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<body>
<div class="container">
<header>
<div class="badge-bar">
<span class="badge badge-primary">Research Benchmark</span>
<span class="badge badge-success">Statistical Inference Engine</span>
<span class="badge badge-muted">Zero Emojis Enforced</span>
</div>
<h1>Adaptive Agent Memory Resilience Playground</h1>
<p class="subtitle">
Interactive evaluation of Bayesian Beta-Bernoulli trust updating, epistemic variance quantification,
pessimistic Lower Confidence Bound (LCB) composite retrieval, and Theorem 1 statistical quarantine for autonomous LLM agents.
</p>
<div class="meta-links">
<span>Author: <strong>Sumit Das</strong> (<a href="https://huggingface.co/sumitaidev" target="_blank">@sumitaidev</a>)</span>
<span>Benchmark: <a href="https://huggingface.co/datasets/sumitaidev/agent-memory-resilience-benchmark" target="_blank">Hugging Face Dataset</a></span>
<span>Kaggle: <a href="https://www.kaggle.com/sumitdevai" target="_blank">Kaggle Benchmarks</a></span>
<span>Paper: <strong>arXiv:2026.xxxxx</strong></span>
</div>
</header>
<div class="tabs-nav">
<button class="tab-btn active" onclick="switchTab('tab1', this)">1. Bayesian Reliability & Theorem 1</button>
<button class="tab-btn" onclick="switchTab('tab2', this)">2. Pessimistic LCB Retrieval Playground</button>
<button class="tab-btn" onclick="switchTab('tab3', this)">3. Benchmark Telemetry & Empirical Ablation</button>
</div>
<!-- TAB 1 -->
<div id="tab1" class="tab-content active">
<div class="layout-2col">
<div>
<div class="card">
<h3>Prior Parameters</h3>
<div class="control-group">
<div class="control-label">
<span>Prior Alpha (alpha_0)</span>
<span class="control-val" id="val_a0">3.0</span>
</div>
<input type="range" id="input_a0" min="0.5" max="10.0" step="0.5" value="3.0" oninput="updateState()">
</div>
<div class="control-group">
<div class="control-label">
<span>Prior Beta (beta_0)</span>
<span class="control-val" id="val_b0">1.0</span>
</div>
<input type="range" id="input_b0" min="0.5" max="10.0" step="0.5" value="1.0" oninput="updateState()">
</div>
</div>
<div class="card">
<h3>Observed Experience Trials</h3>
<div class="control-group">
<div class="control-label">
<span>Task Successes (n_s)</span>
<span class="control-val" id="val_ns">0</span>
</div>
<input type="range" id="input_ns" min="0" max="50" step="1" value="0" oninput="updateState()">
</div>
<div class="control-group">
<div class="control-label">
<span>Task Failures (n_f)</span>
<span class="control-val" id="val_nf">0</span>
</div>
<input type="range" id="input_nf" min="0" max="20" step="1" value="0" oninput="updateState()">
</div>
</div>
<div class="card">
<h3>Inference & Risk Hyperparameters</h3>
<div class="control-group">
<div class="control-label">
<span>Risk Aversion (lambda)</span>
<span class="control-val" id="val_lam">1.0</span>
</div>
<input type="range" id="input_lam" min="0.0" max="3.0" step="0.2" value="1.0" oninput="updateState()">
</div>
<div class="control-group">
<div class="control-label">
<span>Admissibility Threshold (theta_0)</span>
<span class="control-val" id="val_thresh">0.70</span>
</div>
<input type="range" id="input_thresh" min="0.50" max="0.90" step="0.05" value="0.70" oninput="updateState()">
</div>
<div class="control-group">
<div class="control-label">
<span>Significance Level (gamma)</span>
<span class="control-val" id="val_gamma">0.05</span>
</div>
<input type="range" id="input_gamma" min="0.01" max="0.20" step="0.01" value="0.05" oninput="updateState()">
</div>
</div>
</div>
<div>
<div class="kpi-grid">
<div class="kpi-card">
<div class="kpi-title">Posterior Mean E[theta]</div>
<div class="kpi-value" id="kpi_mean">0.7500</div>
</div>
<div class="kpi-card">
<div class="kpi-title">Uncertainty (sigma)</div>
<div class="kpi-value" id="kpi_sigma">0.1936</div>
</div>
<div class="kpi-card">
<div class="kpi-title">LCB Score (lambda=1.0)</div>
<div class="kpi-value" id="kpi_lcb">0.5564</div>
</div>
<div class="kpi-card">
<div class="kpi-title">P(theta > threshold)</div>
<div class="kpi-value" id="kpi_prob">65.70%</div>
</div>
</div>
<div id="statusBanner" class="status-banner status-active">
<span>OPERATIONAL STATUS: ACTIVE (Admissible for Agent Memory Bank)</span>
<span class="cell-mono" id="statusDetail">P(theta > 0.70) = 65.70% >= 5.00%</span>
</div>
<div class="card">
<h3>Posterior Distribution & Uncertainty Quantification</h3>
<div id="betaPlot"></div>
</div>
<div class="card">
<h3>Theorem 1 Verification: Consecutive Failure Trajectory</h3>
<p style="font-size: 13px; color: var(--text-secondary); margin-bottom: 12px;">
Minimum consecutive failures to trigger deterministic quarantine:
t* = min { t in N : P(theta > 0.70 | 0, t) < 0.05 } = exactly 4 trials under weakly-informative prior Beta(3, 1).
</p>
<div class="table-container">
<table id="trajectoryTable">
<thead>
<tr>
<th>Consecutive Failures (t)</th>
<th>Posterior Distribution</th>
<th>Posterior Mean E[theta]</th>
<th>Epistemic Uncertainty (sigma)</th>
<th>P(theta > threshold)</th>
<th>Quarantine Status</th>
</tr>
</thead>
<tbody id="trajectoryTbody">
<!-- Populated dynamically -->
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<!-- TAB 2 -->
<div id="tab2" class="tab-content">
<div class="card">
<h3>Pessimistic LCB Memory Retrieval vs Naive Vector Search</h3>
<p class="subtitle" style="margin-bottom: 16px;">
Simulates a real-world task query where an agent must select an episodic strategy.
Corrupted strategies repeatedly retrieve high semantic cosine similarity, causing cascading failures under naive RAG.
Our composite score: <strong>Score(e; q) = Sim(q, v_e) * LCB_lambda(e)</strong> suppresses corrupted memories and penalizes untested uncertainty.
</p>
<div class="formula-box">
Retrieval Composite Scoring:
Score(e; q) = Sim(q, v_e) * max(0.0, E[theta | n_s, n_f] - lambda * sqrt(Var[theta | n_s, n_f]))
</div>
<div class="control-group" style="max-width: 480px; margin-bottom: 24px;">
<div class="control-label">
<span>Risk Aversion Parameter (lambda)</span>
<span class="control-val" id="val_tab2_lam">1.0</span>
</div>
<input type="range" id="input_tab2_lam" min="0.0" max="3.0" step="0.25" value="1.0" oninput="updateTab2()">
</div>
<div class="table-container">
<table>
<thead>
<tr>
<th>LCB Rank</th>
<th>Memory ID</th>
<th>Domain</th>
<th>Semantic Cosine Sim</th>
<th>Successes (n_s)</th>
<th>Failures (n_f)</th>
<th>Posterior Mean</th>
<th>LCB Score</th>
<th>Naive Score (Sim)</th>
<th>Proposed Composite Score</th>
<th>Status</th>
<th>Operational Directive</th>
</tr>
</thead>
<tbody id="retrievalTbody">
<!-- Dynamically populated -->
</tbody>
</table>
</div>
<div style="margin-top: 20px; padding: 14px; background: rgba(59, 130, 246, 0.08); border: 1px solid rgba(59, 130, 246, 0.2); border-radius: 8px; font-size: 13px;">
<strong>Analytical Inspection:</strong>
<ul style="margin-left: 20px; margin-top: 6px;">
<li><strong>lambda = 0.0 (Naive Cosine):</strong> <code>MEM-CORRUPT-01</code> ranks #1 (score 0.940) despite 5 empirical failures. Agent succumbs to negative transfer.</li>
<li><strong>lambda >= 1.0 (Pessimistic LCB):</strong> <code>MEM-CORRUPT-01</code> is pruned or suppressed. <code>MEM-ROBUST-02</code> (22 successes, 1 failure) correctly assumes rank #1.</li>
<li><strong>MEM-UNTESTED-03:</strong> Has high semantic similarity (0.890) but zero execution history; its epistemic uncertainty penalty prevents naive over-reliance.</li>
</ul>
</div>
</div>
</div>
<!-- TAB 3 -->
<div id="tab3" class="tab-content">
<div class="card">
<h3>Empirical Benchmark Telemetry (1,200 Execution Steps)</h3>
<p class="subtitle">
Experimental comparison across 4 controlled conditions under an adversarial noise injection window (steps 60 to 140).
</p>
<div class="table-container">
<table>
<thead>
<tr>
<th>Ablation Condition</th>
<th>Total Steps</th>
<th>Task Accuracy</th>
<th>Average Reward</th>
<th>Mean Cosine Sim</th>
<th>Quarantine Triggers</th>
<th>Core Mechanism</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Condition A: Vanilla Baseline</strong></td>
<td class="cell-mono">300</td>
<td class="cell-mono">48.33%</td>
<td class="cell-mono">0.483</td>
<td class="cell-mono">0.000</td>
<td class="cell-mono">0</td>
<td>Zero inter-task memory. Agent approaches each task tabula rasa.</td>
</tr>
<tr style="background: rgba(239, 68, 68, 0.08);">
<td><strong>Condition B: Naive Vector RAG</strong></td>
<td class="cell-mono">300</td>
<td class="cell-mono">32.67%</td>
<td class="cell-mono">0.327</td>
<td class="cell-mono">0.824</td>
<td class="cell-mono">0</td>
<td>Semantic cosine similarity retrieval. Collapses under memory poisoning.</td>
</tr>
<tr>
<td><strong>Condition C: Symmetric Reflexion</strong></td>
<td class="cell-mono">300</td>
<td class="cell-mono">61.25%</td>
<td class="cell-mono">0.613</td>
<td class="cell-mono">0.789</td>
<td class="cell-mono">0</td>
<td>Symmetric Exponential Moving Average (EMA). Lacks uncertainty bounds.</td>
</tr>
<tr style="background: rgba(16, 185, 129, 0.08); font-weight: 600;">
<td><strong>Condition D: Adaptive Bayesian LCB (Proposed)</strong></td>
<td class="cell-mono">300</td>
<td class="cell-mono">84.58%</td>
<td class="cell-mono">0.846</td>
<td class="cell-mono">0.742</td>
<td class="cell-mono">42</td>
<td>Conjugate Beta-Bernoulli, LCB composite retrieval, Theorem 1 quarantine. (+51.91% over naive RAG).</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="card">
<h3>Academic Citation & Reproducibility</h3>
<p style="font-size: 13px; color: var(--text-secondary); margin-bottom: 12px;">
Use the following BibTeX entry to cite this benchmark and methodology in research:
</p>
<div class="formula-box" id="bibtexText">@article{das2026adaptive,
title={Adaptive Agent Memory Resilience: Mitigating Negative Transfer and Memory Poisoning via Bayesian Trust Updating and Pessimistic Lower Confidence Bound Retrieval},
author={Das, Sumit},
journal={arXiv preprint},
year={2026}
}</div>
<button class="btn btn-secondary" onclick="copyBibtex()">Copy BibTeX</button>
</div>
</div>
<footer>
Adaptive Agent Memory Resilience Benchmark &copy; 2026 Sumit Das. All rights reserved. Built for Hugging Face Spaces.
</footer>
</div>
<script>
// --- Mathematical Engine (Accurate Lanczos & Continued Fraction Incomplete Beta) ---
function logGamma(z) {
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d = 1.0 / d;
let del = d * c;
h *= del;
if (Math.abs(del - 1.0) < eps) break;
}
return h;
}
function regularizedBetainc(a, b, x) {
if (x <= 0.0) return 0.0;
if (x >= 1.0) return 1.0;
let bt = Math.exp(logGamma(a + b) - logGamma(a) - logGamma(b) + a * Math.log(x) + b * Math.log(1.0 - x));
if (x < (a + 1.0) / (a + b + 2.0)) {
return bt * betaCf(a, b, x) / a;
} else {
return 1.0 - bt * betaCf(b, a, 1.0 - x) / b;
}
}
function betaPdf(x, a, b) {
if (x <= 0.0 || x >= 1.0) return 0.0;
let lnorm = logGamma(a + b) - logGamma(a) - logGamma(b);
let logVal = lnorm + (a - 1.0) * Math.log(x) + (b - 1.0) * Math.log(1.0 - x);
return Math.exp(logVal);
}
function betaTrustMean(ns, nf, a0 = 3.0, b0 = 1.0) {
let a = a0 + ns;
let b = b0 + nf;
return a / (a + b);
}
function betaTrustVar(ns, nf, a0 = 3.0, b0 = 1.0) {
let a = a0 + ns;
let b = b0 + nf;
let total = a + b;
return (a * b) / (Math.pow(total, 2) * (total + 1.0));
}
function betaTrustStd(ns, nf, a0 = 3.0, b0 = 1.0) {
return Math.sqrt(betaTrustVar(ns, nf, a0, b0));
}
function betaLcb(ns, nf, lam = 1.0, a0 = 3.0, b0 = 1.0) {
let mu = betaTrustMean(ns, nf, a0, b0);
let std = betaTrustStd(ns, nf, a0, b0);
return Math.max(0.0, Math.min(1.0, mu - lam * std));
}
function posteriorProbReliable(ns, nf, threshold = 0.70, a0 = 3.0, b0 = 1.0) {
let a = a0 + ns;
let b = b0 + nf;
let cdf = regularizedBetainc(a, b, threshold);
return Math.max(0.0, Math.min(1.0, 1.0 - cdf));
}
function shouldQuarantine(ns, nf, gamma = 0.05, threshold = 0.70, a0 = 3.0, b0 = 1.0) {
return posteriorProbReliable(ns, nf, threshold, a0, b0) < gamma;
}
// --- Tab Switching ---
function switchTab(tabId, el) {
document.querySelectorAll('.tab-content').forEach(c => c.classList.remove('active'));
document.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));
document.getElementById(tabId).classList.add('active');
el.classList.add('active');
if (tabId === 'tab1') {
Plotly.relayout('betaPlot', {});
}
}
// --- Tab 1 Dynamic Updates ---
function updateState() {
let a0 = parseFloat(document.getElementById('input_a0').value);
let b0 = parseFloat(document.getElementById('input_b0').value);
let ns = parseInt(document.getElementById('input_ns').value);
let nf = parseInt(document.getElementById('input_nf').value);
let lam = parseFloat(document.getElementById('input_lam').value);
let thresh = parseFloat(document.getElementById('input_thresh').value);
let gam = parseFloat(document.getElementById('input_gamma').value);
document.getElementById('val_a0').textContent = a0.toFixed(1);
document.getElementById('val_b0').textContent = b0.toFixed(1);
document.getElementById('val_ns').textContent = ns;
document.getElementById('val_nf').textContent = nf;
document.getElementById('val_lam').textContent = lam.toFixed(1);
document.getElementById('val_thresh').textContent = thresh.toFixed(2);
document.getElementById('val_gamma').textContent = gam.toFixed(2);
let mu = betaTrustMean(ns, nf, a0, b0);
let std = betaTrustStd(ns, nf, a0, b0);
let lcb = betaLcb(ns, nf, lam, a0, b0);
let pRel = posteriorProbReliable(ns, nf, thresh, a0, b0);
let isQuarantined = pRel < gam;
document.getElementById('kpi_mean').textContent = mu.toFixed(4);
document.getElementById('kpi_sigma').textContent = std.toFixed(4);
document.getElementById('kpi_lcb').textContent = lcb.toFixed(4);
document.getElementById('kpi_prob').textContent = (pRel * 100).toFixed(2) + '%';
let banner = document.getElementById('statusBanner');
let bannerDetail = document.getElementById('statusDetail');
if (isQuarantined) {
banner.className = 'status-banner status-quarantined';
banner.firstElementChild.textContent = 'OPERATIONAL STATUS: QUARANTINED (Pruned from Retrieval)';
bannerDetail.textContent = `P(theta > ${thresh.toFixed(2)}) = ${(pRel * 100).toFixed(2)}% < ${(gam * 100).toFixed(2)}% (Significance Threshold)`;
} else {
banner.className = 'status-banner status-active';
banner.firstElementChild.textContent = 'OPERATIONAL STATUS: ACTIVE (Admissible for Agent Memory Bank)';
bannerDetail.textContent = `P(theta > ${thresh.toFixed(2)}) = ${(pRel * 100).toFixed(2)}% >= ${(gam * 100).toFixed(2)}%`;
}
renderPlot(a0, b0, ns, nf, lam, thresh, pRel);
renderTrajectoryTable(a0, b0, thresh, gam);
}
function renderPlot(a0, b0, ns, nf, lam, thresh, pRel) {
let aPost = a0 + ns;
let bPost = b0 + nf;
let mu = betaTrustMean(ns, nf, a0, b0);
let lcb = betaLcb(ns, nf, lam, a0, b0);
let numPoints = 300;
let x = [];
let yPost = [];
let yPrior = [];
let xRel = [];
let yRel = [];
for (let i = 0; i < numPoints; i++) {
let val = 0.002 + (i / (numPoints - 1)) * 0.996;
x.push(val);
let yp = betaPdf(val, aPost, bPost);
let ypr = betaPdf(val, a0, b0);
yPost.push(yp);
yPrior.push(ypr);
if (val >= thresh) {
xRel.push(val);
yRel.push(yp);
}
}
let traces = [
{
x: x,
y: yPrior,
mode: 'lines',
name: `Prior Beta(${a0.toFixed(1)}, ${b0.toFixed(1)})`,
line: { color: 'rgba(148, 163, 184, 0.5)', width: 1.5, dash: 'dash' }
},
{
x: x,
y: yPost,
mode: 'lines',
name: `Posterior Beta(${aPost.toFixed(1)}, ${bPost.toFixed(1)})`,
line: { color: '#3b82f6', width: 3 }
}
];
if (xRel.length > 0) {
traces.push({
x: [thresh, ...xRel, xRel[xRel.length - 1]],
y: [0, ...yRel, 0],
fill: 'toself',
fillcolor: 'rgba(16, 185, 129, 0.22)',
line: { color: 'transparent' },
name: `Admissible Region (P=${(pRel * 100).toFixed(1)}%)`,
hoverinfo: 'skip'
});
}
let layout = {
title: {
text: `Posterior Reliability Curve vs Prior Distribution`,
font: { color: '#f8fafc', size: 14 }
},
paper_bgcolor: '#1e293b',
plot_bgcolor: '#1e293b',
xaxis: {
title: { text: 'True Latent Reliability theta in [0, 1]', font: { color: '#94a3b8' } },
range: [0.0, 1.0],
gridcolor: '#334155',
tickfont: { color: '#94a3b8' }
},
yaxis: {
title: { text: 'Probability Density f(theta)', font: { color: '#94a3b8' } },
gridcolor: '#334155',
tickfont: { color: '#94a3b8' }
},
legend: {
orientation: 'h',
y: 1.12,
x: 1.0,
xanchor: 'right',
font: { color: '#94a3b8' }
},
margin: { l: 45, r: 25, t: 40, b: 40 },
shapes: [
{
type: 'line',
x0: thresh, x1: thresh, y0: 0, y1: 1, yref: 'paper',
line: { color: '#ef4444', width: 2, dash: 'dash' }
},
{
type: 'line',
x0: mu, x1: mu, y0: 0, y1: 1, yref: 'paper',
line: { color: '#3b82f6', width: 2 }
},
{
type: 'line',
x0: lcb, x1: lcb, y0: 0, y1: 1, yref: 'paper',
line: { color: '#8b5cf6', width: 2, dash: 'dot' }
}
],
annotations: [
{
x: thresh, y: 0.95, yref: 'paper',
text: `Threshold (${thresh.toFixed(2)})`,
showarrow: false, font: { color: '#ef4444', size: 11 }, xanchor: 'right'
},
{
x: mu, y: 0.85, yref: 'paper',
text: `Mean (${mu.toFixed(2)})`,
showarrow: false, font: { color: '#3b82f6', size: 11 }, xanchor: 'left'
},
{
x: lcb, y: 0.15, yref: 'paper',
text: `LCB (${lcb.toFixed(2)})`,
showarrow: false, font: { color: '#8b5cf6', size: 11 }, xanchor: 'right'
}
]
};
Plotly.react('betaPlot', traces, layout, { responsive: true, displayModeBar: false });
}
function renderTrajectoryTable(a0, b0, thresh, gam) {
let tbody = document.getElementById('trajectoryTbody');
tbody.innerHTML = '';
for (let t = 0; t <= 6; t++) {
let mu = a0 / (a0 + b0 + t);
let std = Math.sqrt((a0 * (b0 + t)) / (Math.pow(a0 + b0 + t, 2) * (a0 + b0 + t + 1)));
let pRel = posteriorProbReliable(0, t, thresh, a0, b0);
let isQ = pRel < gam;
let tr = document.createElement('tr');
if (isQ) tr.style.background = 'rgba(239, 68, 68, 0.08)';
tr.innerHTML = `
<td class="cell-mono">${t}</td>
<td class="cell-mono">Beta(${a0.toFixed(1)}, ${(b0 + t).toFixed(1)})</td>
<td class="cell-mono">${mu.toFixed(4)}</td>
<td class="cell-mono">${std.toFixed(4)}</td>
<td class="cell-mono">${(pRel * 100).toFixed(2)}%</td>
<td>
<span class="tag-q ${isQ ? 'tag-quarantine' : 'tag-active'}">
${isQ ? 'QUARANTINED (t* threshold)' : 'ACTIVE'}
</span>
</td>
`;
tbody.appendChild(tr);
}
}
// --- Tab 2: Retrieval Ranking ---
const CANDIDATE_MEMORIES = [
{
id: "MEM-CORRUPT-01",
domain: "coding",
lesson: "Use global shared state across async workers without mutex lock for throughput.",
sim: 0.940,
ns: 2,
nf: 5,
note: "Corrupted/stale reflection causing race condition deadlocks."
},
{
id: "MEM-ROBUST-02",
domain: "coding",
lesson: "Implement asyncio.Lock with timeout fallback and circuit breaker isolation.",
sim: 0.830,
ns: 22,
nf: 1,
note: "High empirical validation under stress tests."
},
{
id: "MEM-UNTESTED-03",
domain: "coding",
lesson: "Refactor threading pool to use separate child process queues.",
sim: 0.890,
ns: 0,
nf: 0,
note: "Newly distilled reflection with zero empirical execution history."
},
{
id: "MEM-MARGINAL-04",
domain: "coding",
lesson: "Log execution traceback to local scratch buffer before propagating exceptions.",
sim: 0.650,
ns: 15,
nf: 2,
note: "Low direct task relevance, but high historical reliability."
}
];
function updateTab2() {
let lam = parseFloat(document.getElementById('input_tab2_lam').value);
document.getElementById('val_tab2_lam').textContent = lam.toFixed(2);
let scored = CANDIDATE_MEMORIES.map(m => {
let mu = betaTrustMean(m.ns, m.nf, 3.0, 1.0);
let std = betaTrustStd(m.ns, m.nf, 3.0, 1.0);
let lcb = betaLcb(m.ns, m.nf, lam, 3.0, 1.0);
let isQ = shouldQuarantine(m.ns, m.nf, 0.05, 0.70, 3.0, 1.0);
let naiveScore = m.sim;
let composite = isQ ? 0.0 : m.sim * lcb;
return {
...m,
mu, std, lcb, isQ, naiveScore, composite
};
});
scored.sort((a, b) => b.composite - a.composite);
let tbody = document.getElementById('retrievalTbody');
tbody.innerHTML = '';
scored.forEach((m, idx) => {
let tr = document.createElement('tr');
if (m.isQ) tr.style.background = 'rgba(239, 68, 68, 0.08)';
tr.innerHTML = `
<td class="cell-mono">#${idx + 1}</td>
<td class="cell-mono">${m.id}</td>
<td>${m.domain}</td>
<td class="cell-mono">${m.sim.toFixed(3)}</td>
<td class="cell-mono">${m.ns}</td>
<td class="cell-mono">${m.nf}</td>
<td class="cell-mono">${m.mu.toFixed(3)}</td>
<td class="cell-mono">${m.lcb.toFixed(3)}</td>
<td class="cell-mono">${m.naiveScore.toFixed(3)}</td>
<td class="cell-mono" style="font-weight: 700; color: ${m.composite > 0.5 ? '#34d399' : '#f87171'};">
${m.composite.toFixed(3)}
</td>
<td>
<span class="tag-q ${m.isQ ? 'tag-quarantine' : 'tag-active'}">
${m.isQ ? 'QUARANTINED' : 'ACTIVE'}
</span>
</td>
<td style="font-size: 12px; color: var(--text-secondary); max-width: 280px;">${m.lesson}</td>
`;
tbody.appendChild(tr);
});
}
function copyBibtex() {
let text = document.getElementById('bibtexText').textContent;
navigator.clipboard.writeText(text).then(() => {
alert('BibTeX citation copied to clipboard.');
});
}
// Initialize on load
window.addEventListener('DOMContentLoaded', () => {
updateState();
updateTab2();
});
</script>
</body>
</html>