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<title>SociAgentTransformer Architecture</title>
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<text x="360" y="38" text-anchor="middle" class="title">SociAgentTransformer</text>
<text x="360" y="56" text-anchor="middle" class="subtitle">Transformer + Mixture-of-Experts for Agent Decision Making</text>
<text x="360" y="72" text-anchor="middle" class="param-text">1.45M params | ~5.5 MB (fp32) | ~1ms inference (50 agents, ONNX)</text>
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<text x="162" y="236" text-anchor="middle" class="group-dim">[0:6] Big5 + Age</text>
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<text x="557" y="236" text-anchor="middle" class="group-dim">[12:21] 6 needs + urgency</text>
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<text x="360" y="316" text-anchor="middle" class="brace-text">+ learnable positional embeddings per token</text>
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<!-- TRANSFORMER ENCODER (x4) -->
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<text x="360" y="754" text-anchor="middle" class="box-detail">Learned query (1, 1, 128) attends to all 6 tokens via cross-attention</text>
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<text x="180" y="908" text-anchor="middle" class="box-detail">2-layer MLP</text>
<text x="180" y="926" text-anchor="middle" class="param-text">Linear(128, 128)</text>
<text x="180" y="938" text-anchor="middle" class="param-text">GELU + Dropout(0.1)</text>
<text x="180" y="950" text-anchor="middle" class="param-text">Linear(128, 9)</text>
<text x="180" y="972" text-anchor="middle" class="box-dim">(B, 9) logits</text>
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<text x="590" y="950" text-anchor="middle" class="param-text">Linear(64, 1)</text>
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<text x="590" y="1114" text-anchor="middle" class="group-dim">(15 min each)</text>
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<text x="360" y="1184" text-anchor="middle" class="param-text">L = 1.0*CE_action(weighted) + 0.5*CE_location + 0.2*MSE_duration</text>
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AdamW (lr=3e-4, wd=1e-4) | CosineAnnealing | Grad clip=1.0 | 30 epochs | Batch=512
</text>
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<text x="360" y="1268" text-anchor="middle" class="subtitle">ONNX export with opset 17 | CPU inference ~1ms for 50 agents</text>
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