IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?
Authors: Yang Chen
Minghao Liu
Yufan Shen
Yunwen Li
Tianyuan Huang
Xinyu Fang
Tianyu Zheng
Wenxuan Huang
Cheng Yang
Licheng Wen
Xuemeng Yang
Daocheng Fu
Jianbiao Mei
Rong Wu
Song Mao
Qunshu Lin
Zhi Yu
Yongliang Shen
Yu Qiao
Botian Shi
Venue: ICLR 2026 Poster (2026)
Keywords: benchmark
mllm
web
Primary area: datasets and benchmarks

Abstract: The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code.
However, existing benchmarks primarily focus on static screenshot-to-code tasks, thereby overlooking the dynamic interactions fundamental to real-world web applications.
To address this limitation, this paper introduces IWR-Bench, a novel benchmark for evaluating the capabilities of Large Vision-Language Models (LVLMs) in interactive webpage reconstruction from video.
IWR-Bench comprises 113 meticulously curated tasks from 100 real-world websites, with 1,001 actions and featuring diverse interaction complexities (e.g., web games), visual styles, and domains.
Aligning with standard web development practices, each task includes not only user interaction videos but also all crawled static assets (e.g., images, videos).
This benchmark evaluates models on two fundamental challenges: comprehensive multi-modal reasoning to infer interaction logic from video and assets, and advanced code generation to translate this logic into functional code.
An agent-as-a-judge framework with a comprehensive metric system automatically assesses the functional correctness and visual fidelity of generated webpages.
Extensive experiments on 28 LVLMs reveal a significant challenge: the best model achieves an overall score of only 36.35\%, as functional correctness (24.39\% IFS) lags significantly behind visual fidelity (64.25\% VFS).
These results highlight critical limitations in current models' ability to reason about temporal dynamics and synthesize event-driven logic, establishing IWR-Bench as a challenging frontier for vision-language research.
The benchmark and evaluation code will be made publicly available.

Published as a conference paper at ICLR 2026
IWR-BENCH: CAN LVLMS RECONSTRUCT INTER- ACTIVE WEBPAGE FROM A USER INTERACTION VIDEO?
Yang Chen .1.2, Minghao Liu 3.3.4 Liu*.3.4 Yufan Shen Yunwen Li Tianyuan Huang
Xinyu Fang -1,2 Tianyu Zheng'. Wenxuan Huang Cheng Yang', Daocheng Fu!-7,
Jianbiao Meil.2, Rong Wul.2, Licheng Wen', Xuemeng Yang'. Song Mao', Qunshu Lin
Zhi Yu², Yongliang Shen², Yu Qiao', Botian Shi
Shanghai Artificial Intelligence Laboratory, *Zhejiang University, 2007AAI, MM-A-P, CChinese University of Hong Kong (Shenzhen), "Chinese University of Hong Kong,
Fudan University, *Shanghai Innovation Institute sh gma il........ com, shibot shibotian@p anê jlab. org.cn
ABSTRACT
The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code. However, existing benchmarks pri- marily focus on static screenshot-to-coes tasks, thereby overlooking the dynamic interactions fundamental to real-world web applications. To address this limi- tation, this paper introduces WWR-Bench, a novel benchmark for evaluating the capabilities of Large Vision-Language Models (LVLMs) in interactive webpage reconstruction from video. (WR-Bench comprises 1 113 meticulously curated tasks from 100 real-world websites, with 1,001 actions and featuring diverse interac- tion complexities (e.g., web games), visual styles, and domains. Aligning with standard web development practices, each task includes not only user interaction videos but also all crawled static assets (e.g., images, videos). This benchmark evaluates models on two fundamental challenges: comprehensive multi-modal reasoning to infer interaction logic from video and assets, and advanced code generation to translate this logic into functional code. An agent-as-a-judge frame- work with comprehensive metric system automatically assesses the functional correctness and visual fidelity of generated webpages. Extensive experiments on 28 LVLMs reveal a significant challenge: the best model achieves overall score of only 36.35%, as functional correctness (24.39% IFS) lags significantly behind visual fidelity (64.25% VFS). These results highlight critical limitations in current models' ability to reason about temporal dynamics and synthesize event-driven logic, establishing IWR-Bench as a challenging frontier for vision-language re- search. The benchmark and evaluation code would be made publicly available.
INTRODUCTION
Recent advances in Large Vision-Languann Models (LVLMs) have unlocked remarkable capabilities
visual understanding and code generation (OpenAll 2025 Comanici et al. 2025 Bai et al., 2025).
State-of-the models can translate static screenshot of a webpage into corresponding HTML
with impressive fidelity (Yun et al. 2024] Gui et al. 2025). This nascent success, however, highlights fundamental limitation of current evaluation methodologies. Existing benchmarks either
fined to static reconstruction (e.g.. Dose (Si et al. 2024), WebSight (Laurençon et al.) (024)) model interactions as single-step. stateless events from image pairs (e.g., Interaction2Code (Xiao et al. 2025)), while also failing to provide the necessary static assets for reconstruction. This simpli- fied setup falls short of capturing the continuous, stateful workflows and complete resource context web applications. The disconnect between demonstrated capabilities and the demands of true interactivity motivates our central research question: Can LVLMs reconstruct the dynamic, interactive functionalities of webpage from observing user interaction video?
characteristic of
Equal contribution, Corresponding authors.

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Published as a conference paper at ICLR 2026
Reconstructing interactive webpage from video poses two fundamental challenges. The first, comprehensive multi-modal perception and reasoning Luo et al., 2024 Gupta & Kembhavi, 2023 Song et al., [2025] Deka et al., 2017; Lee et al. 2023), the process of inferring latent interac- tion logic from dynamic visual evidence. This requires model to ground its temporal understanding of observed interactions in a precise visual comprehension of the resultant UI states. critical facet of this reasoning is robust image matching to associate dynamic elements with their static asset counterparts. The second challenge, advanced code generation (Jimenez et al., 2024 Xiao al. 2025 Li et al. 2022), is the translation of this inferred logic into functional code that implements the complex, stateful logic of interactive applications (e.g., web-games like 2048 and Minesweeper).
The construction of comprchensive benchmark for interactive webpage reconstruction confronts three pivotal challenges. The first pertains to ensuring Diverse Interaction Coverage, which ne- cessitates the curation of tasks spanning broad spectrum of interaction paradigms and visual com- plexities, while simultaneously adhering to strict standardization for reproducible evaluation. The second challenge centers the establishment of an Authentic Task Environment. Departing from prior benchmarks characterized by incomplete setups or placeholder assets (Jiang et al., 2025 Gui et al. 2025), this requires the meticulous curation of complete set of authentic resources from live websites. Such resources must encompass both static assets, such as images and icons, and dynamic content, such embedded videos, faithfully represent real-world development contexts. The final challenge lies in the formulation of Robust Automated Evaluation protocol. Conventional met- rics, including pixel-wise similarity, are insufficient for this purpose (Zhang al. 20188 Caron al.) 2021 Radford et al. (202)), as they cannot appraise functioonaityy. An effective protocol must there- fore employ programmatic interaction with the generated webpage ascertain both the functional integrity of its components and the state-wisg wise visual consistency across dynamic transitions.
This paper formalizes the task of Interactive Webpage Reconstruction (IWR) and introduces IWR- Bench, a comprehensive benchmark that addresses these fundamental design challenges. To ensure comprehensive coverage, tasks are taxonomized along orthogonal axes of application domain, vi- sual complexity, and interaction logic, as illustrated in Figure 3 Each task instance, as depicted in Figure 2 then provides the model with (a) an interaction video that captures a complete, stateful workflow, and (b) the full set of crawled static assets. This setup ensures realistic reconstruction context. Evaluation is conducted via programmatic interaction: an lagent-as---judee executes a ground-truth (c) action sequence assess the generated webpage's functionality. Performance is quantified by two holistic metrics: the Interactive Functionality Score (IFS), a unified measure of operational and logical correctness, and the Visual Fidelity Score (VFS), composite metric inte- grating low-level features with high-level semantic evaluation.
An extensive evaluation 28 leading LVLMs reveals substantial challenges posed by the IWR task. The top-performing proprietary model, GPT-5, achieves a Final Score of 36.35%. A clear performance hierarchy is observed, leading open-source models attain lower scores, and video- specialized models lag even further behind. For the top-performing model, a significant disparity exists between its functional correctness (24.39% IFS) and visual fidelity (64.25% VFS). This gap indicates fundamental limitation across the field: while models can reproduce static layouts with
Final Score of IWR- Bench (Out 100)
60
34.00
30.36
25.53
20
17.89
10.07
10.
GPT-5
Claude- Sonnet-4 (hhnkknng)
Claude- Sonnet-4
Qwena-VL 2358-A228 (tinking)
Gemini- 2.5-pro (thaking))
Grok-4
Qwen2.5 VL-728
MiniCPM .V44.
VideoLL 7B
InternVidde 2.- 8B
Figure 1: Performance of 10 representative models on WWR-Bench. For a comprehensive list of all 28 model results, Table
2

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(a) Video
(b) Assets
Generated code
(c) Actions Annotation
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style
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Figure 2: Overview of the IWR-Bench task and evaluation. The inputs to the model are (a) user interaction video and (b) composite images of all static assets sniffed from the webpage. The evaluation employs an agent-as-judge framework (Zhuge et al. (2224), where automated agent assesses the rendered page's 's interactivity by executing (c) ground-truth action sequence and its visual fidelity through screenshot comparison.
moderate success, their capacity for synthesizing event-driven logic remains severely underdevel- oped.
Our key contributions are:
Benchmark for Interactive Webpage Reconstruction. We introduce IWR-Bench, the first benchmark to formalize and evaluate Interactive Webpage Reconstruction (IWR) from video. It comprises 113 curated tasks from real-world websites, taxonomized along axes of domain, visual complexity, and interaction logic.
Automated Evaluation Protocol. We develop a robust evaluation protocol that employs programmatic agent to assess functional correctness by executing ground- truth action sequences. Performance is quantified by two holistic metrics: the Interactive Func- tionality Score (IFS) and the Visual Fidelity Score (VFS).
Cumetionality-Centere
An Extensive Evaluation and Analysis. We conduct a comprehensive evaluation of 28 lead- ing LVLMs, establishing strong initial baselines. The results reveal critical performance gap between visual replication and functional implementation. Further analysis identifies systematic weaknesses in temporal reasoning and logic synthesis, outlining concrete directions for future research.
2 RELATED WORK
Webpage Understanding. Webpage understanding evolved from structural analysis based on DOM parsing to subsequent multimodal perspective that jointly represents a page's visual and textual content (Furuta al., 2023; Burns et al. 2023, Liu et al., 2002a).. Large Vision-Language Mod- els (LVLMMs) have advanced webpage understanding by enabling unified approach where single model demonstrates strong performance across diverse downstream tasks, indicative deep com- prehension, such element grounding (Team] 2025) and screen-based question answering (Wang et al. al. 2024b) Xu et al. 2024). Among these capabilities, generating code from a visual webpage rep- resentation is a key task where existing models have demonstrated strong performance (Beltramelli
2018 Yun et al.) 2024 Gui et al.) 2025). With the enhanced capabilities of LVLMs in handling
multiple images videos (Bai et al. 2025 OpenAl 2025
Guo al. 2025 Comanici al. 2025
3

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Operation Description (High
Education Learnin
money YouTube
Chtemall
Kowsleedee & Education
Data Thars
Progee
V1-Simple Layout (11.50%)
V2-Standard Grid Layout (43.36%)
Beos
Busihese Services
gasooget 5
Commmmorc
Gaming
V3-Complex Layout (35.40%)
V4- V4-Data Intensive Layout (9.73%)
(1). Core Domain/Functionality
(2). Complexity of Layout Visuals
L1-Static Display(7.96%)
L2-Simple State(47.79%) L3-Complex Workflow(33.63) L4-Algertthmicc/6mm@(0662x (3). Complexity of Interaction Logic
Level)
Figure 3: An overview of the WWR-Bench taxonomy, which organizes tasks along three orthogonal axes: Domain, Visual Complexity, and Interaction Logic.
logical extension of this capability is the generation of interactive wehpages, moving beyond static layouts to better mimic real-world applications.
LVLM Benchmarks. The development of benchmarks for LVLMs has been driven by the rapid
expansion of their capabilities, leading to evaluations of increasing complexity (Jimenez et al.) 2024
Yang et al. 2024 Mialon al. 2023 Lu et al. 2023). This progression is evident in the evolution
from single-image comprehension to mu mi-maee reasoning and video understanding (Yue et al.,
2024
Li ct 2023 Wang et [224a Liu al. 2024b Hu et 2025 Li al.] 2024 Fang
et al.
2024 Fu et al.) 20255 Ning et al. 2023 Chen et al. [024 Yang et al.] 2024: Lu et al.
2025
Concurrently, in the web domain, benchmarks have targeted either webpage understanding
or static code generation from single screenshot (Beltramelli 2018 Laurençon et al., 2024 Yun
(et al., 2024, Si al.) 2024 Gui et al. 2025 Jiang et al. 2025 Awal et al., 2025 Xu et al. 2025), with works like IWR-Bench (Guo et al. (024) creating more robust evaluation metries for this task, while others like PairBench (Feizi et al. 2025) investigate the fundamental reliability of using models evaluators. A recent advancement, Interaction2Code (Xiao et al. (025). extends this by generating code from discrete interaction traces. However, such approaches primarily evaluate single-step, stateless events, rather than the complete, stateful workflows captured in continuous

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video. Therefore, critical disconnect exists between model capabilities for dynamic inputs and the benchmarks for interactive web generation.
3 IWR-BENCH
3.1 TASK DEFINITION AND STRUCTURE
The Interactive Webpage Reconstruction (IWR) task challenges models to generate functional web code from observing interactions. Formally, given video V = (f,,-., fn demonstrating interactions and set of static assets A = {a..... am from the original webpage, the model must generate code C' that reproduces both the visual appearance and interactive behavior observed in V. Each task instance in IWR-Bench comprises four key components:
Video Recording: A screen capture documenting complete user interactions, preserving temporal dynamics and state transitions that define the webpage's behavior.
Static Web Assets: All relevant images, icons, and videos necessary for reconstruction. To pre- vent models from leveraging prior knowledge based semantic filenames (e.g.. logo.png), all asset filenames are anonymized (e.g., renamed to asset_00] cessett00_ppngg (Agrawal et al. 2018 Gurari
et al. 2018). This forces the model rely on visual matching and reasoning.
Action Trajectory: A structured sequence T = {(,,, Ps. d,, U2 4 where each action contains type as parameters Pt. natural language description d,, a visual evaluation flag vi and logical assertions 4 for verification.
Checkpoint Screenshots: Stable-state images = {s1,..,,55 5 capturing the visual state after each action. This ensures evaluation occurs fully rendered pages rather than on transitional
states.
3.2 BENCHMARK CONSTRUCTION
Task Sourcing and Curation
Interoction Recording and Asset Collection
Ground-Truth Truth Trajectory and Checkpoint Annotation
Verification and Finalization
Annatation for orolutionn ogenn exccutios
Expert-deual teoke from sethshes (p:200))
BREC
Intersction video capture with himeshorpped oction loga
Cross verfication enseres connect tie trejectoriss, complese aspets, aczanote checkpaints
D
Staple checkpamm actist for visua vdlidction
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Comprohenssive collectier staffc assers (renges icens, emboatded videss)
Removd trivil coses (final (c113)
decrsity bedeschy/ss161))
Annotations nelace Iogical
Figure 4: The overview of Benchmark construction.
Establishing and maintaining high standards for annotation quality and impartiality is a central de- sign principle in the development of IWR-Bench. The process is shown in Figure
Task Sourcing and Curation. The process begins with an initial set of 200 candidate tasks sourced from real-world websites by experts in web development. Each task is defined by high-level goal and URL to reflect common usage patterns. Through a rigorous curation process involving deduplication and balancing for diversity across predefined axes, such as domain and complexity (Figure 3), this set is reduced to a high-quality candidate pool of 161 tasks for annotation.
Interaction Recording and Asset Collection. For each curated task, interactions the live website are performed by trained annotators and captured as screen recordings, while a browser extension concurrently records the action type a, and parameters p; for each action (Yun et al. 2024 Zhou et al. 2023 In parallel, all relevant static assets, such as images and icons, are collected via auto- mated crawlers and manual inspection.
Ground-Truth Trajectory and Checkpoint Annotation. The raw recordings and action logs are converted into the final ground-truth representation. For each action, the logged type a; and param-
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eters p; augmented through three-step annotation process: (1) natural language description d, is authored to provide a clear instruction; (2) a visual evaluation flag vs is assigned, and a corre- sponding checkpoint screenshot 8; is captured only when this flag is true, signifying major visual state change; and (3) an optional logical assertion is defined where necessary to programmatically verify functional correctness, such the appearance of message or game logic.
Verification. Each annotated task undergoes a two-stage quality assurance process. First, a cross- verification review by different annotator assesses trajectory correctness, asset completeness, and checkpoint fidelity, with a large model additionally used to verify the accuracy of logical assertions against the ground truth (Zheng al., 2023 Gou et al. 2025). All identified discrepancies are rec- tified. Finally, disputed, ambiguuus, or overly trivial tasks are filtered out, leaving final collection of 1 113 verified tasks.
3.3 TAXONOMY AND STATISTICS
IWR-Bench organizes tasks along three orthogo- nal axes (Figure see Appendix [ for details): (I) Domain Coverage, which spans 5 major and 16 subcategories such as e-commerce and educa- tion to reflect real-world web diversity; (2) Visual Complexity, which scales from minimalist lay- outs to data-dense dashboards; and (3) Interaction Logic, which progresses from static content dis- play to complex workflows and algorithmic game logic.
Table 1: Key Statistics of (WR-Bence
Statistic
Number
Video Resolution Statistics Total Videos
113
Short Videos ($20s)
25 1%)
MMediu Videos (20 60s)
72 (63.7%)
Loong Videos (>=60s)
16 (14.2%)
Video Duration (avg/max)
35.4s 172.9s
Unique Resolutions
19 10.62%
Table presents key statistics. The benchmark includes 113 videos averaging 35.4 seconds, with 1,001 total actions across all sequences. Of these,
Mobile
Evaluation Statisties
Total Actions Sequences
1001
620 require visual evaluation and 403 include as- sertion checks, ensuring comprehensive assess- ment of both appearance and functionality. Tasks
Visual Evaluation
620
Assertion Checks
403
Actions per Video (avg)
8.9
average 8.9 actions, with 10.62% targeting mobile interfaces, reflecting modern web usage patterns.
3.4 COMPARISON TO OTHER BENCHMARKS
As detailed in Table 2 WWR-Bench addresses a critical gap between webpage reconstruction and video understanding benchmarks. Existing webpage reconstruction benchmarks either focus static image-to-code tasks (e.g.. Pix2Code, WebSight) or model interaction as stateless, single-step events without providing the necessary static assets for reconstruction (e.g., Interaction2Code). Con- versely, general video understanding benchmarks (e.g., MVBench) are designed for comprehension tasks like Video QA, not code generation.
IWR-Bench overcomes these limitations by using videos of stateful, full-trajectory workflows from al. (2025), the most closely related benchmark, focuses on stateless, single-step events captured by image pairs from archived pages, whereas IWR-Bench requires temporal reasoning over complete user workflows with state transitions. Notably, while all existing webpage reconstruction benchmarks, including Interaction2Code, remove static assets to simplify the task, (WR-Bench provides original assets (images, videos, icons) to enable realistic visual-to-asset grounding. This asset provision further allows the evaluation protocol to compute page-level visual similarity metrics, ensuring comprehensive assessment of reconstruction fidelity.
live websites. Interaction2CodéXiin
4 EVALUATION AND METRICS
4.1 EVALUATION PROTOCOL
The evaluation of generated code C. is conducted using deterministic executor built upon the browser-use library (browser-use 2025). This executor programmatically interacts with the ren- dered webpage by sequentially executing each pre-defined action 0; from the ground-truth action

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Table Comparison of IWR-Bench with existing benchmarks. IWR-Bench is unique its sourcing from live websites, based tasks, comprehensive interactive evaluation, and provision of static assets to create realistic reconstruction task.
Images (Checkpoints)
Asset Input
Desktop Mobile
Interactive
Benchmark
Task Type
Data Source
Videos
Evaluation
(aj Wehpage
Benchmanks
Psx2Code [Beltramelli: 2018
Imuge-to Code
Codde
Symthesized
1.7K
X
V
X
DWCG
Image-to-Code
Synthesized
60K
K
WebSight Laurenyun cl al 2024
Innge-to-Code Code
Symthesized
2M
X
K
X
Design2Cour
Image-to-Code
C4
484
K
CC-HARD Gilll ef 2173
Image-to-Code
C4
128
X
X
X
ScreenCoder Drang
Image-to-Code
Websites
3K
Interaction2Cla: (Allo Eil
2025
Inages-to-Code
C4 GitHab
374
X
X
((Singlestep)
(bj Video Understanding
MMBench Video (Fang al. 2024
Video QA
609
MVBench eLi
Video QA
Vidco-MME Fu 0. 2025
Video QA
900
Video-MMMU at
Video QA
TWR-Bench (Ours)
Video-to-Code
Live Wehsites
113
620
(Full Trajectory)
These are images used for evaluating visual fidelity across interaction states.
This design isolates the evaluation to code execution and removes any dependency on high-level task planning, thereby ensuring a stable and reproducible protocol.
trajectory T.
The evaluation of the trajectory proceeds stsp-by-step. At each step i, action a, is attempted. The action is considered a failure under two conditions: (1) it is operationally infeasible (e.g.. target element not found), or (2) its corresponding logical assertions 4 are not satisfied.
For logical assertion verification, an MLLM judge, specifically Gemini-2.5-Pro (Comanici et al,, 2025). is employed to analyze screenshots the page state before and after an action to determine its correctness. The prompt for this judge is detailed in Appendix L of an action, a new screenshot is captured. If the visual evaluation flag e for this step is true, the new screenshot undergoes visual fidelity assessment. This assessment is based that integrates OCR-base text similarity (Cui et al. 2025), DINO-based structural similarity (Oquab) al. 2023), and high-level evaluation also conducted by Gemini-2.5-Pro (see Appendix for the prompt). Actions where v, is false. which typically involve insignificant or stochastic visual changes, are omitted from this visual assessment phase.
Upon the successful completion
composite score
4.2 METRICS
Model performance is quantified through a hierarchy of metrics designed to measure functional correctness, visual fidelity, and overall task completion.
Interactive Functionality Score (IFS). This metric measures model's ability generate func- tionally correct code. An action a; from the trajectory T is considered successful if and only if executes without operational errors and all associated logical assertions I are satisfied, as deter- mined by the protocol in Section 4.1 The IFS is defined the ratio of successfully completed actions (Nawce) to the total number of actions (Nout)
Nsscc Niotal
IFS
Visual Fidelity Score (VFS). The VFS assesses the visual quality of the rendered user interface. This score is computed exclusively over checkpoints that were successfully reached and have the visual evaluation flag enabled (v; = true). Let Io,sue the set of indices for these qualifying checkpoints. The score for each checkpoint nents: a Low-level Visual Score (SLvs.). which averages an OCR-based Levenshtein similarity and a DINO-based cosine similarity, and a High-level Visual Score (SHvs x), which is holistic assessment from the MLLM judge. The final VFS is the macro-average these checkpoint scores. The weight is set to 0.5 based on validation studies (Section 5.4).
Tr,suce is weighted combination of two compo-
VFS
Σ iElu,
+ Sivs,
w) -Svs..)
(2)
[T,
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Final Score. The Final Score is defined by combining the IFS and VFS with fixed weights. For steps where actions cannot be executed, no images are available to compute visual similarity scores. An alternative weighting scheme based the ratio of successful (Ne to total (Noual) steps plored, but it proved ineffective for differentiating model performance. Therefore, a simple weighted combination is adopted with the weighting factor set to 0.7 (see Section 5.4p. By assigning sub- stantial weight to the IFS component, the impact of unreachable states on overall evaluation is ap- propriately reflected. All reported scores are macro-averaged across the entire benchmark.
Final Score = a I-FF + (1 -aa) a. VFS
(3)
5 EXPERIMENTS
5.1 EVALUATION SETUP
Evaluation Models. The evaluation is conducted diverse set of 28 leading Large Vision- Language Models (LVLMss to establish a comprehensive performance baseline on IWR-Bench. This selection encompasses both proprietary and open-source models, well as specialized video understanding models. The full list of evaluated models and their performance is detailed in Table
Implementation Details. For each task in IWR-Bench, models are provided with the user inter- action video and a composite image of all crawled static assets. To accommodate models without native video support, each video is sampled at 1 fps, with the number of frames capped at 64. Videos exceeding 64 seconds are uniformly downsampled to meet this limit. The video (or its sam- pled frames) and the composite image are arranged as sequential. multi-image input. The task is to generate a single, self-contained HTML file that integrates all necessary CSS and JavaScript to repli- cate the observed webpage. All other inference parameters utilize the default settings recommended by the model providers. The complete prompt templates are detailed in Appendix
5.2
MAIN RESULTS
The comprehensive evaluation results on WWR-Bench are presented in Table The findings reveal a clear performance landscape, highlighting the substantial difficulty of the task and surfacing several key observations regarding current model capabilities, with case studies provided in Appendix [F]
A Clear Performance Hierarchy Is Observed Across Model Categories. The results on IWR- Bench show a pronounced performance stratification across model categories. Proprietary multi- modal large language models are positioned in the upper echelon, with GPT-5 obtaining the high- est Final Score (36.35). This is followed by a competitive cluster that includes Claude-Sonnet-4 (thinking) (34.62), Claude-Opus-4 (thinking) (34.13), Doubao-seed-1. (34.02), and Claude-Sonnet- 4 (34.00). The op-performing open-source model, Qwen3- VL (thinking). has a score of 31.15. This score is lower than that of the leading proprietary group but surpasses several mid-tier proprietary entries, such as GPT-4o (latest) (29.55). At the lower end of the performance spectrum, video- specialized models like This hierarchy indicates that general multimodal reasoning and code generation capabilities are more critical for success on IWR-Bench than specialized video-processing architectures.
(13.67) and Intern Video-2.5-Chat-88 (10.07) are found.
Interactive Functionality Remains the Primary Performance Bottleneck. A substantial perfor- mance gap exists between static visual replication and dynamic functionality implementation. This gap is reflected in the consistently higher Visual Fidelity Scores (VFS) compared to the Interactive Functionality Scores (IFS). For instance, GPT-5 obtains the highest visual metries (LVS 68.29, HVS 60.21, VFS 64.25), yet its corresponding IFS is only 24.39. A similar pattern is observed for Claude- Sonnet-4, which has the second-highest VFS (61 .34) but an IFS of only 22.29. The difficulty of this task is further underscored by the low absolute IFS values, with the highest score remaining below 25, highlighting that interactive webpage reconstruction is a largely unsolved problem.
Reasoning Enhancement Provides Consistent but Moderate Gains. Consistent but moderate inference. For instance,
performance improvements are observed when using reasoning- the "thinking" variant of Claude-Sonnet-4 shows higher performance in both Final Score (34.62 vs. 34.00) and IFS (23.65 vs. 22.29). A similar trend is noted for SludeeOpus-4 (Final 34.13 33.33; IFS 23.61 vs. 21.83) and Gemini-2.5-Pro (Final 30.36 vs. 30.31; IFS 21,65 vs. 20.51).
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Table 3: Main evaluation results on IWR-Bench. Models are grouped by category and sorted by Final Score. Reasoning-enhancee ("thinking") model variants are highlighted in gray. The best result each column is bolded, and the second-best is underlined.
Low-level Visual Score
High-level Visual Score
Visual Fidelity Score
Interactive Functionality Score
Final Score
Model
Proprictary MILLMs
GPT-S (OpenAl 2025
68.29
60,21
64,25
24.39
36.35
Claude-Sonnet- (lunking) aathropic 2025b
64.90
55.51
60.20
23.65
34.62
Claudc-Opus-4 (thinking) (anthropic 2025a
63.53
53,80
58.67
23.61
34.13
Doubao-seed-10. 1,6 bytedance 2025
65.95
55.62
60.79
22.55
34.02
4onne--Snnee-44- unthropic 2025b
65.75
56.92
61.34
22.29
34.00
Slaude-Opusee (anthropic 2025a
65,23
55.13
60.18
21.83
33.33
GPT-S-mini (OpenAI 2025
63,36
50,25
56.81
23,18
33.27
GPT-4.1 (OpēnAl 2022
63.07
54.63
58.85
20.48
31.99
Gemini-2:s-Pro (thinking) (Comanici et al. 2025
54,52
46.83
50.67
21.65
30,36
Gemini-2.5-Pa (Comunici ct al. 2025
$7.46
48.91
53.18
20.51
30.31
GPT-4o (latest) (Hurst 2024
63.39
51.71
57.55
17.55
29.55
Gemini-2.5- (1asFlasCCCooiii Comanic cl
2025
47.53
37.75
42.64
19.88
26.71
GPT-5-nano (OpenAT 2025
53.49
35.70
44.59
18.17
26.10
Grok-4 [X.ai 2025
48.95
30.54
39.74
19.44
25.53
GPT-4o (0806) Hurrst et al. 2024
39.83
46.93
15.87
25.19
Doubao-sced-1 6-ood-------ss (byiedlace
2025
45.49
32.06
38.78
16.34
23.07
Gemini-2. 1e5mimi2.5-Flash---ee Comaniei el al. 2025
28.95
19.05
24.00
13.29
16.50
Open-Source MLLMs
Qwen3-VL. LL (thinking) Qwen Team
2025
58.55
46.13
52.34
22.07
31.15
Qwen2.5- VL-2BB
(Bai et al 2025
47.83
28.25
38.04
17.42
23.61
Qwen2,5-VL-22 VL-32B [Ba cl al 2025
39.36
23.30
31.33
16.50
20.95
Keye-VL- 1.5-8B
Yang et al 2025
30.81
15.49
23.15
16.06
18.18
MiniCPM-V-44 V--.55
Yu al. 2025
31.18
15.41
23.29
15.58
17.89
Qwen2.5-V1.-7B [Bai al 2025
28.92
12.20
20.56
13.28
15.47
Kimi-VI. (thinking) Team al.
2025b
26.18
12.23
19.20
12.04
14.19
Mimo-VL-7B [Team el al.
4.99
10.57
11.64
GLM-4.5V [Team et al. 2025c
16.31
10.52
13.41
10.11
11.10
Open-Source Video-Specialized LMs
VideoLLaMA3377 (Zhang et al. 2025
31.29
11.86
21.58
10.29
13.67
IntemViideo-255-Chaa-- Wang et al. 2025
17.27
3.33
10.30
9.97
10.07
This evidence indicates that while enhanced reasoning acts useful refinement, the base model's capability remains the primary factor determining the performance ceiling on IWR-Bench.
5.3 PERFORMANCE ANALYSIS ACROSS TASK DIMENSIONS
fine- aine-graieed analysis (detailed in Appendix B reveals distinct performance patterns. The synthe- sis of event-driven functionality is the primary bottleneck, evidenced by sharp performance drop from static (L1) to interactive (L2-L4) tasks (Table 4 Models also struggle with highly structured layouts (Ta Performance varies by domain, with relative strength in "Entertainmeet & Media" (Table 6), pointing structured code generation and state management as key research directions.
5.4
VALIDATION OF THE EVALUATION PROTOCOL
The robustness and reliability of the evaluation protocol are validated through rigorous, two-part analysis that addresses both the metric parameters and the agent-as-a-jude methodology (Zheng al. 2023 Gou et al.) 2025 Maaz et al. 2023). First, the weighting coefficients (w and ()) for the scoring metrics are determined through a human alignment study, with the detailed procedure and re- sults presented in Appendix(C Second, the agent-as-a-judee framework validated through a multi- stage process. This process includes meticulous cross-verifieet veriffiiiii of annotated action trajectories (Section 3.2). automated verification of logical assertions using an MLLM-basee judge(Comannl) et al. 2025), and manual inspection of the agent's 's operational fidelity. For the manual inspection, model-generated pages, with the browser's headless mode disabled to compare on-screen behavior against evaluation
three PhD students observed the agent's execution on 100 randomly sampled, model generated web-

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Published as a conference paper at ICLR 2026
logs. Failures in the agent's evaluation observed in only three instances. These issues typically stemmed from ambiguous element descriptors (e.g., buttons with identical names), which required more precise locator (d i). All identified discrepancies were subsequently rectified.
CONCLUSION
This paper introduces IWR-Bench, the first benchmark designed to evaluate Interactive Webpage Reconstruction from video. Through an automated agent-as-a-judge evaluation protocol, perfor- mance is quantified using two metrics: the IFS and the VFS. Comprehensive evaluations 28 LVLMs reveal a stark disparity between visual replication and functional implementation. While models achieve moderate success in reconstructing static appearance (VFS), their ability generate correct, event-driven logic remains critically limited, as shown by low IFS scores across the board. This finding indicates that the primary bottleneck for current models is not visual understanding but the synthesis of complex interaction logic. IWR-Bench thus establishes a challenging new frontier for vision-language research, highlighting the need for future work to focus on temporal reasoning, dynamic asset binding, and robust code synthesis to create truly functional web applications.
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7 ACKNOWLEDGMENT
The research was supported by Shanghai Artificial Intelligence Laboratory.
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A MULTI-DIMENSIONL TASK TAXONOMY
To move beyond a monolithic view of difficulty and enable a fine-grained analysis of model capabil- ities, developed three-dimensional taxonomy to classify each task. This taxonomy categorizes tasks along the orthogonal axes of Interaction Complexity, Visual Complexity, and Application Do- main, providing structured framework to understand the specific challenges inherent in each task and to diagnose model failure modes with high precision.
Interaction Complexity (L1-L.4) This first axis categorizes tasks based on the depth of logical and temporal understanding required for successful reconstruction.
Ll: Static Content Consumption. Tasks involve passive information consumption, pri- marily requiring correct handling of vertical scrolling reconstruct long pages that extend beyond a single viewport (c.g., browsing a blog post or project's ' README).
L2: Simple State Manipulation. Tasks feature components that manage local state, such as filtering e-commerce results, switching between on-page tabs, or expanding/collapsing accordion menus. This level tests the generation of basic client-side event handlers.
L3: Complex Workflow Interaction. These tasks involve multi-step, sequential inter- actions where state is passed between components, such as a multi-step product config- urator online booking process. This tests understanding of application logic and inter-component communication.
L4: Algorithmic/Game Logic. The most complex level requires the model reverse- engineer and implement a set of rules or algorithms, such as an online calculator, a text- based puzzle, or simple game like 2048.
Visual Complexity (V1-V4) This second axis captures the static challenge of rendering the web- page's appearance, focusing on its layout and styling.
V1: Minimalist Layouts. Simple, single- or two-column structures with standard element alignment, typical of documentation or text-heavy sites.
V2: Standard Grid-hased Layouts. Organized grid systems are used e-commerce or news portals, featuring numerous but regularly arranged elements.
V3: Asymmetric Modern Layouts. Visually overlapping elements, parallax scrolling, and non-standard component shapes.
designs with complex CSS, such
V4: Data- Dense Layouts. Dashboards or admin panels with a high density of information presented in charts, tables, and data cards, testing the ability generate precise, repetitive structures.
Application Domain To ensure benchmark reflects the breadth of the third axis classifies tasks by their Application Domain. Drawing inspiration from established taxonomies in web-centric agent research (Gou al. 2025). we group tasks into five high-level domains: Commerce & Services (e-commerce, booking, finance), Knowledge & Education (aca- demic sites, news portals, documentation). Productivity & Tools (calculators, project management boards), Entertainment & Media (games, streaming platforms), and Lifestyle & Community (so- cial forums, blogs). This classification guarantees that models are evaluated across a diverse spec- trum of functionalities and visual paradigms. For instance, task involving filtering products on an e-commerce site is tagged as [L2, V2, Commerce], while a task requiring the reconstruction of a simple browser game is tagged as [L4, VI, Entertainment]. This multi-dimensionas labeling allows us to analyze whether a model's performance correlates with certain types of interactions, visual styles, application contexts.
web applications,
B DETAILED EXPERIMENTAL RESULTS
This appendix provides a comprehensive breakdown of model performance on IWR-Bench across the three classification axes defined in the taxonomy: Application Domain (Table 6). Interaction Logic Complexity (Table 4). and Visual Complexity (Table 55. For each task category, the Final Score is reported.
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Table 4: Final Score breakdown by Interaction Logic Complexity.
Static Content
Simple State Manipulation (1.2)
Complex Worktlow Interaction (1.3)
Algorithmic/Game Logic (1.4
Model
Consumption (T.I)
Proprietary Ms..Ms
GPT-5
61.85
35.43
35.12
25.26
Claude-Sonnet-4 (thinking)
65.75
33.88
31.05
25.80 25.86
Claude-Opus- (thinking)
61.78
31.52
32.57
30.07
Doubao-scod-1a
63.69
34.03
30.12
Claude-Sonnet-4
68.36
32.86
25.15
Claude-Opus-4
66.88
30.85
31,30
25.76
GPT-5-mini
56.83
32.10
31.38
26.84
GPT-4,
51.45
30.32
31.98
24.78
Gemini-2.5-Pro (thinking)
68.96
26,88
28.15
23.83
Gemini-2.5-Prr
59.58
31.51
23,95
22.58
GPT-4o (latest)
48.61
29.09
27.53
23.75
Gemini-2.5-Hassh
56.57
24.95
25.53
15.00
GPT-5-nano
46.23
25.57
24.31
19.02
Grok-4
48.37
24.94
22.76
19.53
GPT--40 (0806)
38.59
24.95
24.65
17.90
Doubao-seed- 1.6-flash
42.77
22.14
22.65
13.88
Gemini-2.5-Flash-Le
35.34
15.61
13.87
14.70
Open-Source MILMs
Qwen3-VL (thinking)
51.05
30.43
29.86
23.60
Owen2.5-VL-72B
45.35
22.83
21.90
15.46
Owen2.5-VL-32B
37.64
20.45
18,80
17.48
Keye-VL-115-88B
46.85
19.47
12.11
10.13
MiniCPM-V-4.5
37.30
18.02
15.27
11.10
Qwen2.5-VL-7B
28.58
15.99
13.91
8.21
Kimi- VL (thinking)
23.61
15.45
10.63
12.75
Mino-VIL--B
22.23
12.32
9.36
7.87
GLM-4.5V
18.56
11.02
9.77
10.08
Open-Source Aideo-Specialized LMs
ViM33LL
23.11
14.15
12.31
8.77
Intern
27.89
9.81
7.68
5.42
C METRIC PARAMETER VALIDATION
The VFS and Final Score metrics rely the weighting coefficients and These parameters determined and validated through a human alignment study. A sample of 60 evaluation instances constructed by selecting outputs from three randomly chosen models for each of 20 randomly selected tasks from IWR-Bench. Each instance is assessed by five PhD-level students on two di- mensions: visual fidelity and overall quality. To determine the optimal parameters, grid search is performed over the discrete set {0.1, 0. 0.9} for both w and a. The value that maximizes the Spearman's 's p correlation between the automated scores and the aggregated human judgments is selected. For the VFS metric, the peak correlation (p = 0.57) is observed at w = 0.5, indicating an equal weighting between LVS and HVS. For the Final Score, the maximum correlation with human overall judgment (p = 0.65) is achieved with = 0.7, empirically validating the decision to weigh functionality (IFS) more heavily than visual fidelity (VFS).
D TASK AND ACTION REPRESENTATION
Each task in IWR-Bench is formally defined by an 'action sequence', a structured list of discrete actions that an automated agent must perform to validate the reconstructed webpage. This rep- resentation standardizes the evaluation process. We defined vocabulary of atomic actions, in- cluding (deeerdet)))) Type(key, description). Scroll(direction, amount, description), and Press(key, description). A crucial design choice is the use of natural language 'description' field for targeting elements instead of unstable positional coordinates (e.g., "Click the primary 'Submit' button" instead of "Click (x: 120, y:350)"). This makes the evaluation robust to minor layout vari- ations in the generated code and tests a more semantic understanding the page structure, both for the model during generation and the agent during evaluation.
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Table 5: Final Score breakdown by Visual Complexity.
Minimalist Layouts (V1)
Standard
Asymmetric Modern Layouts (V3)
Data-Dense
Model
Grid-based Layouts (V2)
Layouts (V4)
Proprietary MLLMs
GPT-5
44.77
30.73
43.77
26.05
Claudc-Sonnct-4 (thinking)
40.05
31.12
40.08
25.26
Claudc-Opus-4 (thinking)
37.01
32,26
38.95
22.97
Doubao-secd-1.
41.55
30.56
38.03
26.97
Claude-Sonne--4
37.33
30.28
39.95
26.25
Claudo-Opus-4
35.77
30.79
38.34
24.77
GPT-5-mini
38.76
29.29
26.75
GPT-4.1
28.96
25.32
Gemini-2.5-Po (thinking)
31.59
25.77
36.99
25.82
Gcmini-2.5-Pro
37.20
25.17
36,51
23.27
GPT-4o (latest)
31.89
26.14
34.64
24.41
Gemini-2.5-Flash
32.21
21.80
34.13
16.28
GPT-S-nano
29.21
24.39
28.96
20.37
Grok-4
33.73
22.11
30.01
17.18
GPT-4o (0806)
29.88
23.54
27.
19.62
Douhao-seed-1.6-1 1-6-fassh
30.23
22.76
22.87
17.27
Gemini-2.5-Flashe
19.64
15.44
19.31
8.31
Open-Source MLLMs
Qwen3-VL. (thinking)
38.14
28.74
33.37
26.25
Qwen2.5-VT.-72B
28.68
21.14
28.31
12.48
Qwen2.5-VL-32B
27.56
17.90
24.06
16.15
Key5-VL-11.58BB
24.75
15.65
20.85
12.74
MiniCPM-V-4.5
28.24
16.11
18.87
10.79
Qwen2.5-VT.-78
20.48
12.68
18.52
11,48
Kimi-VL (thinking)
18.57
14.22
14.21
9.26
Mimo-VL-7B
14.68
11.42
12.42
6.71
GLMM-45V
12.39
10.66
13.34
4.20
Open-Source Video-Specialized LMs
VideoLLLMMA3-75
18.81
13.55
13.69
8.56
IntemViicoo225-ChaattB
13.36
11.07
9.09
5.57
E PROMPTS
A standardized system prompt is employed for all models and tasks in IWR-Bench to ensure a fair evaluation. This prompt defines clear requirements for the task, output format, and operational con- straints. Such a design minimizes ambiguity and helps isolate the core code generation capabilities of each model. The complete prompt template is detailed in Figure
The evaluation relies on large multimodal model guided by two distinct prompts. To assess the similarity between generated and reference webpages, prompt template utilized (Figure 6 This template instructs the model to perform both quantitative and qualitative evaluations. For logical assertion verification, a separate prompt, presented in Figure 7 is employed to determine the cor- rectness of action.
F
CASE STUDY
This section presents a selection of representative tasks from the IWR-Bench to illustrate the di- versity of challenges encompassed by our benchmark. The input of each case includes webpage operation video and the static resources involved in the webpage. Then the web pages generated by different multimodal large models and the corresponding interaction results are displayed. Then we provide detailed analysis of these representative cases, corresponding to the figures presented below. Each analysis breaks down the task's 's objectives, its position within our taxonomy, and the specific model behaviors observed, illustrating how our benchmark facilitates ime-grained diag- nosis of model capabilities.
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Table 6: Final Score breakdown by Application Domain.
Business & Services
Entertainment & Media
Knowledge & Education
Life & Community
Productivity & Thols
Model
Proprietary MLLMs
GPT-5
39.37
47.24
37.05
22.80
28.53
Claude-Sonnet-4 (thinking)
39.74
41.02
32.60
24.86
31.15
Claude-Opus (thinking)
35.54
42.48
34.91
24.21
28.56
Doubao-seed-1.6
37.82
43.19
32.14
22.44
30.20
Claude-Sonnet-4
38.84
42.09
32.22
25.57
27.58
Claude-Opus-4
38.64
43.25
30.51
22.03
28.09
GPT-5-mini
37.16
45.11
30.29
21.38
28,36
GPT-4.1
33.58
45.35
27.61
17.28
32.65
Gemini-2.5-Pro (thinking)
31.49
41.67
30.31
15.82
26.34
Gemini-2.5-Pro
33.66
36.93
30.74
19.03
25.55
GPT-4o (latest)
36.33
32.35
28.97
20.01
25.42
Gemini-2.5-Flan
29.16
40.60
21,38
15.27
25.83
GPT-5-nano
30.61
34.29
22,67
16.50
23.69
Grok-4
27.71
32.06
26.47
15.00
19.85
GPT-4o (0806)
27.60
32.05
23,97
15.54
23.29
Douban-seed- 1-6fllssh
25.37
31.07
20.17
14.21
22,36
Geminn-22.5Flashh-
18.38
24.93
10.13
9.42
20.55
Open-Source MLLMs
Owen3-VL (thinking)
37.36
35.00
29.57
17.71
31.19
Qwen2.5-VL-77
28.71
31.20
20.89
14.29
19.88
Qwen2.5-VL-32B
24.05
28.58
20.67
9.80
16.73
Keye- Beye-VL-1.5-8B
19.27
26.29
16.42
16.50
MiniCPM-V-445
17.10
26.63
17.38
11.21
14.63
Qwcn2.5-V1.-7B
17.11
21.96
16.69
4.67
11.61
Kimi-VL (thinking)
16.77
17.58
13.87
9.59
10.83
Mimo- VL-7B
12.90
14.64
12.11
4.33
11.08
GLM-4.5V
10.78
18.20
10.10
2.88
11.10
Open-Source Video-Specialized LMy
VideoLLaMA3-7B Intern Viden-2.5-Chht--88
14.36
16.44
14.44
9.21
11.51
10.77
15.77
9.18
2.90
9.36
Case Analysis: E-commerce Workflow Simulation. Our first case study, classified as [L2, V2, E-commerce], simulates fundamental e-commerce user journey to test model's ability to handle sequential state manipulations within a standard visual structure. The task requires the model to replicate workflow involving filtering a product grid by specific brand, sorting the filtered results by price and adding selected item to the shopping cart.
As illustrated in Figure this task effectively exposes different failure modes in different models. On the left, Claude-Sonnete demonstrates good capabilities in static replication and simple state management. accurately renders the initial layout and correctly implements the action for filtering and sorting. However, its failure occurs at the final "add to cart" step. The right side shows the result of GPT-5. This case likely involved too many static resources, causing the product list on the initial page to fail to render successfully. By pinpointing these different stages of failure, the benchmark provides granular diagnosis of each model's specific strengths and weaknesses in front-end code generation.
Case Analysis: Algorithmic Logic Reconstruction. This case study moves the highest level of our interaction complexity scale, L4, to assess model's capacity for algorithmic reasoning. Classified as [L4, V2, Gaming], the task requires the model to reverse-engineer and implement the complete set of rules for simple browser game (e.g., 2048) based solely observing its behavior in the input video. The visual complexity is simple (V2), deliberately shifting the evaluation focus from layout replication to the correetness of the underlying algorithmic logic. The The core challenge is to deduce and codify the game's 's state- transition functions, including tile movement, merging logic, and the spawning of new tiles.
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Prompt template for the IWR-Bench
You are an expert front-end developer. Your task to create a pixel-perfect replica website from video.
Generate a single 'index.html' file that contains all HTML, CSS, and JavaScript necessary to replicate the UI, content, and interaction features shown. The webpage resolution in the video <resolution>>.
Instructions:
All HTML, CSS, and JS must in one 'index.html' file. If backend logic is implied, mock in JS with static data (e.g., JS array for fake API call).
1. Single File Output:
(smaees and Videos in the webpage):
All images must use the provided stitched image assets.
The 'sre' attribute must start with the literal, unchanging string followed by the actual filename iden-
tified from the stitched image.
For example: 'src=" PLACCEOLLDE LASEEESERRSSSSSSSSSESS BASE BASE.DIR-/aSScOO...ee
'<img>> tags must include 'width" and 'height' attributes.
The provided stitched image assets before the video.
No External Dependencies: The generated code must be entirely self-contained. No External Libraries and External Fonts.
5. Final Response: Return only the complete HTML code in single "'html code block, with no additional text or explanations.
Figure 5: Prompt template for the RRRBBench
Prompt for evaluating HVS
You expert Webpage Evaluator. Your task is provide quantitative and qualitative assessment of the similarity between a generated webpage and reference webpage. The default score is O.
Evaluation Format:
Comments:
Layout (10 points):
{ccomeent and subscore]
-Elements (15 points):
(ccomment and subscore)
Content and Text (40 points): $(comment and subscore)
-Style (15 points): S(comment and subscore)
-Overall (20 points): ((comment and subscore]
Score: S(final score //100
Figure 6: Prompt for evaluating HVS
As illustrated in Figure 9 the left side is the result of Grok-4, which successfully reproduce the 2048 game logic from the input video. However, Qwen2.5-Vil V-VL-2BB failed to merge the correspond- ing blocks after inputting 'f'. This type of task requires high level of logical reasoning ability from the model and is a significant challenge.
Case Analysis:
Fidelity and Fine-Gree Grained Visual Detail. This case study, clas- sified [L1, V3, E-commerce], is designed to stress-test model's visual fidelity multiple fronts. While the interaction is simple (L1, passive scrolling). the task's 's difficulty lies three distinct chal- lenges: (1) maintaining structural integrity across long page, (2) correctly handling diverse media assets, including an embedded video, and (3) achieving fine-grained visual accuracy, particularly with small, repetitive elements like icons. This multi-faceted task evaluates not just broad layout
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Prompt used to determine whether the assertion is correct
Please compare two webpage screenshots (Image the previous step, Image 2 is current step) and determine whether the following assertion is true:
Assertion: (assertion)
Return JSON format without any additional information:
{{
"think": "the thinking process",
"result": "Yes-No"
}}
Figure 7: Prompt used to determine whether the assertion is correct
construction but also the model's attention to detail and its ability to precisely match visual elements the provided stitched assets.
As illustrated in Figure TO, Gemini-2.5-pro and GPT-5 can both restore relatively complete long pages based on videos, but they do not handle the details the web pages well, including the corresponding icons and matching product images. A successful reconstruction would require both holistic understanding of the page structure and meticulous attention its smallest components.
Time-Based State Management in a Mobile Viewport. This case study, clas- sified as [L3, VI, Productivity & Toois], is designed to evaluate a model's ability to handle time- driven state changes, presented within the constraints of mobile screen resolution. The task is to reconstruct a functional Pomodoro timer. While the visual complexity is low (V1), the mobile viewport requires the model generate a layout that is responsive or appropriately scaled for nar- row screen. The primary challenge, however, resides in the L3 interaction complexity: the model must implement a state logie governed by both user clicks (e.g., "start', 'pause") and asynchronous, time-based events (the countdown reaching zero).
Case 4 Analysis:
As illustrated in Figure II GLM-4.5V can successfully implement the interactive operations and logic in the video, but Kimi-VL-thine -L-thinkinn is unable to perform subsequent operations because the elements that need to be clicked in the first step are missing in the initial state.
G USE OF LARGE LANGUAGE MODELS
We utilized Large Language Model to assist with grammar correction and language refinement in this paper.
H COMPARISON WITH PIPELINE METHODS
As shown in Table 7 two-stage pipeline method is evaluated to investigate the effect of decoupling perception and generation. In the first stage, the model is prompted to analyze the interaction video and generate a structured description of user actions (the prompt is detailed in Figure 12). In the second stage, this generated description, together with the original visual inputs (video and stitched images), fed to the model to produce the final code (the prompt is detailed in Figure results indicate that this approach does not yield significant performance gains. This outcome sug- gests that the IWR-Bench task requires tight integration of visual perception, reasoning, and code generation. Critical information, such as assets matching and high-fidelity visual layout, is difficult fully encapsulate in textual descriptions. Consequently, the two-stage pipeline approach proves less effective for this task.
13). The
I ANNOTATION GUIDELINES
We have provided the annotation guidelines, shown in Figure 14 and Figure
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Table Performance comparison between End-to-End and Pipeline approaches on IWR-Bench
Model
Method
LVS
HVS
VFS
IFS
Final Score
End-to-End
68.29
60.21
64.25
24.39
36.35
GPT-5
Pipeline
67.15
58.95
63.05
25.03
36.43
End-to-End 65.75
56.92
61.34
22.29
34.00
Claude-Sonnet-4
Pipeline
64.88
58.87
61.88
23.05
34.70
End-to-End
58.55
46,13
52.34
22.07
31.15
Qwen3-VL (thinking)
Pipeline
59.21
43.58
51.39
20.31
29.64
End-to-End
47.83
28.25
38.04
17.42
23.61
Qwen2.5-VI-72B
Pipeline
49.11
27.04
38.08
15.21
22.07
End-to-End
28.92
12.20
20.56
13.28
15.47
Qwen2.5-VI-7B
Pipeline
26.12
12.51
19.32
12.16
14.31
J
TASK SOURCING
Figure 16 displays the examples of our Task Sourcing
K RESOLUTION STATISTICS
Table 8 presents the frequency of various resolutions in IWR-Ber RBBenhh
L ASSETS STATISTICS
To investigate the impact of asset quantity on model performance, the distribution of assets across tasks is analyzed and correlated with the performance of the ttateoffthhear model, GPT-5. As shown in Table 9 IWR-Bench contains average of 74.02 assets per task, with minimum of and maximum of 502 assets. Figure T7 and Figure T8 summarize the relationship between asset count and model performance. As shown in Figure 18 the Visual Fidelity Score (VFS) tends to decrease as the number assets increases. Tasks with many assets require more fine-grained visual grounding and precise asset matching.
In contrast to the VFS trend, Figure 17 shows that the overall Final Score exhibits a generally positive correlation with asset count. This effect appears counter-intuitive if assets considered in isolation and better explained by the interaction between asset density and task type in IWR- Bench. In practice, many tasks with few assets correspond to high interaction complexity. For example, the 2048 game case contains only a small number of visual assets, yet requires models to reconstruct non-trivial algorithmic, event-driven logic. Conversely, the Apple product introduction page, which includes a rich set of images and icons but is dominated by static content consumption and scrolling.
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Table 8: Resolution Statistics
Resolution
Count
2560x 1304
40
1920x990
21
1920x944
20
644x 1398
8
430x932
4
1920x924
2
2560x 1262
2
2560x1270
2
2560x 1260
2
1920x926
2
2560x 1286
2
2560x 1288
2
2180x 1 300
2
1920x968
1
1846x886
2560x 1392
1920x922
Table 9: Statistics and distribution of asset counts per task IWR-Bench.
Statistie
Value Maximum Assets
Minimum Assets
502
Average Assets
74.02
Asset Count
Number of Tasks
0-39
46
40-79
80-119
15
2 120
17
Total Tasks
113
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Input
Vidzo.mp4
Stitched Assets
Claude-sonnee
Gpt 1pt-5-2025-08-07
Click failed
Click passod
Click pessed
Click passed
Click ed
Figure& Case 1: Multi-Step E-commerce Workflow. This task, classified as [L2, V2, E- commerce], requires reconstructing a core e-commerce workflow involving filtering products, sort- ing the results, and adding an item to the shopping cart.
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Input
++
Video mp4
Asises Assets
Grok-4-00 440009
Qwen2. Swen2.5-VL--- VL--2B
Ennerr passed
Enter 'o' feilld
Enterr 4 passed
Enter "a" pessed
Enter passed
Algorithmic Game Logic Reconstruction. This task challenges models to reconstruct the rules of the simple browser game 2048. Classified as [I4,, V2, Gaming], the primary difficulty lies in algorithmic correctness, not visual complexity.
Figure 9: Case 2:
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Input
iPhone
Video.mp4
Stitched_Asses
Gemini-2.5-pro
Gpt-5- 55 2-2025-08-07
iPhone
Samelleassed passed
Scroil passed
Set to know iPhone.
Heer
A18
PRO
Scroll passed
Explore Trchuuig
Euplorentne the Ineup
Scroll failed
Explore
Scroll passed
Figure 10: Case Full-Page Reconstruction with Long Scrolling. This task focuses on funda- mental capability: reconstructing webpage that extends far beyond the initial viewport. Classified as [I.1, V3, E-commerce), it tests the model's ability to handle statie content scale.
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Input
25:00
IIAT
OSt
Video mp4
Stitched Assets
GLM-4.5V
25:00
05:00%
15%00
25:00
25:00
25:00
Click passed
Click passed
Click passed
Click passed
Cocollon Paoaadaro enline Fomodare
Pareiin Tinee
Porader boeat your crodlc
yoortyear poduccitty
bonl you productdds
booll productiiyy
Limi-kL-g
Clickfailed
25:00
25.00
Figure 11: Case 4: Pomodoro Timer Logic within Mobile Viewport. This task requires recon- structing Pomodoro timer rendered at a mobile resolution. Classified as [L3, V1, Productivity & Tools), the core challenge not the simple layout but the implementation of time-based state transitions (start, pause, reset).
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Prompt template for Stage of the pipeline method.
Your task is to analyze the provided video frames and describe the user interactions in structured JSON format.
Your output MUST be a single, valid ISON object.
The JSON should contain an array named 'interactioos'. Each object in the array represents one step and must include:
"step": (Integer) The action number.
"user_action": (String) A clear description of the user's action (e.g., "Click the 'Submit' button")..
"state change": (String) description of the visual changes on the screen after the action. 'inferred logic": (String) The functional rule demonstrated this interaction (e.g., "Click- ing 'Submit' validates the form and shows a success message.)). #*xxample Object:**
"step":
"user_action" "Click the 9th star for rating.". "state change": "The first stars turn yellow and message "Rating updated: 9/10' ap- pears.".
"inferred_oogi "The webpage should update the rating the selected star's value and confirm it with message."
Analyze the video frames and generate the complete JSON.
Prompt template for Stage of the pipeline method. The model is instructed ana- lyze the interaction vidco and generate structured JSON description containing user actions, state changes, and inferred functional logic for each interaction step.
Figure 12:
Prompt template for Stage 2 of the pipeline method.
You are an expert front-end developer. Your task is to create a pixcl-perfect replica website from video and the interactions json.
Generate a single 'index.html' file that contains all HTML, CSS, and JavaScript necessary to replicate the UI, content, and interaction features shown. The webpage resolution in the video <resolution
Instructions:
1. Single File Output: All HTML,, CSS, and JS must in one 'index.html' file. If backend logic is implied, mock in JS with static data (e.g., JS array for fake API call).
Assees(Images and Videos in the webpage):
All images must use the provided stitched image assets.
The 'sre" attribute must start with the literal, unchanging string AACEHOLDERAASSETT ASSETS_BASE_DIR_// tified from the stitched image.
followed by the actual filename iden-
- For example: 'src=" LLA__LPAAAHHHLORAASSSS DA BAS.SSSSSSSEDD..SSSOOO.O.L.....
'<img>" tags must include 'width' and 'height' attributes.
The provided stitched image assets before the video.
No External Dependencies: The generated code must be entirely self-contained. No
External Libraries and no External Fonts.
5. Final Response: Return only the complete HTML code in single "'html code block, with no additional text or explanations.
Interactions JSON: <interactions.json
Figure 13: Prompt template for Stage 2 of the pipeline method. The model receives the structured interaction description from Stage 1, along with the original video frames and extracted assets, to generate the complete webpage code.
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Core Handbook for Data Collection and Annotation Principles (The Four Cornerstones)
1. Clarity: The task's objective and the user's actions must be unambiguous and instantly understandable:
2. Controllability: The recording environment must strictly controlled ensure every task is 100% reproducible.
Evaluability: Each task be braken into sequence of discrete, verifiable steps and actions.
4. Consistency: All annotators must utilize the same tools, settings, and procedures
guarantee dataset consistenty.
II. The Three-Step Recording Process
Step Pre- Rre-Recodding Preparation
Before initiating recording, you must complete the following preparatory steps:
Task Familiarization: Thoroughly understand the task's objective and required operational flow (e.g. game rules).
2. Classification Review: Confirm that the task's [L, Domain] classification appropriate, modifying with note if necessary.
3. Final Checklist (All items must be "Yes"):
[] Compliance:
Can the task be completed within single browser tab?
Does the task avnid file uploads?
Are image assets controllable and not dependent on features infinite
scrolling?
[] Feasibility:
Is the webpage free of sensitive content (political, private)?
Does avoid complex CAPTCHAs (simple numerical recognition acceptablej?
Is the task duration reasonable. ideally betweon 15 and 200 seconds?
Has optimal sequence operations been planned in advanoc?
Step 2: Standardized Recording Environment Setup
ensure consistency, all recordings must be conducted within the following standerdized environment:
Browser:
Google Chrome (Stable version).
Browser State:
Use Incognito mode window every
Disable all browser extensions to prevent interference.
Start fresh Incognito window for each task.
Page Preparation:
lagin required, log first and begin recording from the post-login page. Close any pop-up advertisements whenever possible.
Recording Settings:
Use the officially provided screen recording software.
Scope: Record browser viewport only, excluding the address bar, bookmarks, or OS Ul.
Step Normative Recording Procedure
Execute the recording a manner that ensures clarity and reproducibility.
Starting Point: Begin recording only after the URL has fully loaded the browser.
Mouse Operations: Ensure all movements are deliberate and purposeful, following
Move >> Pause >> Act pattern, while avoiding meaningless or rapid cursor movements.
Figure 14: Annotation Guidelines
29

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Published as a conference paper at ICLR 2026
Wait for all page elements, animations, and transitions to fully load or complete before proceeding to the next action.
Pacing:
Integrity: Record the entire process from the starting point to the final, stable state task completion. If any mistake occurs, you must abort the recording and start new. III. Prot-Procsssnn:::: Screenshots and Assertions
After recording, the video must be processed to add evallaatoon point screenshots, assertions. Evaluation Point Screenshots
Definition: Screenshots captured key moments when the page's visuel state undergoes significant stable change. Naming Convention: wwal-paint-00.ppm png. eval-paint-01 .ng.. etc., incrementing sequentially from 00.
When to Capture Screenshot:
Capture Is Mandatory:
i. Initial State: The stable page before any action taken.
ii. Post-Interaction State: After an action causes stable visuel change, such as modal window appearing.
content updating after filter is applied
Final State: The stable page after the task is fully completed.
Capture Generally Avoided:
Transient effects from mouse hovers.
The process of typing in an input field (only capture after typing complete and triggers a page update).
Intermediate frames during smooth scroll.
2. Assertion Annotation
Definition: textual statement used to verify logical correctness action's outcome, especially for non visual changes like price calculations or sorting logic.
non-visual
Principles Writing Assertions:
1. Binary: The statement must be verifialle as unequivocally true or false.
Good: "The number
top-right comer the cart icon '3"."
Bad: "The cart seems to have been updated."
2. Precise: The statement must describe specific, quantifiable facts.
Good: "The text in the 'Total Cost" field reads '$2,049.99""
Bad: 'The price looks correct."
3. Targeted: The statement should focus logic, data.etc, purely visual style.
Good: "After applying the filter, the result count changed from '100 items' to '15 items'."
"After applying the filter, the list's background turned gray."
When to Add Assertion:
After Content Changes: Following sorting or filtering operations.
Example: "After sorting by 'Price: Low High," the price the first item should less than or equal to the second."
After Numerical Calculations: When shopping total price updates.
Example: "After changing the item quantity from 1 to 2, the total price should update to 'E198.00'""
After State Changes and Feedback: When feodback appears after form submission.
Dxappee: "After entering an invalid password, error message 'Incorrect password format' appears."
For Game/Algorithm Logic: Verifying rules in games like 2048 Sudoku.
Example "After merging two tiles, new tile appears, and the score increases by 8 points."
Figure 15: Annotation Guidelines
30

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Published as a conference paper at ICLR 2026
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Figure 16: Examples of Task Sourcing
31

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Published as a conference paper at ICLR 2026
Task Count and Average Final Score by Asset Count
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Figure 17: Task Count and Average final score by Asset Count
Task Count and Average VFS by Asset Count
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Number of Assets Task
Figure 18: Task Count and Average VFS by Asset Count
32


Reviews:
[{"review_id": "SiZY3OjnXD", "reviewer": "Reviewer_7Tx2", "summary": "This work introduces IWR‑Bench, a benchmark that asks multimodal models to watch a video of a real website in use, along with a sheet of actual page assets, and then generate HTML/CSS/JS code that reproduces both the look and the interaction logic shown in the video. To curate IWR‑Bench, the author asks web‑dev experts to propose a large batch of candidate tasks, following which human annotators perform the workflow on the live website and record interaction videos. In parallel, they automatically collect all static assets used on the page, such as images and icons. Finally, 113 tasks are curated in IWR‑Bench. The generated websites are evaluated by replaying the workflow, and an LLM is used to score the interactive functionality and visual fidelity. The experiment results show that the SOTA LLM struggles to achieve reasonable performance. In particular, synthesizing correct interaction logic is the main bottleneck.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The task of synthesizing website code directly from user‑interaction video is both novel and interesting, and likely to remain challenging for multimodal LLMs in the near term.\n\n2. Dataset curation is careful and well‑documented, with cross‑annotator verification and asset checks. Therefore, the resource should be useful to the community.\n\n3. It's an interesting finding that LLMs mainly struggle with interactive functionality", "weaknesses": "1. It's understandable that building such a dataset is labor-intensive and time-consuming. But a test set with only 113 tasks might be too small-scale.\n\n2. The evaluation metric might be too binary since a minor defect (e.g., a button a few pixels off that breaks a click) is penalized the same as a significant logic error (totally different UI element). \n\n3. Although the proposed task is interesting, practical and non‑malicious value propositions are under‑argued. The new task would be more well-motivated if the author explained why reconstructing an existing site.", "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": "5ESfmpLiFp", "reviewer": "Reviewer_vw9e", "summary": "* The paper introduces IWR-Bench, a new benchmark for evaluating vision-language models on the task of reconstructing interactive webpages from videos of user interactions. The benchmark contains 113 tasks collected from real websites, covering 1,001 user actions with diverse layouts, domains, and interaction complexities. Each task includes a user interaction video, crawled static assets, action trajectories, and checkpoint screenshots.\n* The valuation is performed using an agent-as-judge method. Twenty-eight LVLMs are included for the evaluation: GPT-5, Claude, Gemini, etc and the top-performing model only obtain a score of 36.35.", "questions": "* What is the correlation coefficient between the metric results on the benchmark proposed in this paper and the metric results on Interaction2Code?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The paper provides a comprehensive and well-structured dataset sourced from real-world websites, ensuring realism and diversity across domains, visual styles, and interaction types.\n* Extensive empirical study across 28 LVLMs offers valuable baseline results and comparative insights.", "weaknesses": "* The difference between this paper and prior work such as Interaction2Code is not substantial. The main distinction lies in the number of interaction steps: Interaction2Code handles single-step interactions, whereas IWR-Bench introduces multi-step sequences. This difference, while incremental, limits the benchmark’s overall novelty and impact.\n\n* For experiments, as mentioned around Line 090, the challenges of interactive webpage reconstruction lie in two aspects: multi-modal perception and code generation. It would be valuable to include pipeline-based baselines. A natural idea would be to use a vision-language model to describe each step of the interaction video in terms of states and action assertions, and then feed this detailed description as a caption to a code-specialized model to generate the webpage code.", "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": "9UQv3EZjI1", "reviewer": "Reviewer_czER", "summary": "The authors introduce the benchmark IWR-Bench, a benchmark for video to code generation. In this benchmark, the multimodal large language model (MLLM) receives a video of a user interacting with a website, along with anonymized assets, and must generate code that reproduces the same functionality. They also propose a framework for evaluating the generated code using two metrics: the Interaction Functionality Score (IFS) for functional accuracy and the Visual Fidelity Score (VFS) for visual correctness. The overall performance is reported as a weighted combination of these two metrics. The authors evaluate 28 MLLMs, including GPT-5, Sonnet-4, and Gemini-2.5-Pro, showing that current models still struggle with this challenging task.", "questions": "- What are the resolutions of the videos used in the benchmark?\n\n- How much does it cost to run the benchmark on proprietary models versus open-source models, given that the agent-as-a-LLM is Gemini-2.5-Pro? This would help assess the practicality of the benchmark for everyday use.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The paper defines a novel and interesting task, video-2-code, which is both challenging and potentially useful for practical applications.\n\n- The paper is well-written and provides detailed descriptions of data generation, evaluation metrics, and experimental setup.", "weaknesses": "- Missing citation: the benchmark PairBench [1] investigates when an MLLM can act as a reliable judge and could be used to identify an agent-as-a-judge suitable for this task.\n\n- Minor typos, such as inverted quotation marks.\n\n- The paper does not report the average, maximum, and minimum number of assets per task, which could help analyze how performance varies with the number of input assets. A results table showing model performance grouped by asset count would make this analysis clearer and more informative.\n\n[1] PairBench: Are Vision-Language Models Reliable at Comparing What They See?", "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": "GKScNyTr4T", "reviewer": "Reviewer_dLFH", "summary": "The paper shifts evaluation from static mimicry to working webpage from interaction videos, revealing a wide functionality gap in today’s LVLMs and offering a rigorous, automated protocol to measure progress.", "questions": "1. Task source: the paper mentions \"Experts in web development\", diversity balancing, and the three-step annotation. However, there is no any detail, such as concrete criteria and inter-annotator agreement. Would the authors supply more details?\n2. VFS: VFS is computed only on checkpoints that are both reached and flagged, so that the missing states never contribute. Models that fail early avoid penalties on hard visual states, with higher VFS. please clarify the metric in details.\n3. Weights: VFS blends the scores with w=0.5; the Final Score uses α=0.7. The paper cites a validation study but provides few in‑text sensitivity analyses.\n4. Single‑judge bias: The same MLLM (Gemini‑2.5‑Pro) is used to verify logical assertions and provide the high‑level visual score, and this model also appears on the leaderboard. Were the results verified by humans? i am concern its reproducibility and stability. A single MLLM pass determines assertions, which may exhibits sampling variance. The authors should run multiple independent judging passes and aggregate the results.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Shifts the problem from static screenshot‑to‑code to interactive webpage reconstruction from video;\n2. First benchmark to combine videos and ground‑truth action trajectories for web reconstruction, filling the gap between static page benchmarks and general video QA.", "weaknesses": "1. With only 113 tasks, the benchmark is relatively small compared to other vision-language benchmarks;\n2. The authors set w=0.5 for VFS and α=0.7 for the Final Score “based on validation studies,” but details are limited in the paper;\n3. A single MLLM (Gemini‑2.5‑Pro) is used in both logical asserts verification and providing the visual score, which risks bias and errors.", "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)