From Articles to Code: On-Demand Generation of Core Algorithms from Scientific Publications

Understand

Maintaining software packages imposes significant costs due to dependency management, bug fixes, and versioning.

• We show that rich method descriptions in scientific publications can serve as standalone specifications for modern large language models (LLMs), enabling on-demand code generation that could supplant human-maintained libraries.
• We benchmark state-of-the-art models (GPT-o4-mini-high, Gemini Pro 2.5, Claude Sonnet 4) by tasking them with implementing a diverse set of core algorithms drawn from original publications.
• Our results demonstrate that current LLMs can reliably reproduce package functionality with performance indistinguishable from conventional libraries.