Maswali ya Kusimbua Sambamba ya Mifupa-ya-Mawazo

Majibu na maelezo huonekana unapojibu maswali.

1. Je, hatua ya kwanza ya Mifupa-ya-Mawazo hutoa nini?
   • Jibu kamili la mwisho
   • Kituo cha ukaguzi kilichopangwa vizuri
   • Mifupa fupi ya vichwa vya habari
   • Seti ya lebo za mafunzo
2. Kwa nini hatua ya upanuzi wa uhakika inaweza kukimbia sambamba?
   • Kwa sababu kila hatua inategemea moja uliopita
   • Kwa sababu pointi kwa kiasi kikubwa zinajitegemea
   • Kwa sababu mfano umefunzwa tena kwa usawa
   • Kwa sababu ishara hutolewa nyuma
3. Ni malengo gani kuu ya kizuizi cha SoT katika usimbaji wa kawaida wa LLM?
   • Muda mfuatano wa tokeni kwa ishara
   • Kasi ya kusoma diski
   • Saizi ya msamiati wa ishara
   • Bandwidth ya mtandao kwa mtumiaji
4. Which answer structure is a good fit for Skeleton-of-Thought's parallel expansion?
   • A proof whose next step must use the previous step's newly derived result
   • A single fixed one-word output with no expansion
   • A procedure that requires each section to wait for all earlier outputs
   • Several sections that can be developed largely independently
5. What extra work should a Skeleton-of-Thought latency comparison include?
   • Only the longest point while ignoring every other stage
   • Generating the outline, coordinating point expansion, and assembling the final response
   • Mandatory full model retraining for every answer
   • A guarantee that overhead is always exactly the same fixed duration
6. How can local batched Skeleton-of-Thought decoding expand multiple points?
   • Generate every token of every paragraph in one complete forward pass
   • Retrain a new model for each heading
   • Advance the point sequences together across repeated decoding steps
   • Remove autoregressive dependencies within every point automatically
7. If Skeleton-of-Thought reduces answer latency, what other assessment remains necessary before adopting the workflow?
   • Treat the speedup as proof that all factual claims are correct
   • Measure only the spelling of the model's name
   • Assume outline ordering guarantees that every section agrees
   • Check whether the assembled response remains coherent and meets answer-quality requirements
8. After concurrent point expansion, how should Skeleton-of-Thought outputs be assembled into the answer?
   • Associate expansions with their skeleton points and combine them in the intended outline order
   • Shuffle the sections randomly regardless of their headings
   • Treat the first finished point as the entire answer
   • Discard the outline and keep only token counts