When AI coding agents read too many papers, they try to implement all of them

LLM coding pipelines that pull from research papers have a quiet failure mode: the agent implements every method it retrieved instead of the one the task required. One developer traced the failure to a missing step — a mandatory human specification gate inserted between research and code — and published the artifacts showing what the gate actually changes.

In an r/MachineLearning post titled "My LLM kept implementing every method it found, so I added research and specification gates," the author describes a pipeline running Goal → Decompose → Research → Specification → Implementation. The problem showed up in research: when five papers described five different approaches, "the generated implementation would sometimes combine all of them even when the original engineering goal only required one." Redundant inputs and unnecessary abstractions followed because the model could not separate useful context, interesting alternatives, and actual design decisions.

The fix is a mandatory editing and specification stage between research and code, so the extracted research is reviewable and the implementation choices are refinable before any code is generated. The author's rule, kept verbatim with the original spelling: "research should guild the implementation but it should not become the implementation it's self."

The repository includes a sample data-pipeline block and its spec showing the structure, and a Medium follow-up extends the argument.

The honest limitation: this is one developer's self-published workflow, not a comparative benchmark. The spec-gate claim is falsifiable rather than proven, and adoption across teams is not established.