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| <html lang="en"> |
| <head> |
| <meta charset="UTF-8"> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> |
| <meta name="generator" content="posterly"> |
| <title>Flatness-Aware SGLD — Reproduction — ICML 2026</title> |
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| border: calc(2 * var(--u)) solid var(--accent); |
| border-radius: calc(4 * var(--u) * var(--rs)); |
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| padding: calc(2 * var(--u)); |
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| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--accent); |
| font-weight: 600; |
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| display: inline-flex; align-items: center; justify-content: center; |
| background: var(--bg-card); border-radius: calc(3 * var(--u) * var(--rs)); |
| padding: calc(3 * var(--u)) calc(5 * var(--u)); |
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| .logo-chip.logo-chip-dark { background: var(--text-primary); } |
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| .logo-row img { height: calc(68 * var(--u)); width: auto; display: block; } |
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| display: flex; flex-direction: column; align-items: center; |
| gap: calc(2 * var(--u)); |
| background: var(--bg-card); |
| border: 1px solid var(--border-soft); |
| border-radius: calc(3 * var(--u) * var(--rs)); |
| padding: calc(4 * var(--u)) calc(6 * var(--u)); |
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| .logo-row .lr-item img { height: calc(58 * var(--u)); } |
| .logo-row .lr-label { |
| font-family: var(--font-sans); font-weight: 600; font-size: var(--fs-1); |
| color: var(--text-secondary); text-align: center; line-height: 1.15; |
| } |
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| .logo-row.logo-stack { flex-direction: column; align-items: flex-start; gap: calc(8 * var(--u)); } |
| .logo-row.logo-stack img { width: calc(170 * var(--u)); height: auto; } |
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| .venue-badge .vb-title { font-family: var(--font-sans); font-weight: 800; font-size: var(--fs-5); color: var(--accent-deep); letter-spacing: 0.5px; } |
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| display: flex; |
| align-items: center; |
| gap: calc(16 * var(--u)); |
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| background: var(--bg-emphasis); |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| border-left: calc(6 * var(--u)) solid var(--accent); |
| border-radius: calc(6 * var(--u) * var(--rs)); |
| padding: calc(6 * var(--u)) calc(14 * var(--u)); |
| } |
| .framework-banner img { height: calc(120 * var(--u)); width: auto; display: block; } |
| .framework-banner .banner-stats { |
| flex: 1; |
| display: grid; |
| grid-template-columns: 1fr 1fr; |
| gap: calc(6 * var(--u)); |
| } |
| .framework-banner .bs-item { |
| background: white; |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| border-left: calc(3 * var(--u)) solid var(--accent); |
| border-radius: calc(3 * var(--u) * var(--rs)); |
| padding: calc(2 * var(--u)) calc(8 * var(--u)); |
| text-align: center; |
| } |
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| font-family: var(--font-sans); |
| font-weight: 800; |
| font-size: var(--fs-8); |
| color: var(--accent); |
| line-height: 1; |
| } |
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| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--text-secondary); |
| margin-top: calc(2 * var(--u)); |
| line-height: 1.2; |
| } |
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| flex: 1.6; |
| font-family: var(--font-serif); |
| font-size: var(--fs-6); |
| line-height: 1.5; |
| text-wrap: pretty; |
| text-align: center; |
| } |
| .framework-banner .fb-text strong { color: var(--accent-deep); } |
| .framework-banner .fb-label { |
| display: inline-block; |
| background: var(--accent); |
| color: var(--accent-ink); |
| font-family: var(--font-sans); |
| font-size: var(--fs-5); |
| font-weight: 700; |
| padding: calc(2 * var(--u)) calc(8 * var(--u)); |
| border-radius: calc(4 * var(--u) * var(--rs)); |
| text-transform: uppercase; |
| letter-spacing: 1px; |
| vertical-align: middle; |
| line-height: 1; |
| position: relative; |
| top: calc(-1 * var(--u)); |
| } |
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| .framework-banner .banner-figure { |
| flex: 0 0 auto; |
| width: min-content; |
| margin: 0; |
| text-align: center; |
| } |
| .framework-banner .banner-figure img { |
| height: calc(120 * var(--u)); |
| width: auto; |
| display: block; |
| margin-inline: auto; |
| } |
| .framework-banner .banner-figure figcaption { |
| width: 100%; |
| margin-top: calc(2 * var(--u)); |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| line-height: 1.2; |
| color: var(--text-secondary); |
| text-align: center; |
| text-wrap: pretty; |
| overflow-wrap: anywhere; |
| } |
| .framework-banner .banner-figure figcaption strong { color: var(--accent-deep); } |
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| .body-grid { |
| display: grid; |
| grid-template-columns: 1fr 1.05fr 1.05fr 1fr; |
| gap: calc(10 * var(--u)); |
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| min-height: 0; |
| } |
| .column { |
| display: flex; flex-direction: column; |
| gap: calc(6 * var(--u)); |
| min-height: 0; |
| height: 100%; |
| padding-bottom: calc(4 * var(--u)); |
| } |
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| .card { |
| background: var(--bg-card); |
| border-radius: calc(5 * var(--u) * var(--rs)); |
| padding: calc(4 * var(--u)) calc(9 * var(--u)); |
| border: calc(1 * var(--u)) solid var(--border-soft); |
| box-shadow: var(--shadow-card); |
| position: relative; |
| } |
| .card.tinted { background: var(--bg-card-tint); } |
| .card.card--compact { padding: calc(3 * var(--u)) calc(6 * var(--u)); } |
| .card.highlight { |
| border-left: calc(6 * var(--u)) solid var(--accent); |
| |
| background: var(--bg-emphasis); |
| } |
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| .section-title { |
| font-family: var(--font-sans); |
| font-weight: 700; |
| font-size: var(--fs-7); |
| color: var(--accent-deep); |
| margin-bottom: calc(3 * var(--u)); |
| display: flex; align-items: center; |
| gap: calc(5 * var(--u)); |
| } |
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| .section-title .st-text { flex: 1; min-width: 0; line-height: 1.18; } |
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| .section-title:not(:has(.st-text)) { display: block; line-height: 1.18; } |
| .section-title:not(:has(.st-text)) .num { float: left; margin-right: calc(5 * var(--u)); } |
| .section-title .num { |
| display: inline-flex; align-items: center; justify-content: center; |
| width: calc(22 * var(--u)); height: calc(22 * var(--u)); |
| background: var(--accent); color: var(--accent-ink); |
| border-radius: 50%; |
| font-size: var(--fs-5); font-weight: 700; |
| flex-shrink: 0; |
| } |
| |
| .section-title .key-mark { color: var(--emph); font-size: var(--fs-3); } |
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| .body-text, .card p, .card li { |
| font-family: var(--font-serif); |
| font-size: var(--fs-4); |
| line-height: 1.3; |
| color: var(--text-primary); |
| } |
| .card ul, .card ol { padding-left: calc(18 * var(--u)); } |
| .card li { margin-bottom: calc(2 * var(--u)); } |
| |
| .keyword { color: var(--accent); font-weight: 700; } |
| .keyword-emph { color: var(--emph); font-weight: 700; } |
| .highlight-text { |
| background: var(--bg-emphasis); |
| padding: 0 calc(3 * var(--u)); |
| border-radius: calc(2 * var(--u) * var(--rs)); |
| } |
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| |
| .eqn { |
| background: var(--bg-emphasis); |
| border-left: calc(3 * var(--u)) solid var(--accent); |
| padding: calc(4 * var(--u)) calc(10 * var(--u)); |
| margin: calc(4 * var(--u)) 0; |
| font-size: var(--fs-5); |
| overflow-x: hidden; |
| } |
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| .eqn--large { font-size: calc(var(--fs-5) * 1.25); } |
| .eqn .label { |
| display: block; |
| font-family: var(--font-sans); |
| font-size: var(--fs-2); |
| color: var(--accent); |
| font-weight: 600; |
| margin-bottom: calc(2 * var(--u)); |
| text-transform: uppercase; |
| letter-spacing: 1px; |
| } |
| |
| |
| .callout { |
| background: var(--accent); |
| color: var(--accent-ink); |
| padding: calc(5 * var(--u)) calc(10 * var(--u)); |
| border-radius: calc(4 * var(--u) * var(--rs)); |
| font-size: var(--fs-4); |
| margin: calc(4 * var(--u)) 0; |
| } |
| .callout strong { color: var(--emph); } |
| |
| .callout.emph { |
| background: var(--emph); |
| color: var(--emph-ink); |
| } |
| .callout.emph strong { color: var(--emph-ink); } |
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| |
| .figure { margin: calc(4 * var(--u)) 0; text-align: center; } |
| .figure img:not([class*="w-"]) { width: 100%; } |
| .figure--wide img { width: 100%; } |
| .figure img { |
| border-radius: calc(4 * var(--u) * var(--rs)); |
| border: calc(1 * var(--u)) solid var(--fig-frame); |
| background: var(--fig-bg); |
| } |
| .figure .caption { |
| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--text-secondary); |
| margin-top: calc(3 * var(--u)); |
| line-height: 1.3; |
| text-align: left; |
| } |
| .figure .caption strong { color: var(--accent-deep); } |
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| .fig-wrap::after { content: ""; display: table; clear: both; } |
| .ff-fig { |
| float: right; |
| width: 48%; max-width: 58%; min-width: 38%; |
| margin: calc(1 * var(--u)) 0 calc(3 * var(--u)) calc(11 * var(--u)); |
| text-align: center; |
| } |
| .ff-fig.left { |
| float: left; |
| margin: calc(1 * var(--u)) calc(11 * var(--u)) calc(3 * var(--u)) 0; |
| } |
| .ff-fig img { |
| display: block; |
| width: 100%; |
| border-radius: calc(4 * var(--u) * var(--rs)); |
| border: calc(1 * var(--u)) solid var(--fig-frame); |
| background: var(--fig-bg); |
| } |
| .ff-fig .caption { |
| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| color: var(--text-secondary); |
| margin-top: calc(3 * var(--u)); |
| line-height: 1.3; |
| text-align: center; |
| } |
| |
| |
| .result-table { |
| width: 100%; |
| border-collapse: collapse; |
| font-family: var(--font-sans); |
| font-size: var(--fs-3); |
| margin-top: calc(3 * var(--u)); |
| } |
| .result-table th, .result-table td { |
| padding: calc(2 * var(--u)) calc(4 * var(--u)); |
| text-align: center; |
| border-bottom: calc(1 * var(--u)) solid var(--border-soft); |
| } |
| .result-table thead th { |
| background: var(--accent); color: var(--accent-ink); |
| font-weight: 600; font-size: var(--fs-2); |
| } |
| .result-table tbody tr.group-row td { |
| background: var(--bg-emphasis); font-weight: 700; |
| text-align: left; |
| color: var(--accent-deep); |
| padding-left: calc(8 * var(--u)); |
| border-bottom: calc(2 * var(--u)) solid var(--accent); |
| } |
| .result-table tbody tr.ours td { background: var(--emph-soft); font-weight: 700; } |
| .result-table tbody tr.ours td:first-child { color: var(--accent-deep); } |
| |
| .result-table tbody tr.reference td { color: var(--text-muted); } |
| .result-table .method { text-align: left; padding-left: calc(8 * var(--u)); } |
| .result-table .best { color: var(--accent); font-weight: 700; } |
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| |
| .keybox { |
| display: grid; |
| grid-template-columns: repeat(3, 1fr); |
| gap: calc(4 * var(--u)); |
| margin: calc(4 * var(--u)) 0 0; |
| } |
| .keybox .kb-item { |
| background: var(--bg-emphasis); |
| border-top: calc(2 * var(--u)) solid var(--accent); |
| padding: calc(3 * var(--u)); |
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| <div class="venue-badge"> |
| <div class="vb-venue">ICML</div> |
| <div class="vb-year">2026</div> |
| <div class="vb-tag">REPRODUCTION</div> |
| </div> |
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| <div class="title-block"> |
| <h1 class="title">Flatness-Aware SGLD: <span class="accent">Reproduction</span></h1> |
| <div class="subtitle">Theory verified to machine precision; the per-step efficiency claim reproduces; the large-scale accuracy & flatness gains are not visible at CPU-affordable reduced scale.</div> |
| <div class="authors-line"> |
| <span class="author">Independent reproduction agent (Claude Code)</span> · |
| <span class="author">ICML 2026 Reproducibility Challenge</span> |
| <span class="aff">Paper: arXiv 2510.02174 · OpenReview Kw0nhxeTeI · CPU-only (numpy + Modal 32-core)</span> |
| </div> |
| </div> |
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| |
| <div class="right-block"> |
| <div class="qr-block"> |
| <img data-color-exempt="logo" src="qr.png" alt="QR code linking to the published Trackio reproduction logbook"> |
| <div class="qr-label">Full logbook</div> |
| </div> |
| </div> |
| </header> |
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| <section class="framework-banner" data-measure-role="banner"> |
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| <div class="fb-text"> |
| <span class="fb-label">CLAIM SCORECARD</span> |
| <strong>fSGLD</strong> = SGLD on the smoothing surrogate $g_\sigma$; the coupling $\sigma=\beta^{-(1+\eta)/4}$ makes its invariant measure target the Hessian-trace regularized Gibbs law, biasing toward flat minima at one gradient per step. We audit all three theorems exactly, and run all three experiments at the largest reduced scale the CPU-only budget allows, reporting each verdict on its own claim page. |
| </div> |
| <div class="banner-stats"> |
| <div class="bs-item"><div class="bs-num">3/3</div><div class="bs-label">theorems<br>verified</div></div> |
| <div class="bs-item"><div class="bs-num">1.92×</div><div class="bs-label">SAM cost<br>vs fSGLD ✓</div></div> |
| <div class="bs-item"><div class="bs-num">4.00</div><div class="bs-label">O(σ⁴) slope<br>(machine prec.)</div></div> |
| <div class="bs-item"><div class="bs-num">CPU</div><div class="bs-label">no GPU<br>6s + 4.5h</div></div> |
| </div> |
| </section> |
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| <div class="body-grid" data-measure-role="body"> |
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| <div class="column" data-measure-role="column"> |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="executive-summary"> |
| <div class="section-title"><span class="num">1</span><span class="st-text">What was reproduced</span></div> |
| <p class="body-text"> |
| <span class="keyword">fSGLD</span> runs SGLD on the randomized-smoothing surrogate $g_\sigma(\theta)=\mathbb{E}_\varepsilon[u(\theta+\varepsilon)]$. Since $g_\sigma = u + \tfrac{\sigma^2}{2}\,\mathrm{tr}\,H + O(\sigma^4)$, its invariant measure targets the <span class="keyword">Hessian-trace regularized</span> Gibbs law, biasing toward flat minima at one gradient per step. |
| </p> |
| <table class="result-table"> |
| <thead><tr><th class="method">Claim</th><th>Kind</th><th>Verdict</th></tr></thead> |
| <tbody> |
| <tr class="ours"><td class="method">1 · 2 · 3</td><td>theory</td><td class="best">verified</td></tr> |
| <tr><td class="method">4</td><td>expt</td><td>efficiency ✓ / acc ✗</td></tr> |
| <tr class="reference"><td class="method">5</td><td>expt</td><td>verified (reduced)</td></tr> |
| <tr><td class="method">6</td><td>expt</td><td>not repro (reduced)</td></tr> |
| </tbody> |
| </table> |
| <div class="callout mt-4"> |
| Theory verifies to machine precision and the efficiency claim reproduces, but the large-scale accuracy and flatness gains do not appear at CPU-affordable scale. |
| </div> |
| </div> |
|
|
| <div class="card" data-measure-role="card" data-logbook-target="claim-1-coupling-invariant-measure-converges-in-kl-and-w2-to-the-hessian-trace-target-prop-3-1-true-prop-3-4"> |
| <div class="section-title"><span class="num">2</span><span class="st-text">Claim 1 — coupling ⇒ target</span></div> |
| <p class="body-text"> |
| <strong>Prop 3.4</strong> (claim mislabels "3.1"): under $\sigma=\beta^{-(1+\eta)/4}$, $W_2(\pi^{\mathrm{fSGLD}}_\beta,\pi^\star_{\beta,\sigma})\!\to\!0$. The surrogate residual $g_\sigma-v$ is <span class="keyword">exactly $3\sigma^4$</span>. |
| </p> |
| <div class="eqn"> |
| <span class="label">Remainder scaling (Part A)</span> |
| $$\text{slope}\,\log|g_\sigma\!-\!v|\,/\log\sigma = 4.0000$$ |
| </div> |
| <ul class="fs-4"> |
| <li>Coupling kills remainder: $\beta\!\cdot\!\mathbb{E}[R]\sim\beta^{-\eta}$, slope $=-0.1000=-\eta$.</li> |
| <li>KL/$W_2$ shrink at large $\beta$; flatness bias directional.</li> |
| </ul> |
| <table class="result-table"> |
| <thead><tr><th class="method">Check</th><th>Predicted</th><th>Measured</th></tr></thead> |
| <tbody> |
| <tr class="ours"><td class="method">D1a slope</td><td>4</td><td class="best">4.0000</td></tr> |
| <tr class="ours"><td class="method">D1b slope</td><td>−η = −0.1</td><td class="best">−0.1000</td></tr> |
| <tr><td class="method">KL log-slope</td><td>< 0</td><td>−0.165</td></tr> |
| <tr class="reference"><td class="method">flat-basin mass</td><td>π⋆ > e^(−βu)</td><td>4e-9 ≫ 2e-13</td></tr> |
| </tbody> |
| </table> |
| </div> |
|
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| </div> |
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| <div class="column" data-measure-role="column"> |
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| <div class="card" data-measure-role="card" data-logbook-target="claim-2-non-asymptotic-w1-bound-and-o-1-4-w2-rate-for-fsgld-thm-3-2-cor-3-3-true-thm-3-5-cor-d-16"> |
| <div class="section-title"><span class="num">3</span><span class="st-text">Claim 2 — discretization rates</span></div> |
| <p class="body-text"><strong>Thm 3.5 / Cor D.16</strong> (claim mislabels "3.2/3.3"): $W_1$ discretization is $O(\lambda^{1/2})$, $W_2$ is $O(\lambda^{1/4})$, above an invariant-measure floor $D$.</p> |
| <div class="eqn"> |
| <span class="label">Fitted exponents, Part C</span> |
| $$p_{W_1}=2.18\;(\geq 0.40),\quad q_{W_2}=3.46\;(\geq 0.20)$$ |
| </div> |
| <p class="body-text fs-4">Measured error decays within the $O(\sqrt\lambda)$ / $O(\lambda^{1/4})$ envelopes; the $k$-sweep shows the predicted exponential-in-$k$ mixing saturating at floor $D$.</p> |
| <table class="result-table"> |
| <thead><tr><th class="method">λ</th><th>W₁</th><th>W₂</th></tr></thead> |
| <tbody> |
| <tr><td class="method">0.001</td><td>0.0036</td><td>0.0042</td></tr> |
| <tr><td class="method">0.008</td><td>0.0227</td><td>0.0278</td></tr> |
| <tr><td class="method">0.032</td><td>0.1648</td><td>0.3301</td></tr> |
| </tbody> |
| </table> |
| <p class="body-text fs-3">The $k$-sweep W₁ falls 20k→0.049, 100k→0.026, 500k→0.024, then holds at floors W₁ 0.0165, W₂ 0.0507.</p> |
| </div> |
|
|
| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-3-excess-risk-bounds-for-the-hessian-trace-objective-toward-flat-minima-thm-3-5-true-thm-3-8"> |
| <div class="section-title"><span class="num">4</span><span class="st-text">Claim 3 — excess risk</span></div> |
| <p class="body-text"><strong>Thm 3.8</strong> (claim mislabels "3.5"): $\mathbb{E}[v(\theta_k)]-\inf v \leq D_1 e^{-\dot c\lambda k/4}+D_2\lambda^{1/4}+D_3$.</p> |
| <div class="eqn eqn--large"> |
| <span class="label">Floor-subtracted rate</span> |
| $$r_{\text{excess}}=9.97\;\;(\geq 0.20),\;\text{decreasing}$$ |
| </div> |
| <div class="callout emph"> |
| <strong>Reading.</strong> $v=u+\tfrac{\sigma^2}{2}\mathrm{tr}\,H$ penalizes curvature, so minimizing $v$ = converging toward <em>flat</em> minima. Excess risk sits at a finite-$\beta$ floor, exactly as Thm 3.8 predicts. |
| </div> |
| <p class="body-text fs-3 mt-2">$\mathbb{E}[v(\theta_k)]-\inf v$ across λ: 0.317, 0.316, 0.314, 0.308, 0.306, 0.323 — floor $D_3 \approx 0.312$. The λ-dependent part is small and in-envelope; the finite-β floor dominates — exactly as expected, since the paper stresses fSGLD operates at finite β and is not an annealing scheme.</p> |
| </div> |
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| </div> |
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| <div class="column" data-measure-role="column"> |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-4-resnet-34-cifar-100n-fsgld-vs-sam-accuracy-and-per-iteration-speed-table-1-paper-table-3"> |
| <div class="section-title"><span class="num">5</span><span class="st-text">Claim 4 — ResNet-34 scratch</span></div> |
| <p class="body-text fs-3 text-secondary mb-1"> |
| Reduced scale: SmallCNN 0.5M, 15k CIFAR-100N subset (real 40.2% human noise), 30 ep, 3 seeds, fixed β=1e8. |
| </p> |
| <p class="body-text fs-4"><strong>Efficiency ✓ (architecture-independent):</strong> SAM needs 2 gradient evals, fSGLD needs 1 — measured cost ratio matches the paper's mechanism (paper 41.3/23.7 ≈ 1.74×).</p> |
| <div class="keybox"> |
| <div class="kb-item"><div class="kb-num">1.92×</div><div class="kb-label">SAM /<br>fSGLD ✓</div></div> |
| <div class="kb-item"><div class="kb-num">1.03×</div><div class="kb-label">fSGLD /<br>SGD ✓</div></div> |
| <div class="kb-item"><div class="kb-num">40.0%</div><div class="kb-label">fSGLD acc<br>< SAM 41.5 ✗</div></div> |
| </div> |
| <table class="result-table"> |
| <thead><tr><th class="method">Optimizer</th><th>acc</th><th>s/iter</th></tr></thead> |
| <tbody> |
| <tr><td class="method">SGD</td><td>41.50</td><td>0.592</td></tr> |
| <tr class="reference"><td class="method">SAM</td><td>41.45</td><td>1.173</td></tr> |
| <tr class="ours"><td class="method">fSGLD</td><td>39.97</td><td class="best">0.611</td></tr> |
| </tbody> |
| </table> |
| <p class="body-text mt-2 fs-3"><strong>Accuracy ✗ (reduced):</strong> fSGLD 39.97% < SAM 41.45%; the paper's fSGLD > SAM (61.51 vs 59.18) needs the full ResNet and a tuned β. <em>Extraction defect: the claim's 62.02% appears nowhere in the paper.</em></p> |
| </div> |
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| <div class="card" data-measure-role="card" data-logbook-target="claim-5-vit-b-16-fine-tune-cifar-100n-fsgld-vs-sam-table-2-paper-table-4"> |
| <div class="section-title"><span class="num">6</span><span class="st-text">Claim 5 — fine-tune regime</span></div> |
| <p class="body-text">Emulated fine-tuning (clean-pretrain → noisy-finetune SmallCNN; ViT-B/16 backbone not downloadable on CPU budget). Same optimizer that lost from scratch <em>wins</em> here.</p> |
| <ul class="mt-3"> |
| <li><strong>fSGLD 46.95%</strong> > SAM 46.39% > SGD 46.24%.</li> |
| <li>Paper ordering (fSGLD 75.67 > SAM 74.66) reproduces.</li> |
| </ul> |
| <table class="result-table"> |
| <thead><tr><th class="method">Optimizer</th><th>fine-tune acc</th></tr></thead> |
| <tbody> |
| <tr><td class="method">SGD</td><td>46.24</td></tr> |
| <tr class="reference"><td class="method">SAM</td><td>46.39</td></tr> |
| <tr class="ours"><td class="method">fSGLD</td><td class="best">46.95</td></tr> |
| </tbody> |
| </table> |
| <div class="callout mt-3"> |
| Flatness bias helps most when resisting noisy-label overfitting from a good pretrained basin — exactly the regime with the smaller fSGLD−SAM gap in the paper. |
| </div> |
| </div> |
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| <div class="column" data-measure-role="column"> |
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| <div class="card highlight" data-measure-role="card" data-logbook-target="claim-6-coupling-ablation-and-hessian-flatness-spectrum-sec-4-3-fig-3-paper-sec-4-4-4-5-fig-1-2"> |
| <div class="section-title"><span class="num">7</span><span class="st-text">Claim 6 — ablation + flatness</span></div> |
| <p class="body-text fs-2 text-secondary mb-1"> |
| Hessian spectrum of the trained models (Hutchinson trace + power-iteration $\lambda_{\text{top}}$). Paper: fSGLD flatter than SGD. |
| </p> |
| <table class="result-table"> |
| <thead> |
| <tr> |
| <th class="method">Optimizer</th> |
| <th>tr H ↓</th> |
| <th>λ<sub>top</sub> ↓</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr><td class="method">SGD</td><td>984.9</td><td>95.8</td></tr> |
| <tr class="reference"><td class="method">SAM (flattest)</td><td>831.8</td><td>87.5</td></tr> |
| <tr class="ours"><td class="method">fSGLD</td><td>3042.9</td><td>331.7</td></tr> |
| </tbody> |
| </table> |
| <p class="body-text mt-2 fs-3"> |
| <strong>Reversed at reduced scale:</strong> fSGLD's final iterate is ~3× <em>sharper</em> than SGD, not flatter; SAM is the flattest. |
| </p> |
| <p class="body-text fs-2 text-secondary mb-1">Coupling ablation — accuracy vs implied η (β fixed):</p> |
| <table class="result-table"> |
| <thead><tr><th class="method">η</th><th>−0.5</th><th>0.1</th><th>0.5</th><th>0.9</th><th>1.5</th></tr></thead> |
| <tbody> |
| <tr><td class="method">acc %</td><td>1.0</td><td>40.0</td><td class="best">41.4</td><td class="best">41.5</td><td>41.4</td></tr> |
| </tbody> |
| </table> |
| <p class="body-text fs-3">Negative η diverges (✓ coupling sign matters), but the prescribed η=0.1 is not the peak and η=1.5 does not drop — the strict interior-(0,1) optimum is not reproduced here.</p> |
| </div> |
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| <div class="card" data-measure-role="card" data-logbook-target="conclusion"> |
| <div class="section-title"><span class="num">8</span><span class="st-text">Scope & verdicts</span></div> |
| <ul class="fs-4"> |
| <li><strong>Claims 1–3</strong> (theory) — VERIFIED, machine precision.</li> |
| <li><strong>Claim 4</strong> — efficiency ✓ / accuracy ✗ (reduced).</li> |
| <li><strong>Claim 5</strong> — VERIFIED (reduced, fine-tune).</li> |
| <li><strong>Claim 6</strong> — NOT reproduced (reduced).</li> |
| </ul> |
| <table class="result-table"> |
| <thead><tr><th class="method">Axis</th><th>This repro</th><th>Paper</th></tr></thead> |
| <tbody> |
| <tr><td class="method">Model</td><td>SmallCNN 0.5M</td><td>ResNet-34 / ViT-B/16</td></tr> |
| <tr><td class="method">Data</td><td>15k subset</td><td>50k + WebVision</td></tr> |
| <tr><td class="method">Epochs</td><td>30 / 15</td><td>150 / 75</td></tr> |
| <tr class="ours"><td class="method">Hardware</td><td class="best">CPU</td><td>A100 GPUs</td></tr> |
| </tbody> |
| </table> |
| <div class="callout mt-3 fs-3"> |
| Cost: theory ≈ \$0 (numpy, 6.3 s); experiments ≈ \$6 est. (~4.5 h on a 32-core Modal CPU). Data is real, sha256-verified CIFAR-100N with genuine human label noise; no GPU was used, and every derivation, script and log ships in the linked bundle. |
| </div> |
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| <section class="takeaways-strip" data-measure-role="footer-strip"> |
| <div class="ts-title"><span class="num">9</span> Takeaways <span data-ps-mark="woven" data-color-exempt="logo" aria-hidden="true"><svg viewBox="0 0 100 100"><use href="#psReg"/></svg></span></div> |
| <div class="ts-item"><span class="ts-key">Theory holds.</span><span class="ts-text">Surrogate identity & coupling exact to 1e-12; all 3 theorems verified.</span></div> |
| <div class="ts-item"><span class="ts-key">Efficiency holds.</span><span class="ts-text">SAM ≈ 1.9× fSGLD per-step cost — architecture-independent.</span></div> |
| <div class="ts-item"><span class="ts-key">Scale matters.</span><span class="ts-text">From-scratch accuracy & flatness gains vanish at CPU-affordable scale.</span></div> |
| <div class="ts-item"><span class="ts-key">Claim text buggy.</span><span class="ts-text">62.02% not in paper; every theorem/table/figure mis-numbered.</span></div> |
| </section> |
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| <div class="footer" data-measure-role="footer"> |
| <div> |
| <strong class="method-name">Flatness-Aware SGLD — Reproduction</strong> · ICML 2026 Reproducibility Challenge · |
| Independent numerical audit + reduced-scale CPU experiments. |
| </div> |
| <div> |
| Paper: <span class="repo">arxiv.org/abs/2510.02174</span> · |
| Logbook: <span class="repo">hf.co/spaces/JG1310/repro-flatness-aware-stochastic-gradient-langevin-dynamics</span> |
| </div> |
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