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| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| <title>SPLIT-VLM — A Reproduction (ICML 2026)</title> |
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| <div class="vb-venue">ICML</div> |
| <div class="vb-year">2026</div> |
| <div class="vb-tag">REPRO</div> |
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| <div class="title-block"> |
| <h1 class="title">SPLIT-VLM: <span class="accent">Attention-Free</span> Vision-Token Dropping <span class="tag-key fs-3">[ A Reproduction ]</span></h1> |
| <div class="subtitle">Reproducing salience-guided partitioning for importance-aware token dropping in VLMs (OpenReview Elm4TdaXi0)</div> |
| <div class="authors-line"> |
| <span class="author">Reproduction by Claude (Anthropic)<sup>✉</sup></span> |
| <span class="aff">Original: Seungil Lee, Gilha Lee, Hyun Kim · Seoul Nat. Univ. of Science & Technology</span> |
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| <img data-color-exempt="logo" src="assets/qr.png" alt="QR code linking to the reproduction collection on Hugging Face"> |
| <div class="qr-label">Artifacts & Code</div> |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="methodology-setup" data-logbook-label="Method & setup"> |
| <div class="section-title"><span class="num">1</span><span class="st-text">The Paper & SPLIT</span></div> |
| <p class="body-text"> |
| Prior token dropping trades <span class="keyword">salience</span> (attention — position-biased) against |
| <span class="keyword">diversity</span> (coverage-unstable). <strong>SPLIT</strong> is training-free and unifies both: |
| </p> |
| <div class="eqn"> |
| <span class="label">1. Temporal-shift importance (attention-free)</span> |
| $$I(x)=\tfrac{1}{|L|}\!\sum_{\ell\in L}\frac{\lVert h_\ell(x)-h_{\ell-1}(x)\rVert_2}{\lVert h_\ell(x)\rVert_2}$$ |
| </div> |
| <div class="eqn"> |
| <span class="label">2. Adaptive region budget (K=16 regions)</span> |
| $$\tilde B_k=\tfrac{B}{K}+B\cdot\frac{I(X_k)}{\sum_j I(X_j)}$$ |
| </div> |
| <div class="eqn"> |
| <span class="label">3. Diversity selection ($\lambda{=}0.5$), keep top-$\tilde B_k$/region</span> |
| $$D(i)=\lambda\,\sigma_i-\mu_i,\quad S_{ij}=\langle\hat x_i,\hat x_j\rangle$$ |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-3-temporal-shift-importance-attention-free" data-logbook-label="Claim 3 details"> |
| <div class="section-title"><span class="num">2</span><span class="st-text">Claim 3 ✓ Reproduced <span class="tag-key">★ KEY</span></span></div> |
| <div class="figure"> |
| <img src="assets/fig_mechanism_heatmaps.png" data-source="paper" data-asset-id="mech" class="w-75" alt="Temporal-shift vs attention heatmaps over the patch grid"> |
| <div class="caption fs-2"> |
| Temporal-shift importance is <strong>spread</strong>; CLS attention <strong>collapses</strong> onto a few low-row tokens (position bias). SPLIT covers all 16 regions. |
| </div> |
| </div> |
| <div class="callout gold"> |
| <strong>Attention-free & unbiased:</strong> center-of-mass row 11.5 (=center) vs attention 12.7–13.6; Spearman(imp, attn) ≈ −0.7. |
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| <div class="section-title"><span class="num">3</span><span class="st-text">Claim 1 ≈ Trend reproduced <span class="tag-key fs-3">★</span></span></div> |
| <p class="body-text text-secondary fs-3 mb-1"> |
| LLaVA-1.5-7B (MPS); POPE 150, TextVQA 60, ScienceQA 60; 576→192/128/64. |
| </p> |
| <div class="figure"> |
| <img src="assets/fig_relative_accuracy.png" data-source="paper" data-asset-id="results" class="w-90" alt="Relative accuracy vs token budget and TextVQA robustness"> |
| <div class="caption fs-2"> |
| <strong>Left:</strong> SPLIT degrades gracefully (~92→88%), above random. <strong>Right:</strong> on OCR TextVQA, random collapses at 64 tokens; SPLIT holds. |
| </div> |
| </div> |
| <table class="result-table"> |
| <thead> |
| <tr><th class="method">Avg rel. acc. (%)</th><th>192</th><th>128</th><th>64</th></tr> |
| </thead> |
| <tbody> |
| <tr class="ref"><td class="method"><em>SPLIT (paper, 10 bench)</em></td><td>99.3</td><td>97.8</td><td>92.8</td></tr> |
| <tr class="ours"><td class="method">SPLIT (ours, 3 bench)</td><td class="best">92.4</td><td class="best">89.3</td><td class="best">87.9</td></tr> |
| <tr><td class="method">random (ours)</td><td>88.3</td><td>82.7</td><td>71.5</td></tr> |
| </tbody> |
| </table> |
| <p class="body-text mt-2 fs-3">SPLIT ≫ random; Claim 2's sub-2% SOTA margins sit <strong>below subset noise</strong>.</p> |
| </div> |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="conclusion" data-logbook-label="Conclusion & scope"> |
| <div class="section-title"><span class="num">5</span><span class="st-text">Takeaways <span class="tag-key fs-3">★</span></span></div> |
| <ul> |
| <li><strong>Claim 3 ✓</strong> attention-free, unbiased, coverage-preserving.</li> |
| <li><strong>Claim 1 ≈</strong> graceful degradation reproduced; absolute lower.</li> |
| <li><strong>Claim 2 –</strong> not resolvable at this scale; SPLIT clearly beats random dropping.</li> |
| <li><strong>Cost.</strong> Apple M1 Pro (MPS), ~1.5 h, <strong>$0</strong> — HF Jobs was blocked (402).</li> |
| </ul> |
| <div class="callout gold mt-4"> |
| <strong>In one sentence:</strong> the attention-free mechanism reproduces cleanly; the headline accuracy needs the full 10-benchmark GPU suite. |
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| <div> |
| <strong class="method-name">SPLIT-VLM</strong> · ICML 2026 (Elm4TdaXi0) · |
| independent reproduction, training-free token dropping on LLaVA-1.5-7B. |
| </div> |
| <div> |
| Artifacts: <span class="repo">hf.co/datasets/kpshinnik/split-vlm-repro-bundle</span> · |
| Paper: <span class="repo">openreview.net/forum?id=Elm4TdaXi0</span> |
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| <div class="ornament">LAB · INSTITUTION</div> |
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