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index.html
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| 1 |
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8"/>
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<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
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<title>Matrix Lattice — Architecture Spec</title>
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Share+Tech+Mono&family=Syne:wght@400;700;800;900&display=swap');
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+
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:root {
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--bg: #03070d;
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--card: #070e18;
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| 13 |
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--border: #0f2035;
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+
--accent: #00d4ff;
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--accent2: #7b4dff;
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--accent3: #ff6b35;
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--gold: #f0b429;
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--text: #cdd8e8;
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--muted: #3d5a78;
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--glow: rgba(0,212,255,0.08);
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}
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* { box-sizing: border-box; margin: 0; padding: 0; }
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body {
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background: var(--bg);
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| 27 |
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font-family: 'Syne', sans-serif;
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color: var(--text);
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| 29 |
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min-height: 100vh;
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overflow-x: hidden;
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}
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/* ── Grid background ── */
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| 34 |
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body::before {
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content: '';
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position: fixed;
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| 37 |
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inset: 0;
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| 38 |
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background-image:
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linear-gradient(rgba(0,212,255,0.03) 1px, transparent 1px),
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linear-gradient(90deg, rgba(0,212,255,0.03) 1px, transparent 1px);
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| 41 |
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background-size: 48px 48px;
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| 42 |
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pointer-events: none;
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| 43 |
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z-index: 0;
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}
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| 45 |
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.wrap { position: relative; z-index: 1; max-width: 1200px; margin: 0 auto; padding: 0 32px 80px; }
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| 47 |
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| 48 |
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/* ── Hero ── */
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| 49 |
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.hero {
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padding: 80px 0 60px;
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| 51 |
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text-align: center;
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| 52 |
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position: relative;
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}
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| 54 |
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.hero::after {
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content: '';
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position: absolute;
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bottom: 0; left: 50%;
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transform: translateX(-50%);
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width: 600px; height: 1px;
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background: linear-gradient(90deg, transparent, var(--accent), transparent);
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}
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+
.hero-badge {
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display: inline-block;
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padding: 5px 16px;
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+
border: 1px solid var(--accent2);
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| 66 |
+
border-radius: 2px;
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| 67 |
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font-family: 'Share Tech Mono', monospace;
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| 68 |
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font-size: 11px;
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color: var(--accent2);
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letter-spacing: 4px;
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margin-bottom: 28px;
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background: rgba(123,77,255,0.06);
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}
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.hero h1 {
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font-size: clamp(52px, 8vw, 96px);
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font-weight: 900;
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line-height: 0.92;
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letter-spacing: -2px;
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margin-bottom: 16px;
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}
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.hero h1 .matrix { color: var(--muted); }
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| 82 |
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.hero h1 .lattice {
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color: var(--accent);
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| 84 |
+
text-shadow: 0 0 60px rgba(0,212,255,0.4);
|
| 85 |
+
}
|
| 86 |
+
.hero-sub {
|
| 87 |
+
font-size: 14px;
|
| 88 |
+
color: var(--muted);
|
| 89 |
+
letter-spacing: 3px;
|
| 90 |
+
text-transform: uppercase;
|
| 91 |
+
margin-bottom: 48px;
|
| 92 |
+
}
|
| 93 |
+
.hero-tags {
|
| 94 |
+
display: flex;
|
| 95 |
+
justify-content: center;
|
| 96 |
+
flex-wrap: wrap;
|
| 97 |
+
gap: 10px;
|
| 98 |
+
}
|
| 99 |
+
.tag {
|
| 100 |
+
padding: 6px 16px;
|
| 101 |
+
border: 1px solid var(--border);
|
| 102 |
+
border-radius: 2px;
|
| 103 |
+
font-family: 'Share Tech Mono', monospace;
|
| 104 |
+
font-size: 11px;
|
| 105 |
+
color: var(--muted);
|
| 106 |
+
letter-spacing: 1px;
|
| 107 |
+
}
|
| 108 |
+
.tag.hot { border-color: var(--accent); color: var(--accent); background: var(--glow); }
|
| 109 |
+
.tag.purple { border-color: var(--accent2); color: var(--accent2); background: rgba(123,77,255,0.06); }
|
| 110 |
+
.tag.orange { border-color: var(--accent3); color: var(--accent3); background: rgba(255,107,53,0.06); }
|
| 111 |
+
|
| 112 |
+
/* ── Section headers ── */
|
| 113 |
+
.section { margin-top: 72px; }
|
| 114 |
+
.section-label {
|
| 115 |
+
font-family: 'Share Tech Mono', monospace;
|
| 116 |
+
font-size: 10px;
|
| 117 |
+
color: var(--accent);
|
| 118 |
+
letter-spacing: 5px;
|
| 119 |
+
text-transform: uppercase;
|
| 120 |
+
margin-bottom: 6px;
|
| 121 |
+
}
|
| 122 |
+
.section-title {
|
| 123 |
+
font-size: 28px;
|
| 124 |
+
font-weight: 800;
|
| 125 |
+
color: #fff;
|
| 126 |
+
margin-bottom: 28px;
|
| 127 |
+
}
|
| 128 |
+
|
| 129 |
+
/* ── Model tier cards ── */
|
| 130 |
+
.tier-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 2px; }
|
| 131 |
+
.tier {
|
| 132 |
+
background: var(--card);
|
| 133 |
+
border: 1px solid var(--border);
|
| 134 |
+
padding: 32px 24px;
|
| 135 |
+
position: relative;
|
| 136 |
+
overflow: hidden;
|
| 137 |
+
transition: border-color 0.2s;
|
| 138 |
+
}
|
| 139 |
+
.tier::before {
|
| 140 |
+
content: '';
|
| 141 |
+
position: absolute;
|
| 142 |
+
top: 0; left: 0; right: 0;
|
| 143 |
+
height: 2px;
|
| 144 |
+
}
|
| 145 |
+
.tier.t120::before { background: linear-gradient(90deg, var(--accent2), transparent); }
|
| 146 |
+
.tier.t430::before { background: linear-gradient(90deg, var(--accent), transparent); }
|
| 147 |
+
.tier.t671::before { background: linear-gradient(90deg, var(--gold), transparent); }
|
| 148 |
+
.tier:hover { border-color: var(--accent); }
|
| 149 |
+
|
| 150 |
+
.tier-name {
|
| 151 |
+
font-size: 13px;
|
| 152 |
+
font-weight: 800;
|
| 153 |
+
letter-spacing: 3px;
|
| 154 |
+
margin-bottom: 6px;
|
| 155 |
+
text-transform: uppercase;
|
| 156 |
+
}
|
| 157 |
+
.tier.t120 .tier-name { color: var(--accent2); }
|
| 158 |
+
.tier.t430 .tier-name { color: var(--accent); }
|
| 159 |
+
.tier.t671 .tier-name { color: var(--gold); }
|
| 160 |
+
|
| 161 |
+
.tier-params {
|
| 162 |
+
font-size: 48px;
|
| 163 |
+
font-weight: 900;
|
| 164 |
+
color: #fff;
|
| 165 |
+
line-height: 1;
|
| 166 |
+
margin-bottom: 4px;
|
| 167 |
+
}
|
| 168 |
+
.tier-active {
|
| 169 |
+
font-family: 'Share Tech Mono', monospace;
|
| 170 |
+
font-size: 11px;
|
| 171 |
+
color: var(--muted);
|
| 172 |
+
margin-bottom: 24px;
|
| 173 |
+
}
|
| 174 |
+
.tier-stat { display: flex; justify-content: space-between; padding: 8px 0; border-top: 1px solid var(--border); font-size: 11px; }
|
| 175 |
+
.tier-stat .k { color: var(--muted); font-family: 'Share Tech Mono', monospace; letter-spacing: 1px; }
|
| 176 |
+
.tier-stat .v { color: var(--text); font-weight: 700; font-family: 'Share Tech Mono', monospace; }
|
| 177 |
+
|
| 178 |
+
/* ── Arch blocks ── */
|
| 179 |
+
.arch-row {
|
| 180 |
+
display: grid;
|
| 181 |
+
grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
|
| 182 |
+
gap: 12px;
|
| 183 |
+
}
|
| 184 |
+
.arch-block {
|
| 185 |
+
background: var(--card);
|
| 186 |
+
border: 1px solid var(--border);
|
| 187 |
+
border-left: 3px solid var(--accent);
|
| 188 |
+
padding: 18px 20px;
|
| 189 |
+
}
|
| 190 |
+
.arch-block.purple { border-left-color: var(--accent2); }
|
| 191 |
+
.arch-block.orange { border-left-color: var(--accent3); }
|
| 192 |
+
.arch-block.gold { border-left-color: var(--gold); }
|
| 193 |
+
.arch-name {
|
| 194 |
+
font-size: 12px;
|
| 195 |
+
font-weight: 800;
|
| 196 |
+
color: #fff;
|
| 197 |
+
margin-bottom: 6px;
|
| 198 |
+
letter-spacing: 0.5px;
|
| 199 |
+
}
|
| 200 |
+
.arch-desc {
|
| 201 |
+
font-family: 'Share Tech Mono', monospace;
|
| 202 |
+
font-size: 10px;
|
| 203 |
+
color: var(--muted);
|
| 204 |
+
line-height: 1.8;
|
| 205 |
+
}
|
| 206 |
+
|
| 207 |
+
/* ── Modules grid ── */
|
| 208 |
+
.modules-grid {
|
| 209 |
+
display: grid;
|
| 210 |
+
grid-template-columns: repeat(auto-fill, minmax(340px, 1fr));
|
| 211 |
+
gap: 2px;
|
| 212 |
+
}
|
| 213 |
+
.module {
|
| 214 |
+
background: var(--card);
|
| 215 |
+
border: 1px solid var(--border);
|
| 216 |
+
padding: 24px;
|
| 217 |
+
position: relative;
|
| 218 |
+
transition: all 0.15s;
|
| 219 |
+
}
|
| 220 |
+
.module:hover {
|
| 221 |
+
border-color: var(--accent);
|
| 222 |
+
background: #070f1c;
|
| 223 |
+
}
|
| 224 |
+
.module-num {
|
| 225 |
+
font-family: 'Share Tech Mono', monospace;
|
| 226 |
+
font-size: 10px;
|
| 227 |
+
color: var(--muted);
|
| 228 |
+
letter-spacing: 2px;
|
| 229 |
+
margin-bottom: 8px;
|
| 230 |
+
}
|
| 231 |
+
.module-name {
|
| 232 |
+
font-size: 14px;
|
| 233 |
+
font-weight: 800;
|
| 234 |
+
color: #fff;
|
| 235 |
+
margin-bottom: 10px;
|
| 236 |
+
letter-spacing: 0.3px;
|
| 237 |
+
}
|
| 238 |
+
.module-desc {
|
| 239 |
+
font-family: 'Share Tech Mono', monospace;
|
| 240 |
+
font-size: 10px;
|
| 241 |
+
color: var(--muted);
|
| 242 |
+
line-height: 1.9;
|
| 243 |
+
}
|
| 244 |
+
.module-badge {
|
| 245 |
+
position: absolute;
|
| 246 |
+
top: 16px; right: 16px;
|
| 247 |
+
padding: 2px 8px;
|
| 248 |
+
font-family: 'Share Tech Mono', monospace;
|
| 249 |
+
font-size: 9px;
|
| 250 |
+
letter-spacing: 1px;
|
| 251 |
+
border-radius: 2px;
|
| 252 |
+
}
|
| 253 |
+
.mb-new { background: rgba(0,212,255,0.1); color: var(--accent); border: 1px solid var(--accent); }
|
| 254 |
+
.mb-eq { background: rgba(123,77,255,0.1); color: var(--accent2); border: 1px solid var(--accent2); }
|
| 255 |
+
.mb-safe { background: rgba(240,180,41,0.1); color: var(--gold); border: 1px solid var(--gold); }
|
| 256 |
+
.mb-agent { background: rgba(255,107,53,0.1); color: var(--accent3); border: 1px solid var(--accent3); }
|
| 257 |
+
.mb-mm { background: rgba(0,255,128,0.1); color: #00ff80; border: 1px solid #00ff80; }
|
| 258 |
+
|
| 259 |
+
/* ── API block ── */
|
| 260 |
+
.api-block {
|
| 261 |
+
background: #020608;
|
| 262 |
+
border: 1px solid var(--border);
|
| 263 |
+
border-radius: 4px;
|
| 264 |
+
padding: 28px 32px;
|
| 265 |
+
font-family: 'Share Tech Mono', monospace;
|
| 266 |
+
font-size: 12px;
|
| 267 |
+
line-height: 2;
|
| 268 |
+
overflow-x: auto;
|
| 269 |
+
}
|
| 270 |
+
.kw { color: var(--accent2); }
|
| 271 |
+
.fn { color: var(--accent); }
|
| 272 |
+
.str { color: #86efac; }
|
| 273 |
+
.cm { color: var(--muted); }
|
| 274 |
+
.num { color: var(--gold); }
|
| 275 |
+
|
| 276 |
+
/* ── TPS chart ── */
|
| 277 |
+
.tps-grid { display: grid; grid-template-columns: repeat(3,1fr); gap: 2px; }
|
| 278 |
+
.tps-card {
|
| 279 |
+
background: var(--card);
|
| 280 |
+
border: 1px solid var(--border);
|
| 281 |
+
padding: 24px;
|
| 282 |
+
}
|
| 283 |
+
.tps-model { font-size: 11px; font-weight: 800; letter-spacing: 3px; margin-bottom: 20px; }
|
| 284 |
+
.tps-card:nth-child(1) .tps-model { color: var(--accent2); }
|
| 285 |
+
.tps-card:nth-child(2) .tps-model { color: var(--accent); }
|
| 286 |
+
.tps-card:nth-child(3) .tps-model { color: var(--gold); }
|
| 287 |
+
.tps-row { margin-bottom: 14px; }
|
| 288 |
+
.tps-label { display: flex; justify-content: space-between; font-family: 'Share Tech Mono', monospace; font-size: 10px; margin-bottom: 5px; }
|
| 289 |
+
.tps-label .quant { color: var(--muted); }
|
| 290 |
+
.tps-label .val { color: #fff; font-weight: 700; }
|
| 291 |
+
.tps-bar { height: 5px; background: var(--border); border-radius: 1px; overflow: hidden; }
|
| 292 |
+
.tps-fill { height: 100%; border-radius: 1px; }
|
| 293 |
+
.bf16 { background: var(--muted); }
|
| 294 |
+
.int8 { background: var(--accent); }
|
| 295 |
+
.int4 { background: var(--gold); }
|
| 296 |
+
|
| 297 |
+
/* ── Footer ── */
|
| 298 |
+
.footer {
|
| 299 |
+
margin-top: 80px;
|
| 300 |
+
padding-top: 32px;
|
| 301 |
+
border-top: 1px solid var(--border);
|
| 302 |
+
display: flex;
|
| 303 |
+
justify-content: space-between;
|
| 304 |
+
align-items: center;
|
| 305 |
+
font-family: 'Share Tech Mono', monospace;
|
| 306 |
+
font-size: 10px;
|
| 307 |
+
color: var(--muted);
|
| 308 |
+
letter-spacing: 2px;
|
| 309 |
+
}
|
| 310 |
+
.footer-dots { display: flex; gap: 16px; }
|
| 311 |
+
.dot { display: flex; align-items: center; gap: 6px; }
|
| 312 |
+
.dot::before { content: '●'; font-size: 8px; }
|
| 313 |
+
.dot.cyan::before { color: var(--accent); }
|
| 314 |
+
.dot.purple::before { color: var(--accent2); }
|
| 315 |
+
.dot.gold::before { color: var(--gold); }
|
| 316 |
+
|
| 317 |
+
/* ── Timeline ── */
|
| 318 |
+
.timeline { display: flex; gap: 0; }
|
| 319 |
+
.tl-step {
|
| 320 |
+
flex: 1;
|
| 321 |
+
padding: 20px 24px;
|
| 322 |
+
background: var(--card);
|
| 323 |
+
border: 1px solid var(--border);
|
| 324 |
+
border-right: none;
|
| 325 |
+
position: relative;
|
| 326 |
+
}
|
| 327 |
+
.tl-step:last-child { border-right: 1px solid var(--border); }
|
| 328 |
+
.tl-step::after {
|
| 329 |
+
content: '▶';
|
| 330 |
+
position: absolute;
|
| 331 |
+
right: -10px; top: 50%;
|
| 332 |
+
transform: translateY(-50%);
|
| 333 |
+
font-size: 10px;
|
| 334 |
+
color: var(--muted);
|
| 335 |
+
z-index: 2;
|
| 336 |
+
}
|
| 337 |
+
.tl-step:last-child::after { display: none; }
|
| 338 |
+
.tl-num { font-family: 'Share Tech Mono', monospace; font-size: 10px; color: var(--muted); letter-spacing: 2px; margin-bottom: 8px; }
|
| 339 |
+
.tl-title { font-size: 12px; font-weight: 800; color: #fff; margin-bottom: 6px; }
|
| 340 |
+
.tl-desc { font-family: 'Share Tech Mono', monospace; font-size: 10px; color: var(--muted); line-height: 1.7; }
|
| 341 |
+
</style>
|
| 342 |
+
</head>
|
| 343 |
+
<body>
|
| 344 |
+
<div class="wrap">
|
| 345 |
+
|
| 346 |
+
<!-- Hero -->
|
| 347 |
+
<div class="hero">
|
| 348 |
+
<div class="hero-badge">MATRIX.CORP — FRONTIER SERIES</div>
|
| 349 |
+
<h1>
|
| 350 |
+
<span class="matrix">MATRIX </span><br>
|
| 351 |
+
<span class="lattice">LATTICE</span>
|
| 352 |
+
</h1>
|
| 353 |
+
<div class="hero-sub">Agentic · Multimodal · 1M+ Context · MoE · API-First</div>
|
| 354 |
+
<div class="hero-tags">
|
| 355 |
+
<span class="tag hot">120B / 430B / 671B</span>
|
| 356 |
+
<span class="tag hot">~22–47B ACTIVE PARAMS</span>
|
| 357 |
+
<span class="tag purple">17 CUSTOM MODULES</span>
|
| 358 |
+
<span class="tag purple">DEEPSEEK-V3 + LLAMA 4 LINEAGE</span>
|
| 359 |
+
<span class="tag orange">INFERENCE PROVIDER READY</span>
|
| 360 |
+
<span class="tag orange">OPENAI-COMPATIBLE API</span>
|
| 361 |
+
<span class="tag">MLA ATTENTION</span>
|
| 362 |
+
<span class="tag">MIXTURE OF DEPTHS</span>
|
| 363 |
+
<span class="tag">SPECULATIVE DECODING</span>
|
| 364 |
+
</div>
|
| 365 |
+
</div>
|
| 366 |
+
|
| 367 |
+
<!-- Model Tiers -->
|
| 368 |
+
<div class="section">
|
| 369 |
+
<div class="section-label">Model Family</div>
|
| 370 |
+
<div class="section-title">Three Tiers, One Architecture</div>
|
| 371 |
+
<div class="tier-grid">
|
| 372 |
+
<div class="tier t120">
|
| 373 |
+
<div class="tier-name">Lattice — Entry</div>
|
| 374 |
+
<div class="tier-params">120B</div>
|
| 375 |
+
<div class="tier-active">~22B active params · 64 experts · top-4</div>
|
| 376 |
+
<div class="tier-stat"><span class="k">CONTEXT</span><span class="v">1M tokens</span></div>
|
| 377 |
+
<div class="tier-stat"><span class="k">EXPERTS</span><span class="v">64 routed + 2 shared</span></div>
|
| 378 |
+
<div class="tier-stat"><span class="k">HARDWARE</span><span class="v">4× H100 / 8× p300a</span></div>
|
| 379 |
+
<div class="tier-stat"><span class="k">INT4 VRAM</span><span class="v">~60GB</span></div>
|
| 380 |
+
<div class="tier-stat"><span class="k">TPS (INT4)</span><span class="v">~130</span></div>
|
| 381 |
+
<div class="tier-stat"><span class="k">STATUS</span><span class="v" style="color:#f59e0b">🔴 PLANNED</span></div>
|
| 382 |
+
</div>
|
| 383 |
+
<div class="tier t430">
|
| 384 |
+
<div class="tier-name">Lattice — Pro</div>
|
| 385 |
+
<div class="tier-params">430B</div>
|
| 386 |
+
<div class="tier-active">~38B active params · 128 experts · top-4</div>
|
| 387 |
+
<div class="tier-stat"><span class="k">CONTEXT</span><span class="v">1M tokens</span></div>
|
| 388 |
+
<div class="tier-stat"><span class="k">EXPERTS</span><span class="v">128 routed + 4 shared</span></div>
|
| 389 |
+
<div class="tier-stat"><span class="k">HARDWARE</span><span class="v">8× H100 / 28× p300a</span></div>
|
| 390 |
+
<div class="tier-stat"><span class="k">INT4 VRAM</span><span class="v">~215GB</span></div>
|
| 391 |
+
<div class="tier-stat"><span class="k">TPS (INT4)</span><span class="v">~72</span></div>
|
| 392 |
+
<div class="tier-stat"><span class="k">STATUS</span><span class="v" style="color:#f59e0b">🔴 PLANNED</span></div>
|
| 393 |
+
</div>
|
| 394 |
+
<div class="tier t671">
|
| 395 |
+
<div class="tier-name">Lattice — Max</div>
|
| 396 |
+
<div class="tier-params">671B</div>
|
| 397 |
+
<div class="tier-active">~47B active params · 256 experts · top-4</div>
|
| 398 |
+
<div class="tier-stat"><span class="k">CONTEXT</span><span class="v">1M tokens</span></div>
|
| 399 |
+
<div class="tier-stat"><span class="k">EXPERTS</span><span class="v">256 routed + 8 shared</span></div>
|
| 400 |
+
<div class="tier-stat"><span class="k">HARDWARE</span><span class="v">32× H100 / 48× p300a</span></div>
|
| 401 |
+
<div class="tier-stat"><span class="k">INT4 VRAM</span><span class="v">~336GB</span></div>
|
| 402 |
+
<div class="tier-stat"><span class="k">TPS (INT4)</span><span class="v">~50</span></div>
|
| 403 |
+
<div class="tier-stat"><span class="k">STATUS</span><span class="v" style="color:#f59e0b">🔴 PLANNED</span></div>
|
| 404 |
+
</div>
|
| 405 |
+
</div>
|
| 406 |
+
</div>
|
| 407 |
+
|
| 408 |
+
<!-- Public Architectures -->
|
| 409 |
+
<div class="section">
|
| 410 |
+
<div class="section-label">Foundation</div>
|
| 411 |
+
<div class="section-title">Public Architectures Integrated</div>
|
| 412 |
+
<div class="arch-row">
|
| 413 |
+
<div class="arch-block">
|
| 414 |
+
<div class="arch-name">Multi-Head Latent Attention (MLA)</div>
|
| 415 |
+
<div class="arch-desc">DeepSeek-V3 · KV cache compressed ~90% via<br>low-rank projection · Essential for 1M context</div>
|
| 416 |
+
</div>
|
| 417 |
+
<div class="arch-block purple">
|
| 418 |
+
<div class="arch-name">Mixture of Experts (MoE)</div>
|
| 419 |
+
<div class="arch-desc">DeepSeek-V3 style · Fine-grained expert segmentation<br>Auxiliary-free load balancing · No token dropping</div>
|
| 420 |
+
</div>
|
| 421 |
+
<div class="arch-block orange">
|
| 422 |
+
<div class="arch-name">Mixture of Depths (MoD)</div>
|
| 423 |
+
<div class="arch-desc">Google Research · Tokens skip up to 50% of layers<br>~30% compute reduction at same quality</div>
|
| 424 |
+
</div>
|
| 425 |
+
<div class="arch-block gold">
|
| 426 |
+
<div class="arch-name">iRoPE / YaRN Scaling</div>
|
| 427 |
+
<div class="arch-desc">Llama 4 + YaRN · NTK-aware RoPE for 1M+ context<br>Full attention every 4th layer · 8K sliding window</div>
|
| 428 |
+
</div>
|
| 429 |
+
<div class="arch-block">
|
| 430 |
+
<div class="arch-name">Speculative Decoding</div>
|
| 431 |
+
<div class="arch-desc">Paired draft model per tier (~4B params each)<br>3–5× inference speedup · Shared embedding weights</div>
|
| 432 |
+
</div>
|
| 433 |
+
<div class="arch-block purple">
|
| 434 |
+
<div class="arch-name">Multimodal Vision Encoder</div>
|
| 435 |
+
<div class="arch-desc">Llama 4 / InternVL lineage · ViT 6B params<br>Images, video, documents, charts · 4K via tiling</div>
|
| 436 |
+
</div>
|
| 437 |
+
<div class="arch-block orange">
|
| 438 |
+
<div class="arch-name">Audio Encoder</div>
|
| 439 |
+
<div class="arch-desc">Whisper-large-v3 lineage · Speech + sound understanding<br>Cross-attention injected into LM backbone</div>
|
| 440 |
+
</div>
|
| 441 |
+
<div class="arch-block gold">
|
| 442 |
+
<div class="arch-name">Sliding Window Attention</div>
|
| 443 |
+
<div class="arch-desc">Mistral · 8K window on non-full-attention layers<br>O(n) memory for most layers of the network</div>
|
| 444 |
+
</div>
|
| 445 |
+
</div>
|
| 446 |
+
</div>
|
| 447 |
+
|
| 448 |
+
<!-- 17 Modules -->
|
| 449 |
+
<div class="section">
|
| 450 |
+
<div class="section-label">Custom Architecture</div>
|
| 451 |
+
<div class="section-title">17 Custom Modules</div>
|
| 452 |
+
<div class="modules-grid">
|
| 453 |
+
|
| 454 |
+
<div class="module">
|
| 455 |
+
<div class="module-badge mb-eq">EQ V2</div>
|
| 456 |
+
<div class="module-num">MODULE 01</div>
|
| 457 |
+
<div class="module-name">EQ Engine V2</div>
|
| 458 |
+
<div class="module-desc">Conversation-arc emotional tracking via persistent GRU.<br>12-emotion classification. Frustration trajectory<br>prediction. Per-user baseline calibration (3 turns).</div>
|
| 459 |
+
</div>
|
| 460 |
+
|
| 461 |
+
<div class="module">
|
| 462 |
+
<div class="module-badge mb-new">CORE</div>
|
| 463 |
+
<div class="module-num">MODULE 02</div>
|
| 464 |
+
<div class="module-name">Lattice Router</div>
|
| 465 |
+
<div class="module-desc">Hierarchical MoE routing: token → domain cluster →<br>expert group → expert. 8 domain clusters.<br>Experts self-label. Load-aware dispatch.</div>
|
| 466 |
+
</div>
|
| 467 |
+
|
| 468 |
+
<div class="module">
|
| 469 |
+
<div class="module-badge mb-new">API</div>
|
| 470 |
+
<div class="module-num">MODULE 03</div>
|
| 471 |
+
<div class="module-name">Confidence Calibration Head</div>
|
| 472 |
+
<div class="module-desc">Parallel to LM head. Epistemic uncertainty [0–1]<br>per token. Aggregated per sentence. Exposed via<br>X-Lattice-Confidence header in streaming API.</div>
|
| 473 |
+
</div>
|
| 474 |
+
|
| 475 |
+
<div class="module">
|
| 476 |
+
<div class="module-badge mb-agent">AGENTIC</div>
|
| 477 |
+
<div class="module-num">MODULE 04</div>
|
| 478 |
+
<div class="module-name">Native Tool Schema Reasoner</div>
|
| 479 |
+
<div class="module-desc">Dedicated attention heads for JSON Schema, OpenAPI,<br>GraphQL, SQL DDL. Tool call planner generates<br>multi-step plans. Parallel tool dispatch.</div>
|
| 480 |
+
</div>
|
| 481 |
+
|
| 482 |
+
<div class="module">
|
| 483 |
+
<div class="module-badge mb-agent">AGENTIC</div>
|
| 484 |
+
<div class="module-num">MODULE 05</div>
|
| 485 |
+
<div class="module-name">Multi-Agent Coordination Layer</div>
|
| 486 |
+
<div class="module-desc">Structured agent message protocol. Role awareness:<br>orchestrator / subagent / critic / executor.<br>Shared scratchpad attention. Conflict resolution head.</div>
|
| 487 |
+
</div>
|
| 488 |
+
|
| 489 |
+
<div class="module">
|
| 490 |
+
<div class="module-badge mb-new">CONTEXT</div>
|
| 491 |
+
<div class="module-num">MODULE 06</div>
|
| 492 |
+
<div class="module-name">Hierarchical Context Compression</div>
|
| 493 |
+
<div class="module-desc">Every 32K tokens compressed to summary + key-facts.<br>Meta-summary at 128K. Recent 32K always full-res.<br>~20:1 narrative · ~5:1 code compression ratio.</div>
|
| 494 |
+
</div>
|
| 495 |
+
|
| 496 |
+
<div class="module">
|
| 497 |
+
<div class="module-badge mb-new">OUTPUT</div>
|
| 498 |
+
<div class="module-num">MODULE 07</div>
|
| 499 |
+
<div class="module-name">Structured Output Enforcer</div>
|
| 500 |
+
<div class="module-desc">Constrained decoding via token masking. Guaranteed<br>valid JSON, YAML, XML, Python, SQL, HTML.<br>Partial streaming of valid JSON as tokens generate.</div>
|
| 501 |
+
</div>
|
| 502 |
+
|
| 503 |
+
<div class="module">
|
| 504 |
+
<div class="module-badge mb-new">REASON</div>
|
| 505 |
+
<div class="module-num">MODULE 08</div>
|
| 506 |
+
<div class="module-name">Causal Reasoning Graph</div>
|
| 507 |
+
<div class="module-desc">Builds explicit cause-effect graph during generation.<br>Graph attention on reasoning steps. Detects loops<br>and contradiction chains. Optional API trace output.</div>
|
| 508 |
+
</div>
|
| 509 |
+
|
| 510 |
+
<div class="module">
|
| 511 |
+
<div class="module-badge mb-new">TIME</div>
|
| 512 |
+
<div class="module-num">MODULE 09</div>
|
| 513 |
+
<div class="module-name">Temporal Awareness Module</div>
|
| 514 |
+
<div class="module-desc">Dedicated temporal embeddings for absolute dates,<br>relative references, durations. Timeline builder.<br>Temporal consistency checker for event ordering.</div>
|
| 515 |
+
</div>
|
| 516 |
+
|
| 517 |
+
<div class="module">
|
| 518 |
+
<div class="module-badge mb-new">LANG</div>
|
| 519 |
+
<div class="module-num">MODULE 10</div>
|
| 520 |
+
<div class="module-name">Cross-Lingual Alignment Layer</div>
|
| 521 |
+
<div class="module-desc">50+ languages. Language-agnostic semantic space.<br>Code-switching aware. CJK, Arabic RTL, Devanagari<br>native. Dialect modeling. Self-scoring translation head.</div>
|
| 522 |
+
</div>
|
| 523 |
+
|
| 524 |
+
<div class="module">
|
| 525 |
+
<div class="module-badge mb-safe">SAFETY</div>
|
| 526 |
+
<div class="module-num">MODULE 11</div>
|
| 527 |
+
<div class="module-name">Safety Reasoning Module</div>
|
| 528 |
+
<div class="module-desc">Explicit safety chain before generation, not post-hoc.<br>47 harm categories with confidence scores.<br>Provider-configurable tiers. Structured audit log.</div>
|
| 529 |
+
</div>
|
| 530 |
+
|
| 531 |
+
<div class="module">
|
| 532 |
+
<div class="module-badge mb-mm">VISION</div>
|
| 533 |
+
<div class="module-num">MODULE 12</div>
|
| 534 |
+
<div class="module-name">Vision-Language Grounding</div>
|
| 535 |
+
<div class="module-desc">Object-level text-to-region grounding. Chart/diagram<br>interpreter. Document layout understanding.<br>Screenshot-to-code. Video temporal grounding.</div>
|
| 536 |
+
</div>
|
| 537 |
+
|
| 538 |
+
<div class="module">
|
| 539 |
+
<div class="module-badge mb-agent">AGENTIC</div>
|
| 540 |
+
<div class="module-num">MODULE 13</div>
|
| 541 |
+
<div class="module-name">Long-Horizon Task Planner</div>
|
| 542 |
+
<div class="module-desc">Task decomposition into DAGs. Dependency resolver.<br>Progress tracker across long sessions. Replanning<br>trigger. Integrates with MACL for multi-agent tasks.</div>
|
| 543 |
+
</div>
|
| 544 |
+
|
| 545 |
+
<div class="module">
|
| 546 |
+
<div class="module-badge mb-eq">PERSONA</div>
|
| 547 |
+
<div class="module-num">MODULE 14</div>
|
| 548 |
+
<div class="module-name">Persona Stability Enforcer</div>
|
| 549 |
+
<div class="module-desc">Operator-defined persona as persistent embedding.<br>Style consistency loss during training. Factual<br>self-consistency checker. EQ-aware tone modulation.</div>
|
| 550 |
+
</div>
|
| 551 |
+
|
| 552 |
+
<div class="module">
|
| 553 |
+
<div class="module-badge mb-new">API</div>
|
| 554 |
+
<div class="module-num">MODULE 15</div>
|
| 555 |
+
<div class="module-name">API Telemetry & Observability</div>
|
| 556 |
+
<div class="module-desc">Per-token latency, expert utilization, compression events,<br>confidence, module activation trace — all exposed as<br>structured SSE metadata alongside token stream.</div>
|
| 557 |
+
</div>
|
| 558 |
+
|
| 559 |
+
<div class="module">
|
| 560 |
+
<div class="module-badge mb-new">CODE</div>
|
| 561 |
+
<div class="module-num">MODULE 16</div>
|
| 562 |
+
<div class="module-name">Code Intelligence Engine</div>
|
| 563 |
+
<div class="module-desc">AST-aware attention. Multi-file dependency graph.<br>Runtime simulation head. CVE bug pattern library.<br>Test generation. Build/exec tool integration.</div>
|
| 564 |
+
</div>
|
| 565 |
+
|
| 566 |
+
<div class="module">
|
| 567 |
+
<div class="module-badge mb-safe">TRUST</div>
|
| 568 |
+
<div class="module-num">MODULE 17</div>
|
| 569 |
+
<div class="module-name">Knowledge Boundary Detector</div>
|
| 570 |
+
<div class="module-desc">Hallucination risk scorer per claim. Claim classification:<br>known / uncertain / hallucination-risk / out-of-training.<br>3-pass self-consistency check on uncertain outputs.</div>
|
| 571 |
+
</div>
|
| 572 |
+
|
| 573 |
+
</div>
|
| 574 |
+
</div>
|
| 575 |
+
|
| 576 |
+
<!-- TPS -->
|
| 577 |
+
<div class="section">
|
| 578 |
+
<div class="section-label">Performance</div>
|
| 579 |
+
<div class="section-title">Estimated Inference Throughput</div>
|
| 580 |
+
<div class="tps-grid">
|
| 581 |
+
<div class="tps-card">
|
| 582 |
+
<div class="tps-model">LATTICE-120B</div>
|
| 583 |
+
<div class="tps-row">
|
| 584 |
+
<div class="tps-label"><span class="quant">BF16</span><span class="val">~35 TPS</span></div>
|
| 585 |
+
<div class="tps-bar"><div class="tps-fill bf16" style="width:27%"></div></div>
|
| 586 |
+
</div>
|
| 587 |
+
<div class="tps-row">
|
| 588 |
+
<div class="tps-label"><span class="quant">INT8</span><span class="val">~70 TPS</span></div>
|
| 589 |
+
<div class="tps-bar"><div class="tps-fill int8" style="width:54%"></div></div>
|
| 590 |
+
</div>
|
| 591 |
+
<div class="tps-row">
|
| 592 |
+
<div class="tps-label"><span class="quant">INT4</span><span class="val">~130 TPS</span></div>
|
| 593 |
+
<div class="tps-bar"><div class="tps-fill int4" style="width:100%"></div></div>
|
| 594 |
+
</div>
|
| 595 |
+
</div>
|
| 596 |
+
<div class="tps-card">
|
| 597 |
+
<div class="tps-model">LATTICE-430B</div>
|
| 598 |
+
<div class="tps-row">
|
| 599 |
+
<div class="tps-label"><span class="quant">BF16</span><span class="val">~18 TPS</span></div>
|
| 600 |
+
<div class="tps-bar"><div class="tps-fill bf16" style="width:25%"></div></div>
|
| 601 |
+
</div>
|
| 602 |
+
<div class="tps-row">
|
| 603 |
+
<div class="tps-label"><span class="quant">INT8</span><span class="val">~38 TPS</span></div>
|
| 604 |
+
<div class="tps-bar"><div class="tps-fill int8" style="width:53%"></div></div>
|
| 605 |
+
</div>
|
| 606 |
+
<div class="tps-row">
|
| 607 |
+
<div class="tps-label"><span class="quant">INT4</span><span class="val">~72 TPS</span></div>
|
| 608 |
+
<div class="tps-bar"><div class="tps-fill int4" style="width:100%"></div></div>
|
| 609 |
+
</div>
|
| 610 |
+
</div>
|
| 611 |
+
<div class="tps-card">
|
| 612 |
+
<div class="tps-model">LATTICE-671B</div>
|
| 613 |
+
<div class="tps-row">
|
| 614 |
+
<div class="tps-label"><span class="quant">BF16</span><span class="val">~12 TPS</span></div>
|
| 615 |
+
<div class="tps-bar"><div class="tps-fill bf16" style="width:24%"></div></div>
|
| 616 |
+
</div>
|
| 617 |
+
<div class="tps-row">
|
| 618 |
+
<div class="tps-label"><span class="quant">INT8</span><span class="val">~26 TPS</span></div>
|
| 619 |
+
<div class="tps-bar"><div class="tps-fill int8" style="width:52%"></div></div>
|
| 620 |
+
</div>
|
| 621 |
+
<div class="tps-row">
|
| 622 |
+
<div class="tps-label"><span class="quant">INT4</span><span class="val">~50 TPS</span></div>
|
| 623 |
+
<div class="tps-bar"><div class="tps-fill int4" style="width:100%"></div></div>
|
| 624 |
+
</div>
|
| 625 |
+
</div>
|
| 626 |
+
</div>
|
| 627 |
+
</div>
|
| 628 |
+
|
| 629 |
+
<!-- API -->
|
| 630 |
+
<div class="section">
|
| 631 |
+
<div class="section-label">Integration</div>
|
| 632 |
+
<div class="section-title">OpenAI-Compatible API</div>
|
| 633 |
+
<div class="api-block">
|
| 634 |
+
<span class="kw">from</span> openai <span class="kw">import</span> OpenAI<br><br>
|
| 635 |
+
client = <span class="fn">OpenAI</span>(<br>
|
| 636 |
+
base_url=<span class="str">"https://api.provider.com/v1"</span>,<br>
|
| 637 |
+
api_key=<span class="str">"your-key"</span><br>
|
| 638 |
+
)<br><br>
|
| 639 |
+
response = client.chat.completions.<span class="fn">create</span>(<br>
|
| 640 |
+
model=<span class="str">"matrix-lattice-671b"</span>,<br>
|
| 641 |
+
messages=[{<span class="str">"role"</span>: <span class="str">"user"</span>, <span class="str">"content"</span>: <span class="str">"..."</span>}],<br>
|
| 642 |
+
tools=[...],<br>
|
| 643 |
+
extra_body={<br>
|
| 644 |
+
<span class="str">"lattice"</span>: {<br>
|
| 645 |
+
<span class="str">"expose_confidence"</span>: <span class="kw">True</span>, <span class="cm"># X-Lattice-Confidence per chunk</span><br>
|
| 646 |
+
<span class="str">"expose_reasoning_graph"</span>: <span class="kw">False</span>, <span class="cm"># Causal graph trace</span><br>
|
| 647 |
+
<span class="str">"expose_module_trace"</span>: <span class="kw">True</span>, <span class="cm"># Which modules fired</span><br>
|
| 648 |
+
<span class="str">"safety_tier"</span>: <span class="str">"standard"</span>, <span class="cm"># standard | strict | minimal</span><br>
|
| 649 |
+
<span class="str">"agent_role"</span>: <span class="str">"orchestrator"</span>, <span class="cm"># orchestrator | subagent | critic</span><br>
|
| 650 |
+
<span class="str">"persona"</span>: <span class="str">"helpful-assistant"</span> <span class="cm"># Persona Stability Enforcer</span><br>
|
| 651 |
+
}<br>
|
| 652 |
+
}<br>
|
| 653 |
+
)<br><br>
|
| 654 |
+
<span class="cm"># Response extensions:</span><br>
|
| 655 |
+
<span class="cm"># response.lattice.confidence_scores</span><br>
|
| 656 |
+
<span class="cm"># response.lattice.active_modules</span><br>
|
| 657 |
+
<span class="cm"># response.lattice.hallucination_risk</span><br>
|
| 658 |
+
<span class="cm"># response.lattice.expert_clusters_used</span>
|
| 659 |
+
</div>
|
| 660 |
+
</div>
|
| 661 |
+
|
| 662 |
+
<!-- Training Timeline -->
|
| 663 |
+
<div class="section">
|
| 664 |
+
<div class="section-label">Training Plan</div>
|
| 665 |
+
<div class="section-title">Four-Phase Training Strategy</div>
|
| 666 |
+
<div class="timeline">
|
| 667 |
+
<div class="tl-step">
|
| 668 |
+
<div class="tl-num">PHASE 01</div>
|
| 669 |
+
<div class="tl-title">Foundation</div>
|
| 670 |
+
<div class="tl-desc">Mixed distillation from DeepSeek-V3, R1, Llama 4. Web + code + science + multimodal. Context curriculum 8K→1M.</div>
|
| 671 |
+
</div>
|
| 672 |
+
<div class="tl-step">
|
| 673 |
+
<div class="tl-num">PHASE 02</div>
|
| 674 |
+
<div class="tl-title">Module Integration</div>
|
| 675 |
+
<div class="tl-desc">All 17 modules trained with auxiliary losses. Frozen in sequence as each converges.</div>
|
| 676 |
+
</div>
|
| 677 |
+
<div class="tl-step">
|
| 678 |
+
<div class="tl-num">PHASE 03</div>
|
| 679 |
+
<div class="tl-title">Agentic SFT</div>
|
| 680 |
+
<div class="tl-desc">Tool use, MACL, long-horizon planning. Synthetic agentic trajectories. GRPO on task completion.</div>
|
| 681 |
+
</div>
|
| 682 |
+
<div class="tl-step">
|
| 683 |
+
<div class="tl-num">PHASE 04</div>
|
| 684 |
+
<div class="tl-title">Alignment</div>
|
| 685 |
+
<div class="tl-desc">Safety module fine-tuning. Constitutional AI self-critique. Red-team adversarial tuning.</div>
|
| 686 |
+
</div>
|
| 687 |
+
</div>
|
| 688 |
+
</div>
|
| 689 |
+
|
| 690 |
+
<!-- Footer -->
|
| 691 |
+
<div class="footer">
|
| 692 |
+
<span>MATRIX.CORP — LATTICE SPEC V1.0 — 🔴 PLANNED</span>
|
| 693 |
+
<div class="footer-dots">
|
| 694 |
+
<span class="dot cyan">ZENITH</span>
|
| 695 |
+
<span class="dot purple">VORTEX</span>
|
| 696 |
+
<span class="dot" style="--c:#4ade80">TOUCH GRASS</span>
|
| 697 |
+
<span class="dot gold">LATTICE</span>
|
| 698 |
+
</div>
|
| 699 |
+
</div>
|
| 700 |
+
|
| 701 |
+
</div>
|
| 702 |
+
</body>
|
| 703 |
+
</html>
|