IX2-0319
① SA Source
- Source: 開啟完整 SA 文章
- Section:
Multi Token Prediction (MTP) - Line hint:
524
Context Before
Source: Brendan Bycroft ↗
Evidence
For Mixture-of-Experts models, different tokens may route to different experts, so verifying multiple draft tokens can activate more experts than single-token decoding and force additional expert weights to be fetched from memory
Context After
Multi-token prediction pursues similar benefits without requiring a separate draft model. Auxiliary prediction heads are added to the model architecture, so a single model can propose several future tokens from the same underlying representation. This improves distribution alignment because the proposals come from the same model that ultimately scores them. Multi-token prediction also avoids the operational complexity of serving an additional model while still enabling multi-token generation strategies but requires the MTP heads to be pretrained alongside the main model.
② Atomic Claim
對 Mixture-of-Experts models，不同 token 可能 route 到不同 experts，因此一次驗證多個 draft tokens 可能啟動更多 experts，迫使系統從記憶體載入額外 expert weights。
- Epistemic Mode:
HYPOTHETICAL - Mapping Status:
COMPLETE
③ Semantic Frame
{
"attribute": "COUNT",
"context_nodes": [],
"entity": {
"id": "04_knowledge_base/Mixture of Experts",
"label": "Mixture-of-Experts"
},
"frame_type": "ATTRIBUTE",
"qualifiers": {
"condition_text": null,
"numeric_mentions": [],
"temporal_mentions": []
},
"value": {
"numeric_mentions": [],
"value_text": "對 Mixture-of-Experts models，不同 token 可能 route 到不同 experts，因此一次驗證多個 draft tokens 可能啟動更多 experts，迫使系統從記憶體載入額外 expert weights。"
}
}④ Canonical Entity Mapping
| Role | Surface Label | Canonical Target |
|---|---|---|
| entity | Mixture-of-Experts | 04_knowledge_base/Mixture of Experts |
⑤ Human Review
請在 Properties 逐項確認：
- 原文 → Atomic Claim 是否忠實
- Atomic Claim → Semantic Frame 是否忠實
- Canonical Entity mapping 是否正確
- Epistemic mode 是否保留原文語氣
- 最後選擇
review_action
Review state
Markdown 內文不是正式 approval。只有 Apply bridge 寫入的 Decision Ledger event 才是正式決策。