# draft fragment — reasoning methods section (unfinished)

Prompting techniques that elicit intermediate reasoning have become the dominant
approach to improving large language model performance on multi-step tasks.
Chain-of-thought prompting demonstrated that providing worked examples with
explicit reasoning steps substantially improves arithmetic and symbolic reasoning,
and subsequent work showed that sampling multiple reasoning paths and aggregating
their answers yields further gains. More recent approaches structure reasoning as
explicit search over a tree of partial solutions, trading additional computation
for large improvements on planning-heavy problems.

A parallel line of work connects reasoning to action. Rather than reasoning purely
over parametric knowledge, [CITE react] interleaves reasoning traces with external
actions such as retrieval, which reduces factual hallucination...

(stopped here — need the matrix before I can write the comparison paragraph.
also need a transition into tool-use methods, and citations are all missing)
