Agentic Search replaces one-shot retrieval with an iterative loop for finding, opening, navigating, reading, and verifying evidence across complex documents.
Why it matters: Use the retrieval loop when answers depend on long documents, multiple sources, tables, or evidence that must be verified. For direct lookups and simple predictable searches, the article says one-shot RAG is often sufficient.
DSpark uses a lightweight draft to propose tokens and a target-model pass to verify them together, aiming to reduce repeated weight-loading cost; reported gains vary by model, text, and hardware.
Why it matters: For deployment, the model and text distribution matter as much as the headline speedup: LFM2.5-8B-A1B has higher acceptance than the dense models but a smaller mean on-device gain, while LFM2.5-1.2B-Instruct’s speedup varies by as much as 52% across text distributions.
Generative recommenders recast recommendation as predicting the next item from a user’s history—and require infrastructure suited to sparse data, huge embedding tables, and tight serving latency.
Why it matters: The practical implication is to match the serving design to the workload: HSTU and DynamicEmb target training and embedding-heavy inference, while Semantic ID-GR handles long contexts and large beam searches. The reported gains depend on the stated benchmark setup, including an ideal all-GPU cache-hit scenario for one HSTU result.