| # Glossary |
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| Shared vocabulary across packages. Prefer package `derived/SPEC.md` for numbers. |
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| | Term | Meaning | |
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| | **MoE** | Mixture of Experts — sparse FFN; only some experts run per token | |
| | **Active params** | Parameters used for a typical forward (not total stored weights) | |
| | **Shared expert** | Expert(s) always on, often combined with routed experts | |
| | **Shared-expert sink** | Shared experts share normalized routing mass with top-k (Inkling) | |
| | **Shared-expert add** | Shared expert output **added** after routed experts (Laguna) — not sink mass | |
| | **Sigmoid router** | Token-choice scores via σ(logits), not softmax (Laguna `LagunaTopKRouter`) | |
| | **Softplus attention gate** | Per-head (or per-element) softplus gate on attn output before `o_proj` (Laguna) | |
| | **QK-norm** | RMSNorm on Q and K per head before RoPE (Laguna) | |
| | **YaRN** | Yet another RoPE extensioN — long-context RoPE scaling (Laguna global layers) | |
| | **OpenMDW** | Open Model Deep Wide license family (Laguna S 2.1: OpenMDW-1.1) | |
| | **pool** | Poolside agent harness; native training/eval environment for Laguna | |
| | **DFlash** | Poolside speculative draft model pair for Laguna serving | |
| | **poolside_v1** | Tool-call + reasoning parser name for vLLM/SGLang Laguna serve | |
| | **Quantile Balancing** | Expert load balance from router-score quantiles (`noaux_tc` sigmoid in Kimi K3) | |
| | **Stable LatentMoE** | K3 MoE framework at 16/896+2 sparsity (`routed_expert_hidden_size=3584 = H/2`) | |
| | **SiTU** | Sigmoid Tanh Unit activation function in Kimi K3 ($\beta=4.0$, linear $\beta=25.0$) | |
| | **MoonViT3d** | 27-layer 3D vision encoder (`d=1024`, patch 14, `patchmergerv2`) in Kimi K3 | |
| | **SWA** | Sliding-window attention — local context only | |
| | **MLA** | Multi-Head Latent Attention — compressed KV (DeepSeek-style; K2.x) | |
| | **KDA** | Kimi Delta Attention — linear/hybrid attention for long sequences | |
| | **AttnRes** | Attention Residuals — selective depth retrieval (K3) | |
| | **Relative PE** | Position via attention bias vs distance, not RoPE (Inkling) | |
| | **RoPE** | Rotary positional embeddings (common baseline) | |
| | **SConv** | Short causal convolution on residual branches (Inkling) | |
| | **hMLP** | Hierarchical MLP patch encoder for images (Inkling) | |
| | **dMel** | Discrete mel-bin audio tokens (Inkling) | |
| | **MTP** | Multi-token prediction / speculative draft heads | |
| | **μP / width multiplier** | Logit scaling by model width factor (Inkling `logits_mup_width_multiplier`) | |
| | **QAT** | Quantization-aware training | |
| | **Thinking effort** | Controllable test-time compute / CoT budget | |
| | **Preserved thinking** | Multi-turn must return prior reasoning content (K3 / Laguna `preserve_thinking`) | |
| | **Harness overfitting** | Model recalls native tool schemas instead of third-party definitions (Laguna limit) | |
| | **Tinker** | Thinking Machines fine-tune platform | |
| | **Primary source** | Official blog, model card, Hub config, paper — not secondary recaps | |
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