Implementing quantum many-body algorithms from the literature is fragile when executable correctness depends on tacit choices of index order, gauge, fermionic sign, contraction sequence, and scaling strategy. We formulate this as a knowledge-externalization problem and test a staged, human-in-the-loop workflow that converts source papers into reviewed technical specifications before code generation. DMRG from Schollw{ö}ck's review serves as calibration: specification-guided implementations pass all 16 model pairings, compared with 6/13 direct attempts, while a prose-specification ablation retains the improvement when externalized content is preserved without \LaTeX{} form. Pfaffian conversion of HFB states to MPS from a five-page Letter provides a closed-world stress test using standalone NumPy/SciPy/Matplotlib implementations without supplied tensor-network code. The workflow yields 11/26 audited passes, versus none under direct prompting. Cross-specification transfer is asymmetric: GPT~5.5 implements four non-GPT specifications successfully, whereas the reverse direction fails in four tested cases. These results support two distinguishable bottlenecks: paper-to-code ambiguity, reduced by explicit specification, and residual implementation-agent capability. Iterative meta-specification shifts but does not remove the latter. The resulting \emph{Paper-to-Program Many-Body} protocol couples expert review, provenance checks, production-scale gates, and physics oracles to provide an auditable pathway from published many-body theory to validated code.

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