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| <head> | |
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| <title>Adaptive Agent Memory Resilience Playground</title> | |
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| </style> | |
| </head> | |
| <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 © 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) { | |
| const p = [ | |
| 676.5203681218851, -1259.1392167224028, 771.32342877765313, | |
| -176.61502916214059, 12.507343278686905, -0.13857109583654525, | |
| 9.9843695780195716e-6, 1.5056327351493116e-7 | |
| ]; | |
| if (z < 0.5) { | |
| return Math.log(Math.PI / Math.sin(Math.PI * z)) - logGamma(1 - z); | |
| } | |
| z -= 1; | |
| let x = 0.99999999999980993; | |
| for (let i = 0; i < p.length; i++) { | |
| x += p[i] / (z + i + 1); | |
| } | |
| let t = z + p.length - 0.5; | |
| return 0.5 * Math.log(2 * Math.PI) + (z + 0.5) * Math.log(t) - t + Math.log(x); | |
| } | |
| function betaCf(a, b, x, maxIter = 100, eps = 1e-12) { | |
| const qab = a + b; | |
| const qap = a + 1.0; | |
| const qam = a - 1.0; | |
| let c = 1.0; | |
| let d = 1.0 - qab * x / qap; | |
| if (Math.abs(d) < 1e-30) d = 1e-30; | |
| d = 1.0 / d; | |
| let h = d; | |
| for (let m = 1; m <= maxIter; m++) { | |
| let m2 = 2 * m; | |
| // Even step | |
| let aa = m * (b - m) * x / ((qam + m2) * (a + m2)); | |
| d = 1.0 + aa * d; | |
| if (Math.abs(d) < 1e-30) d = 1e-30; | |
| c = 1.0 + aa / c; | |
| if (Math.abs(c) < 1e-30) c = 1e-30; | |
| d = 1.0 / d; | |
| h *= d * c; | |
| // Odd step | |
| aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)); | |
| d = 1.0 + aa * d; | |
| if (Math.abs(d) < 1e-30) d = 1e-30; | |
| c = 1.0 + aa / c; | |
| if (Math.abs(c) < 1e-30) c = 1e-30; | |
| 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> | |