15 Overall, GPT-5 shows agreement with expert scoring at a level comparable to human evaluators, and in some cases its consistency even slightly higher. This suggests that GPT-5 reliable evaluator rather than source of additional variance.
300
300
300
Trequency
250
Hequency
250
250
200
200
150
150
150
50
50
50
Adjust Weight
Detete and Fiter
Aztio
Supplement
Adlutt Weight
Delete and Fiter
No Artion
Susplement
Adjust Weights
Deleka and Piter
Axten
Susplamare
Actions
Actions
Actions
(a) Expert
(b) Expert
(c) Expert 3
Figure 8: Actions Log of Three Independent Experts.
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Table 12: Detailed expert actions during the rubric revision process.
Action
Expert
Expert 2
Expert 3
Adjusting weights
180
221
189
Filtering
137
128
146
No Action
94
82
95
Supplementing
48
28
29
Table 13: Expert intervention frequency across different biological scales.
Biological Level Number of Tasks
Total Expert Actions
Avg. Actions per Task
Organ-level 238
361
1.52
Cellular-level 189
567
3.00
Subcellular-Ievel 32
176
5.50
D TRAINING SETTINGS ON MICROV ERSE
We implemented our training pipeline utilizing a high- serhforrformacc computational node equipped with 8 NVIDIA H200 GPUs. To ensure the model fully captures the intricate dynamics and visual nuances specific the microscopic domain, we adopted full parameter fine-tuning strategy on the pre-trained Wan2.1-T2V-1.33 model.
We utilized a global batch size of 8 and set the learning rate to x 10-5 with a consistent weight decay of 0.01 to prevent overfitting. To maximize computational efficiency and memory utilization without compromising numerical precision, we employed bfloat16 (bf16) mixed-pree sexeedprecision training. Furthermore, full gradient checkpointing was enabled to significantly reduce the memory footprint during the backpropagation phase.
For the visual data configuration, the model was trained to generate high-fidelity video quences with a resolution of 480 x 832 pixels and a temporal duration of 81 frames. A training ControlNerrClaassffer--ii Guidance (CFG) rate of 0.1 was applied to randomly drop text condition- ing, thereby reinforcing the model's capability for unconditional generation and improving overall robustness. Detailed training configurations enumerated in Table 16
E INFERENCE SETTINGS ON MICROVERSS ERRS
To ensure a rigorous and unbiased assessment of generation quality, we standardized the inference protocol across all evaluated models. The inference configuration strictly mirrors the training reso- lution settings (480 832, 81 frames) avoid potential domain shifts during the generation phase.
We employed standard sampling strategy with 50 inference steps, striking an optimal balance between generation latency and visual fidelity. A guidance scale of 5.0 was selected ensure strong adherence to the text prompts while maintaining natural visual diversity and avoiding artifacts associated with excessive guidance.
Crucially, to uphold the integrity of our evaluation benchmark, we enforced strict single-shot in- ference policy. For each test prompt, only a single video sample was generated using a fixed random seed. This approach eliminates the possibility of sianry-ppcciig-ssleettii multiple attempts- -ttessbythyyyyyee providing an honest reflection of the model's 's stability and generalized performance. The comprehensive parameter settings for inference are listed in Table I7
the best output from
Table 14: Scoring agreement between human experts and LLMs.
Evaluation Group
Agreement Score
Human-only (Fleiss' Kappa)
0.733
Human GPT-5 (Fleiss' Kappa)
0.771
GPT-5 vs Gemini-2.5-pmm (Spearman)
0.835
GPT-5 vs GPT-5 (Spearman)
0.912
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Table 15: Agreement across biological scales.
Level
Agreement (Human-only)
Agreement (Human GPT-5)
Organ-level
0.728
0.742
Cellular-level
0.740
0.739
Subcellular-level
0.691
0.727
Table 16: Detailed hyperparameters used during the training phase of MicroVerss
Parameter
Value
--train batch size
--max train steps
5000
--learning rate
le-5
medxxee_pecise -trrining.cfg rate
bf16
0.1
-num.height
480
-numwwidth
832
-num_frames
81
weigght.eecat
0.01
--dit -di..precision
fp32
full
Table 17: Standardized inference parameter settings for model evaluation.
Parameter
Value
--height
480
--width
832
--nun_frames
81
--quidance.scale
5.0
-mm..inference.stes
50
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F PROMPT GPT-5 TO GENERATE RUBRIC CRITERIA
Prompt GPT-5 to Generate Rubric Criteria
Task:
You are biology expert. Your task to design a set of rubrics to evaluate completion of given task based on the provided Prompt. The rubric should consist of multiple triplets in the form:
{a,, de, S4, we}
a,: the evaluation aspect, restricted to one of the following three categories:
Scientific Fidelity: Accurate representation organs, cells, and subcellular structures in scale, morphology, and spatial relationships, with dynamic processes consistent with biological and physical laws.
Visual Quality: Emphasis on clarity, detail, and aesthetics, including model precision, rendering, lighting, and color balance.
Instruction Following: Generated vidcos strictly follow the prompt description.
d; description of the i-th evaluation criterion.
8; polarity of the criterion, either +1 (contrihutes positively) or -1 (deducts points); leave this field empty.
ue; weight importance the range (0, 1]:
11. core scientific requirements
0.5 + important but secondary requirements
0.2 + auxiliary or presentational requirements
Output example:
"al": "Scientific Fidelity",
"di": "Key cell structures clearly defined and proportionally accurate", "si": "+1",
"wi": "1.0"
Constraint:
1. Do not give any explanation, output directly.
2. Please describe the evaluation criterion (d:) in as much detail as possible.
3. Directly describe the key rubrics in the evaluation criterion, and do not use words such as 'whether'.
4. Only English output is allowed.
Given prompt:
(prompt)
G INTER-RATER RATER RELIABILITY AMONG HUMAN EVALUATORS
We used Cohen's Kappa coefficient to measure agreement among the three experts. Table 18 indicate strong agreement among the experts, confirming reliability of the scoring process.
Table 18: Comparison of Cohen's 's Kappa values between experts.
Comparison Pair
Cohen's Kappa
Expert1 vs. Expert2
0.87
Expertl vs. Expert3
0.83
Expert2 vs. Expert3
0.81
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H A RUBRIC EXAMPLE FROM MICROWORLDBENCH
0:00
0:04
0:24
Prophase
Prometaphase
Telophase
Figure 9: Example of cell mitosis simulation video frame, taken from excellent example video on YouTube.
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Table 19: Rubric Example for Cell Mitosis Evaluation
Dimension
Criteria
Weight
The sequence of stages, segregation patterns. and ploidy changes in mitosis and meiosis are accurately represented: mitosis produces two genetically identical diploid daughter cells, whereas meiosis involves two successive divisions resulting in four genetically diverse haploid gametes.
1.0
The alignment of chromosomes the metaphase plate and their segregation from metaphase anaphase occur correctly; sister chromatids are distinctly differentiated from homologous chromosomes; the attachment of kinetochores lo spindle microtubules and the direction of their lension conform to biological principles.
1.0
Scientific Fidelity
Structural details dynamic coordination between spindle apparatus and the centrosome (centriole) are accurate; spindle pole positioning, microtubule polarity, and force distribution are appropriate; the relationship between the pacrotubulc-organizze center and cell polarity is correctly established.
1.0
The timing and mechanisms of DNA replication and genetic recombination are correctly presented; DNA replication occurs during the pre-mitotic S phase, homologous pairing and crossing over take place in prophase I of meiosis, and DNA replication meiosis and
+ 1.0
between
The image demonstrates high clarity and fine presentation of microstructural details, with sharp edges of subcellular structures, well-defined layer separation, and absence of artifact noise.
+ 0.5
The animation exhibits coherent motion with stable temporal rhythm, smooth phase transitions, and natural movement trajectories, without any stuttering or tearing.
0.5
Visual Quality
The 3D modeling and material texture credible, with consistent form proportions and scale hierarchy; the textures are detailed, and surface microstructures are discernible.
0.2
Coordination of lighting, shadows, and depth of field; controlled volumetric scattering and highlights without excess; clear subject contours with well-defined micro-scale detailing.
Accurately present the key stages of mitosis in single somatic cell, ensuring clear transition and coherent progression between meiotic divisions and II in gonadal germ cells.
+ 0.5
Accurate reproduction of subcellular elements dynamics: chromosome separation following metaphase plate alignment, coordinated movement of spindle fibers and centrioles, and continuous changes and details of the cell membrane/cytokinesi.
0.5
Accurately describe genetic outcomes and differences: mitosis produces two genetically identical diploid daughter cells, while meiosis results in four genetically diverse haploid gametes, highlighting the mechanistic distinctions.
0.2
Instruction Following
Compliance with technical specifications and viewing angle requirements: within an approximate total duration 5 seconds, information organized clearly; microscopic close-up focuses on the single-cell subject; the camera remains stable, transitions are clear, and the subject is unobstructed.
0.2
Presence of fundamental conceptual and procedural between mitosis meiosis, incorrect sequencing of stages, inaccurate ploidy descriptions, omission of the two meiotic divisions, or failure to represent the single-cell focus.
confusion
0..
21

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Published as a conference paper at ICLR 2026
I EXAMPLE OF REAL BIOLOGICAL VIDEO CLIPS
rightly packed layer rounded, polygonal cells finely granular cytoplasm and dark, well-defined nuclei, their thin bright borders fomning mossic-like like tissue texture. At the center, prominent cell is caught the midst of mitosis, distinguished by its luminous, ring-shaped outline and mucleus that appears clongated or partially split as condensed align and sepurate visual nuclei with small dark spots suggestive of macleoli, providing contast to the dynamic strictural rearrangements within the dividing cell. The overall captures the subcle shifts division.
Caption:
high-magnifiestion microsoapic
chromatin
active division stage. Surounding cells remain interphase, showing intact round
contast, and intmcellular organization characteristic of living cells undergoing cell
Figure 10: Example of real biological video clips.
J GENERATE PROMPT FROM THE VIDEO TITLE AND DESCRIPTION
Generate Prompt from the Video Title and Description.
Your task: Based the "video title" and "video description" provide, craft extremely detailed, professional, and keyword rich English prompt specifically for generating breathtaking microscopic- world videos. not give any explanation, output directly. Don't use bullet points. Write as single, complete paragraph.
Generate complete, ready-to-use viden-generation prompt. This prompt must include all of the following sections:
1. Main Subject: Clearly describe the central object within the microscopic seene.
2. Scene & Action: Describe what happening.
3. Details & Textures: Emphasize the details that should be visible.
The video title is:
(video_title)
The video description is:
piieo_description)
K PROMPT LLM CLASSIFIES BASED ON TASK DESCRIPTIONS
Prompt LLM Classifies Based Task Descriptions.
Your task: are an expert scientific video classification. Given a task description, classify into one of the following categories for diversity:
Organ-level simulations tasks focusing on behavior, dynamics, or interactions at scale of whole organs or organ systems.
2. Cellular-level simulations tasks focusing the behaviors and interactions of single cells or collections of cells, such
division, cell fusion, cell migration, signaling.
3. Subcellular-level simulations tasks focusing on molecular, genetic, or biochemical pro- cesses within cells, such as protein folding, gene regulation, intracellular signaling.
The task description is:
(Task. Descriptpn)))
Please output only most appropriate category label based on the task description provided.
22

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Published as a conference paper at ICLR 2026
L VIDEOMAE-BASED MICROSIMULATION CLASSIFIER
To filter out video clips related to microsimulation, we trained a classifier using 2,580 manually annotated samples based the VideoMAE model. The training was implemented within the Trans- formers, with a learning rate of 5e-5 and a total of 10 epochs, enabling the model to effectively capture video features and achieve accurate classification. Finally, our classifier achieved an accu- racy of 92% on the test set.
Table 20: Dataset statistics for microsimulation classification.
Category
Total
Train (80%)
Test (20%)
Microsimulltioon-rlatted
1107
885
222
Non-mierrosimulation
1473
1178
295
Total
2580
2063
517
23


Reviews:
[{"review_id": "bGXTU958Wa", "reviewer": "Reviewer_Snf5", "summary": "This paper introduces MicroWorldBench, a rubric-based benchmark designed to evaluate microscale simulation tasks across multiple biological and physical scales, including organ-level dynamics, cellular processes, and molecular interactions. The benchmark includes 459 expert-annotated evaluation criteria covering dimensions such as scientific accuracy, temporal consistency, visual quality, and instruction following. The authors show that current state-of-the-art video generation models perform poorly on these tasks, exhibiting physical law violations and biologically inaccurate behavior. To address this gap, they create MicroSim10K, an expert-verified dataset of physically grounded microscale simulations, and train MicroVerse, a video generation model specialized for this domain. MicroVerse demonstrates improved fidelity in reproducing complex microscale phenomena. Overall, the work introduces the concept of \"micro-world simulation\" and presents an initial benchmark, dataset, and model aimed at advancing scientific and biological video simulation capabilities.", "questions": "Please address this main concern above ^", "limitations": "", "rating": null, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "This work creates a curated microscale simulation dataset (MicroSim-10K) with expert verification, addressing lack of domain-specific training data.\n\nPresents a fine-tuned model (MicroVerse) that improves fidelity and consistency on the proposed benchmark, showing feasibility of domain adaptation.", "weaknesses": "By looking at Figure 1, my primary concern is that the simulations themselves do not appear realistic (as these simulations look they were made for educational purposes). In some cases, the model-generated outputs look more realistic than the ground-truth simulations (e.g., the SORA cell-division example). This raises questions about whether the benchmark’s “expert-verified” reference simulations accurately reflect real **biological phenomena** and whether the evaluation metric truly measures **biological fidelity** rather than stylistic similarity (because again, these are not realistic life cell imaging videos, rather educational style videos).\n\nI believe this work could still be framed as generating educational biological simulations or illustrative biology videos, but claiming true biological fidelity is not supported by the examples shown (anyone with basic wetlab experience can discern that these videos are not realistic). The current outputs do not convincingly reflect real biological processes, making it difficult to justify the biological realism claims.\n\nGiven these facts  (and that I don't think it is possible to make a single claim of biological fidelity) I recommend either expanding this dataset to include real biological videos or restructuring the whole paper.", "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": "iC3bpID31l", "reviewer": "Reviewer_qfnB", "summary": "This paper introduces MicroVerse, a preliminary exploration toward micro-world simulation, aiming to extend video generation models to capture biologically and physically meaningful microscale dynamics (e.g., organ-, cellular-, and subcellular-level processes). The authors propose MicroWorldBench, a rubric-based benchmark with 459 expert-annotated tasks for evaluating microscale simulations, and build MicroSim-10K, a large-scale dataset of 9.6K physics-grounded video clips. Based on this dataset, they fine-tune Wan2.1 to obtain MicroVerse, which reportedly improves scientific fidelity compared to existing open-source video generation models. The paper highlights a new research direction and provides valuable resources for future study.", "questions": "- How scalable is the rubric-based evaluation if new tasks or domains are added? Would each require renewed expert verification?\n- Is there any plan to integrate explicit physical priors (e.g., fluid or diffusion equations) to improve scientific fidelity?\n- How consistent are GPT-5 rubric scores compared to human experts across diverse biological processes?\n- Can the authors clarify how much of the performance improvement is due to dataset quality versus model fine-tuning?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper opens an underexplored and impactful direction — using video generation models for microscale (biomedical) simulation, which connects generative AI with scientific and educational applications.\n\n- MicroWorldBench and MicroSim-10K are well-constructed and carefully validated, providing a foundation for future research in scientific video generation.\n\n-  The rubric-based evaluation (Scientific Fidelity, Visual Quality, Instruction Following) combined with expert refinement offers a transparent and interpretable assessment framework.\n\n-  The paper is well-written and conceptually coherent, linking macroscopic “world models” with microscopic phenomena convincingly.", "weaknesses": "- MicroVerse is essentially a fine-tuned version of Wan2.1 without introducing new architectural or physical modeling components. The improvement in scientific fidelity (+0.8 overall) is marginal.\n\n- While the benchmark and dataset are significant contributions, the proposed model does not clearly outperform commercial systems or strong open-source baselines, suggesting that the key contribution lies in data curation rather than modeling innovation.\n\n- The scoring process relies heavily on GPT-5 grading, which may introduce subjectivity or bias. The human validation is limited in scale and lacks quantitative rigor.\n\n- Despite the “physics-grounded” claim, no explicit physical constraints or differentiable physical priors are used in the model training, which limits its scientific credibility.", "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": "12pQlfvlCb", "reviewer": "Reviewer_9ESj", "summary": "The paper introduces a video generation benchmark, MicroWorldBench, and a video generation model, MicroVerse, to explore how well video generative models perform on microscale simulation tasks. MicroWorldBench is a rubric-based benchmark with 459 tasks spanning organ, cellular, and subcellular levels. The authors also construct MicroSim-10K, a dataset of 9,601 expert-verified microscale simulation videos, and fine-tune Wan2.1 on MicroSim-10K to obtain the model MicroVerse. MicroVerse shows some improvement in scientific fidelity over the base model, though with decreased visual quality and instruction-following capabilities.", "questions": "See weaknesses.\n\nTypos: Figure 1 \"Mircoscale\" -> \"Microscale\"; Figure 3 \"Boraders\" -> \"Borders\".", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Novel application domain. Applying video generation to microscale simulation is unexplored and potentially impactful for education and biomedical research.\n\n- Comprehensive benchmark design. The rubric-based evaluation with expert-curated criteria across three hierarchical levels (organ/cellular/subcellular) is well-motivated and systematic.\n\n- Dataset construction effort. Building MicroSim-10K with multiple filtering stages and expert verification demonstrates thoroughness in data curation.", "weaknesses": "- Single-domain dataset that may not represent the full distribution and may have data contamination issues. Both the training set (MicroSim-10K) and test set (MicroWorldBench) are built entirely from YouTube videos, which may not reflect the full range of scientific simulation requirements. The authors have not reported how they deduplicate to ensure the test set is fully separate from the training set. Additionally, there is a concern that private models (like Veo3) may have already been trained on all YouTube videos.\n\n- Evaluation metrics may not capture the biological/physical processes. For the test set, MicroWorldBench, the rubrics were first generated by GPT-5 and then verified by humans. While these human-in-the-loop metrics might be useful, GPT-5 is unlikely to capture the biological/physical processes in the video, and thus important metrics related to these are likely missing.\n\n- Missing ablations. No ablation studies on how dataset filtering stages, dataset size, or training recipes (CFG rate, steps, frames) affect the final performance.", "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)