Glossary
Shared vocabulary across packages. Prefer package derived/SPEC.md for numbers.
| Term | Meaning |
|---|---|
| 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 (Kimi K3 claim) |
| Stable LatentMoE | K3 named MoE framework at 16/896 sparsity |
| 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 |