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<!doctype html>
<html lang="en">
<head>
<title>OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation</title>
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</style>
</head>
<body>
<div class="header-wrapper">
<div class="header-container", id="header-container">
<div class="header-content">
<h1 style="font-size:3.2rem;">
<span class="onevl-name"><span class="o1">O</span><span class="n">n</span><span class="e">e</span><span class="v">V</span><span class="l">L</span></span>:
One-Step Latent Reasoning and Planning with Vision-Language Explanation
</h1>
<h2 style="margin:2.2rem 0 1.8rem; display:flex; align-items:center; gap:0.4rem;">
<!-- TODO: Replace with Xiaomi EI team logo if available -->
<span class="icon is-small">
<img src="assets/onevl/figures/xiaomi.png" style="height:1.8em; vertical-align:text-top;">
</span> Xiaomi Embodied Intelligence Team
</h2>
<div class="button-container">
<a href="#" class="button paper-link" target="_blank">
<span class="icon is-small">
<i class="ai ai-arxiv" style="height: 1.5em;"></i>
</span><span>arXiv</span>
</a>
<a href="#" class="button paper-link" target="_blank">
<span class="icon is-small">
<img src="assets_common/icons/pdf.png" style="height: 1.4em;">
</span><span>PDF</span>
</a>
<a href="#" class="button" target="_blank">
<span class="icon is-small">
<img src="assets_common/icons/github.png" style="height: 1.4em;">
</span><span>GitHub</span>
</a>
<a href="#" class="button" target="_blank">
<span class="icon is-small">
<img src="assets_common/icons/hf.png" style="height: 1.5em;">
</span><span>HuggingFace</span>
</a>
</div>
</div>
<div class="header-image">
<!-- TODO: Replace with OneVL teaser bar chart (Fig 1 from paper) -->
<img src="assets/onevl/figures/teaser.png" alt="Teaser Image" class="teaser-image">
</div>
</div>
</div>
<d-article>
<d-contents>
<nav>
<h4>Contents</h4>
<div><a href="#introduction">Introduction</a></div>
<div><a href="#architecture">Architecture</a></div>
<div><a href="#pipeline">Training Pipeline</a></div>
<div><a href="#results">Results</a></div>
<div><a href="#qualitative">Qualitative</a></div>
<div><a href="#examples">Detailed Examples</a></div>
<div><a href="#contributors">Contributors</a></div>
</nav>
</d-contents>
<!-- ==================== Introduction ==================== -->
<section id="introduction">
<div class="wb-section">
<div class="wb-kicker">
Can latent reasoning outperform explicit CoT?
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
Chain-of-Thought (CoT) reasoning has become a powerful driver of trajectory prediction in VLA-based autonomous driving, yet its autoregressive nature imposes a latency cost that is prohibitive for real-time deployment.
</p>
<p class="wb-lead">
Latent CoT methods compress reasoning into hidden states to reduce latency, but consistently underperform explicit CoT — because purely linguistic latents encode symbolic abstractions, not the causal dynamics that govern driving.
</p>
<figure style="width: 100%; max-width: 1000px; display: block; margin: 24px auto;">
<img src="assets/onevl/figures/paradigm.png" alt="Comparison of three CoT paradigms">
</figure>
<p class="wb-lead">
We argue that the compression target itself must capture genuine causal relationships. A latent vector that compresses only language is merely compressing an abstraction of the world, not the underlying physical structure.
</p>
<p class="wb-lead">
<span class="onevl-name"><span class="o1">O</span><span class="n">n</span><span class="e">e</span><span class="v">V</span><span class="l">L</span></span> addresses this with dual auxiliary decoders: a <b>language decoder</b> that reconstructs text CoT, and a <b>visual world model decoder</b> that predicts future-frame tokens — forcing the latent space to internalize causal scene dynamics rather than symbolic summaries.
</p>
<p class="wb-lead">
Across four benchmarks, <span class="onevl-name"><span class="o1">O</span><span class="n">n</span><span class="e">e</span><span class="v">V</span><span class="l">L</span></span> is the <b>first</b> latent CoT method to surpass explicit CoT, delivering state-of-the-art accuracy at answer-only latency.
</p>
<br><br>
</div>
</section>
<!-- ==================== Architecture ==================== -->
<section id="architecture">
<div class="wb-section">
<h2>Model Architecture</h2>
<!-- TODO: Replace with Fig 3 from paper (full architecture diagram) -->
<figure style="width: 100%; max-width: 1000px; display: block; margin: 24px auto;">
<img src="assets/onevl/figures/onevl.png" alt="OneVL Architecture">
</figure>
<div class="wb-kicker">
<span class="onevl-name"><span class="o1">O</span><span class="n">n</span><span class="e">e</span><span class="v">V</span><span class="l">L</span></span>: Dual-Modal Latent Reasoning
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
OneVL augments a pretrained VLM with a compact latent token interface and dual auxiliary decoders for multimodal explanation.
During inference, the auxiliary decoders are discarded and all latent tokens are prefilled in a single parallel pass, matching answer-only AR prediction latency.
</p>
<style>
#architecture .wb-grid {
grid-template-columns: repeat(3, minmax(0, 1fr)) !important;
max-width: 960px;
margin: 0 auto;
gap: 2rem 2.5rem;
}
#architecture .wb-grid .wb-card {
margin: 0.75rem 0;
}
@media (max-width: 1024px) {
#architecture .wb-grid { grid-template-columns: 1fr !important; }
}
</style>
<div class="wb-grid">
<article class="wb-card">
<h3>
<a class="wb-anchor">Dual Latent Tokens</a>
</h3>
<p>
<b>35 visual</b> + <b>20 language</b> latent tokens create a tight information bottleneck that forces the model to distill only the causal structure of the scene — discouraging memorization in favor of generalizable representations.
</p>
</article>
<article class="wb-card">
<h3>
<a class="wb-anchor">Language Auxiliary Decoder</a>
</h3>
<p>
Recovers <b>human-readable CoT text</b> from language latent states, grounding the bottleneck in semantic intent: scene interpretation, object analysis, and driving decisions.
</p>
</article>
<article class="wb-card">
<h3>
<a class="wb-anchor">Visual Auxiliary Decoder</a>
</h3>
<p>
Predicts <b>future-frame visual tokens</b> at +0.5s and +1.0s, acting as a <b>world model auxiliary</b> that grounds the bottleneck in physical scene dynamics — a causal compression target that language alone cannot supply.
</p>
</article>
</div>
<br><br><br>
</div>
</section>
<!-- ==================== Training Pipeline ==================== -->
<section id="pipeline">
<div class="wb-section">
<h2>Three-Stage Training Pipeline</h2>
<div class="wb-kicker">
Progressive Alignment for Stable Compression
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
Training OneVL presents a unique optimization challenge: the main VLM, the language auxiliary decoder, and the visual auxiliary decoder must all be jointly optimized, yet they have fundamentally different learning objectives.
A principled three-stage pipeline progressively aligns these components.
</p>
<div class="stage-grid">
<article class="wb-card">
<h3>
<span class="stage-num">0</span>
<a class="wb-anchor">Main Model Warmup</a>
</h3>
<p>
Train the main VLM end-to-end on trajectory prediction with latent tokens embedded in each training sample.
The model learns to develop meaningful latent representations and establish information routing pathways.
</p>
</article>
<article class="wb-card">
<h3>
<span class="stage-num">1</span>
<a class="wb-anchor">Auxiliary Decoder Warmup</a>
</h3>
<p>
Freeze the main model and train the auxiliary decoders to align with the stable latent representations.
The language decoder learns to decode CoT text; the visual decoder learns to predict future frames.
</p>
</article>
<article class="wb-card">
<h3>
<span class="stage-num">2</span>
<a class="wb-anchor">Joint End-to-End Fine-tuning</a>
</h3>
<p>
Jointly fine-tune all three model components.
Gradients from both decoders flow back into the main model, creating a virtuous cycle that tightens the information bottleneck from both sides.
</p>
</article>
</div>
<br><br><br>
</div>
</section>
<!-- ==================== Results ==================== -->
<section id="results">
<div class="wb-section">
<h2>Key Results</h2>
<div class="wb-kicker">
State-of-the-Art Across Benchmarks
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
OneVL achieves state-of-the-art performance across NAVSIM, ROADWork, Impromptu, and APR1 with a <b>4B parameter model</b>, surpassing prior 8B methods.
Prefill inference matches answer-only prediction speed, and an MLP variant reaches <b>0.24s latency (4.16 Hz)</b> for real-world deployment.
</p>
<div class="results-grid">
<article class="wb-card result-card">
<div class="result-benchmark">NAVSIM</div>
<div class="result-value">88.84</div>
<div class="result-label">PDM-score</div>
</article>
<article class="wb-card result-card">
<div class="result-benchmark">ROADWork</div>
<div class="result-value">12.49</div>
<div class="result-label">ADE (pixels)</div>
</article>
<article class="wb-card result-card">
<div class="result-benchmark">Impromptu</div>
<div class="result-value">1.31</div>
<div class="result-label">ADE (meters)</div>
</article>
<article class="wb-card result-card">
<div class="result-benchmark">APR1</div>
<div class="result-value">2.62</div>
<div class="result-label">ADE (meters)</div>
</article>
</div>
<br><br><br>
</div>
</section>
<!-- ==================== Qualitative Examples ==================== -->
<section id="qualitative">
<div class="wb-section">
<div class="wb-kicker">
Trajectory Prediction & Interpretable Explanations
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
OneVL provides human-interpretable explanations in both language and vision.
The language auxiliary decoder recovers high-quality CoT text from compressed latents, while the visual auxiliary decoder generates spatially coherent future-frame previews.
</p>
<br>
<style>
.onevl-video-item {
max-width: 900px;
margin: 0 auto 2.5rem;
}
.onevl-video-item video {
width: 100%;
height: auto;
display: block;
border-radius: 0;
background: transparent;
}
.onevl-video-item p.onevl-video-caption {
text-align: center;
font-size: 0.92rem;
color: #6b7280;
margin: 0 0 0.3rem !important;
padding: 0;
font-family: "Rubik", sans-serif;
}
.onevl-video-item video {
margin-bottom: 0;
}
</style>
<div class="onevl-video-item">
<p class="onevl-video-caption">NAVSIM video log: OneVL trajectory prediction with multi-view and BEV visualization.</p>
<video autoplay loop muted playsinline>
<source src="assets/onevl/videos/demo1.mp4" type="video/mp4" />
</video>
</div>
<div class="onevl-video-item">
<p class="onevl-video-caption">NAVSIM video log: OneVL trajectory prediction with multi-view and BEV visualization.</p>
<video autoplay loop muted playsinline>
<source src="assets/onevl/videos/demo2.mp4" type="video/mp4" />
</video>
</div>
<div class="onevl-video-item">
<p class="onevl-video-caption">NAVSIM video log: OneVL trajectory prediction with multi-view and BEV visualization.</p>
<video autoplay loop muted playsinline>
<source src="assets/onevl/videos/demo3.mp4" type="video/mp4" />
</video>
</div>
<div class="wb-kicker">
Accuracy & Efficiency Across Benchmarks
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
OneVL consistently achieves the best accuracy across all four benchmarks while matching answer-only prediction latency. Existing latent CoT methods (COCONUT, CODI, SIM-CoT) underperform even the AR baseline, whereas OneVL surpasses explicit AR CoT at a fraction of the inference cost.
</p>
<figure style="width: 100%; max-width: 1000px; display: block; margin: 24px auto;">
<img src="assets/onevl/figures/benchmark.png" alt="Accuracy and efficiency comparison across four benchmarks">
</figure>
<br>
<p class="wb-lead">
On NAVSIM, OneVL achieves <b>88.84</b> PDM-score with a 4B model, surpassing 8B methods AdaThinkDrive (86.20) and LaST-VLA (87.30). Prefill inference reaches <b>4.46s</b> latency — matching answer-only prediction (4.49s) while being 32% faster than explicit AR CoT (6.58s).
</p>
<br>
<style>
.onevl-table-wrap {
max-width: 900px;
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}
.onevl-table {
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border-collapse: collapse;
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font-size: 0.92rem;
line-height: 1.5;
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.onevl-table th,
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</style>
<div class="onevl-table-wrap">
<p class="onevl-table-caption"><b>Performance comparisons on the NAVSIM benchmark.</b> PDM-score (higher is better) and average inference latency (lower is better). * indicates the result is derived from the corresponding paper.</p>
<table class="onevl-table">
<thead>
<tr>
<th>Method</th>
<th>Model Size</th>
<th>PDM-score ↑</th>
<th>Latency (s) ↓</th>
<th>Interpretability</th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="5">Previous State-of-the-Art</td></tr>
<tr>
<td>AdaThinkDrive</td>
<td>8B</td>
<td>86.20*</td>
<td>—</td>
<td>Language</td>
</tr>
<tr>
<td>LaST-VLA</td>
<td>8B</td>
<td>87.30*</td>
<td>—</td>
<td>—</td>
</tr>
<tr class="group-header"><td colspan="5">AR-based Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>AR Answer</td>
<td>4B</td>
<td>87.47</td>
<td class="underline">4.49</td>
<td>—</td>
</tr>
<tr>
<td>AR CoT+Answer</td>
<td>4B</td>
<td class="underline">88.29</td>
<td>6.58</td>
<td>Language</td>
</tr>
<tr class="group-header"><td colspan="5">Latent CoT Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>COCONUT</td>
<td>4B</td>
<td>84.84</td>
<td>5.93</td>
<td>—</td>
</tr>
<tr>
<td>CODI</td>
<td>4B</td>
<td>83.92</td>
<td>8.62</td>
<td>—</td>
</tr>
<tr>
<td>SIM-CoT</td>
<td>4B</td>
<td>84.21</td>
<td>10.86</td>
<td>Language</td>
</tr>
<tr class="row-ours">
<td><b>OneVL</b></td>
<td>4B</td>
<td class="best">88.84</td>
<td class="best">4.46</td>
<td>Vision + Language</td>
</tr>
</tbody>
</table>
</div>
<br>
<p class="wb-lead">
On ROADWork, OneVL achieves <b>12.49</b> ADE and <b>28.80</b> FDE (pixels), significantly outperforming the previous SOTA YNet (22.68 / 80.78) and all latent CoT baselines. Inference latency is <b>4.71s</b> — faster than answer-only prediction and over 2x faster than explicit AR CoT (10.74s).
</p>
<br>
<div class="onevl-table-wrap">
<p class="onevl-table-caption"><b>Performance comparisons on the ROADWork benchmark.</b> ADE and FDE (pixels; lower is better), latency (lower is better). * indicates the result is derived from the corresponding paper.</p>
<table class="onevl-table">
<thead>
<tr>
<th>Method</th>
<th>ADE (pixel) ↓</th>
<th>FDE (pixel) ↓</th>
<th>Latency (s) ↓</th>
<th>Interpretability</th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="5">Previous State-of-the-Art</td></tr>
<tr>
<td>YNet</td>
<td>22.68*</td>
<td>80.78*</td>
<td>—</td>
<td>—</td>
</tr>
<tr class="group-header"><td colspan="5">AR-based Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>AR Answer</td>
<td>15.98</td>
<td>40.29</td>
<td class="underline">4.74</td>
<td>—</td>
</tr>
<tr>
<td>AR CoT+Answer</td>
<td class="underline">13.18</td>
<td class="underline">29.98</td>
<td>10.74</td>
<td>Language</td>
</tr>
<tr class="group-header"><td colspan="5">Latent CoT Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>COCONUT</td>
<td>15.44</td>
<td>38.60</td>
<td class="underline">6.06</td>
<td>—</td>
</tr>
<tr>
<td>CODI</td>
<td>16.45</td>
<td>44.28</td>
<td>6.73</td>
<td>—</td>
</tr>
<tr>
<td>SIM-CoT</td>
<td>16.49</td>
<td>44.32</td>
<td>6.19</td>
<td>Language</td>
</tr>
<tr class="row-ours">
<td><b>OneVL</b></td>
<td class="best">12.49</td>
<td class="best">28.80</td>
<td class="best">4.71</td>
<td>Vision + Language</td>
</tr>
</tbody>
</table>
</div>
<br>
<p class="wb-lead">
On Impromptu, OneVL achieves <b>1.34</b> ADE and <b>3.70</b> FDE (meters), outperforming both Impromptu VLA (1.60 / 4.28) and explicit AR CoT (1.42 / 3.96). Latency is <b>4.02s</b> — faster than answer-only prediction and 41% faster than AR CoT (6.84s).
</p>
<br>
<div class="onevl-table-wrap">
<p class="onevl-table-caption"><b>Performance comparisons on the Impromptu benchmark.</b> ADE and FDE (meters; lower is better), latency (lower is better).</p>
<table class="onevl-table">
<thead>
<tr>
<th>Method</th>
<th>ADE (m) ↓</th>
<th>FDE (m) ↓</th>
<th>Latency (s) ↓</th>
<th>Interpretability</th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="5">Previous State-of-the-Art</td></tr>
<tr>
<td>Impromptu VLA</td>
<td>1.60</td>
<td>4.28</td>
<td>6.10</td>
<td>—</td>
</tr>
<tr class="group-header"><td colspan="5">AR-based Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>AR Answer</td>
<td>1.46</td>
<td>4.03</td>
<td class="underline">4.24</td>
<td>—</td>
</tr>
<tr>
<td>AR CoT+Answer</td>
<td class="underline">1.42</td>
<td class="underline">3.96</td>
<td>6.84</td>
<td>Language</td>
</tr>
<tr class="group-header"><td colspan="5">Latent CoT Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>COCONUT</td>
<td>1.49</td>
<td>4.07</td>
<td>5.27</td>
<td>—</td>
</tr>
<tr>
<td>CODI</td>
<td>1.86</td>
<td>5.18</td>
<td>5.24</td>
<td>—</td>
</tr>
<tr>
<td>SIM-CoT</td>
<td>2.43</td>
<td>6.10</td>
<td>5.09</td>
<td>Language</td>
</tr>
<tr class="row-ours">
<td><b>OneVL</b></td>
<td class="best">1.34</td>
<td class="best">3.70</td>
<td class="best">4.02</td>
<td>Vision + Language</td>
</tr>
</tbody>
</table>
</div>
<br>
<p class="wb-lead">
On APR1, OneVL achieves <b>2.62</b> ADE (meters), the best among all methods, and <b>7.53</b> FDE, competitive with Cosmos-Reason (7.42) which uses RL-based fine-tuning. Latency is <b>3.26s</b>, faster than all latent CoT baselines.
</p>
<br>
<div class="onevl-table-wrap">
<p class="onevl-table-caption"><b>Performance comparisons on the APR1 benchmark.</b> ADE and FDE (meters; lower is better), latency (lower is better).</p>
<table class="onevl-table">
<thead>
<tr>
<th>Method</th>
<th>ADE (m) ↓</th>
<th>FDE (m) ↓</th>
<th>Latency (s) ↓</th>
<th>Interpretability</th>
</tr>
</thead>
<tbody>
<tr class="group-header"><td colspan="5">Previous State-of-the-Art</td></tr>
<tr>
<td>Cosmos-Reason</td>
<td class="underline">2.86</td>
<td class="best">7.42</td>
<td>—</td>
<td>Language</td>
</tr>
<tr class="group-header"><td colspan="5">AR-based Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>AR Answer</td>
<td>3.27</td>
<td>9.59</td>
<td>3.06</td>
<td>—</td>
</tr>
<tr>
<td>AR CoT+Answer</td>
<td>2.99</td>
<td>8.54</td>
<td>3.51</td>
<td>Language</td>
</tr>
<tr class="group-header"><td colspan="5">Latent CoT Baselines (4B, Qwen3-VL)</td></tr>
<tr>
<td>COCONUT</td>
<td>3.29</td>
<td>9.48</td>
<td>3.76</td>
<td>—</td>
</tr>
<tr>
<td>CODI</td>
<td>3.22</td>
<td>9.25</td>
<td>3.85</td>
<td>—</td>
</tr>
<tr>
<td>SIM-CoT</td>
<td>3.40</td>
<td>9.85</td>
<td>3.78</td>
<td>Language</td>
</tr>
<tr class="row-ours">
<td><b>OneVL</b></td>
<td class="best">2.62</td>
<td class="underline">7.53</td>
<td class="best">3.26</td>
<td>Vision + Language</td>
</tr>
</tbody>
</table>
</div>
<br><br><br><br>
</div>
</section>
<!-- ==================== Detailed Examples ==================== -->
<section id="examples">
<div class="wb-section">
<div class="wb-kicker">
Trajectory, Future Frames & Reasoning
</div>
<div class="onevl-hr"></div>
<p class="wb-lead">
Each panel compares the AR baseline and OneVL side by side: front-view trajectory overlays, bird's-eye-view (BEV) plans, predicted future frames (T+1, T+2), and the decoded chain-of-thought reasoning.
</p>
<style>
.qual-panel {
max-width: 960px;
margin: 1.5rem auto;
border: 1px solid #e5e7eb;
border-radius: 14px;
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}
</style>
<div class="qual-panel">
<!-- Row 1: Baseline -->
<div class="qual-row-header two-col">
<span>Baseline Front View</span>
<span>Baseline BEV</span>
</div>
<div class="qual-row two-col">
<div class="qual-cell">
<img src="assets/onevl/figures/example1/baseline_front_view.jpg" alt="Baseline Front View">
</div>
<div class="qual-cell">
<img src="assets/onevl/figures/example1/baseline_bev.png" alt="Baseline BEV">
</div>
</div>
<!-- Row 2: OneVL -->
<div class="qual-row-header two-col">
<span>OneVL Front View</span>
<span>OneVL BEV</span>
</div>
<div class="qual-row two-col">
<div class="qual-cell">
<img src="assets/onevl/figures/example1/onevl_front_view.jpg" alt="OneVL Front View">
</div>
<div class="qual-cell">
<img src="assets/onevl/figures/example1/onevl_bev.png" alt="OneVL BEV">
</div>
</div>
<!-- Row 3: Future frames + Reasoning -->
<div class="qual-row-header" style="display:grid; grid-template-columns: 1fr 1fr;">
<span>OneVL T+1</span>
<span>OneVL T+2</span>
</div>
<div class="qual-row" style="display:grid; grid-template-columns: 1fr 1fr;">
<div class="qual-cell">
<img src="assets/onevl/figures/example1/onevl_t+1.jpg" alt="OneVL T+1 Future Frame">
</div>
<div class="qual-cell">
<img src="assets/onevl/figures/example1/onevl_t+2.jpg" alt="OneVL T+2 Future Frame">
</div>
</div>
<!-- Row 4: Reasoning -->
<div class="qual-row-header" style="display:block; text-align:center;">
<span>OneVL Reasoning</span>
</div>
<div class="qual-row" style="display:block;">
<div class="qual-cell" style="border-right:none;">
<div class="qual-reasoning">
<b>CoT:</b> The right side of the lane where ego vehicle is located is close to the undrivable area, so I need to drive slightly to the left. There are no objects in the current scene that I need to pay attention to. Based on the understanding of the scene and the navigation information, the ego should maintain speed and turn left.
</div>
</div>
</div>
</div>
<br><br><br><br>
</div>
</section>
<!-- ==================== Contributors ==================== -->
<section id="contributors">
<div class="wb-section">
<h2>Contributors</h2>
<p class="wb-lead">
<span class="onevl-name"><span class="o1">O</span><span class="n">n</span><span class="e">e</span><span class="v">V</span><span class="l">L</span></span> is developed by the <img src="assets/onevl/figures/xiaomi.png" style="height:1.4em; vertical-align:text-bottom; margin: 0 0.15em;"> <b>Xiaomi Embodied Intelligence Team</b>.
</p>
<div class="wb-kicker" style="font-size:1.1rem;">Core Contributors</div>
<div class="onevl-hr"></div>
<div class="contributors-name-list">
<span>Jinghui Lu</span>
<span>Jiayi Guan</span>
<span>Zhijian Huang</span>
<span>Jinlong Li</span>
<span>Guang Li</span>
<span>Lingdong Kong</span>
<span>Yingyan Li</span>
<span>Han Wang</span>
<span>Shaoqing Xu</span>
<span>Yuechen Luo</span>
<span>Fang Li</span>
<span>Chenxu Dang</span>
<span>Junli Wang</span>
<span>Tao Xu</span>
<span>Jing Wu</span>
<span>Jianhua Wu</span>
<span>Xiaoshuai Hao</span>
<span>Wen Zhang</span>
<span>Tianyi Jiang</span>
<span>Kuiyuan Yang</span>
<span>Hangjun Ye</span>
<span>Long Chen<sup>†</sup></span>
</div>
<p style="font-size:0.82rem; color:#9ca3af; margin-top:0.3rem; font-family:'Rubik',sans-serif;">Note: <sup>†</sup> Corresponding Author</p>
<br>
<div class="wb-kicker" style="font-size:1.1rem;">Contributors</div>
<div class="onevl-hr"></div>
<div class="contributors-name-list">