LLM coding workflow adds a research gate to stop it from implementing every paper it finds
hypergraphr · reddit · 2026-07-29
The author describes building an MCP-based workflow for deep-learning systems that goes from goal decomposition to research, specification, and implementation. The surprise was that once research was retrieved, the LLM tended to implement every method it found—mixing competing papers, adding redundant inputs, and introducing unnecessary abstractions.
To fix that, they added a mandatory editing gate between research and code generation:
- research stays reviewable instead of becoming code directly
- someone explicitly chooses which approaches to keep or exclude
- assumptions and implementation decisions are refined before coding
The broader point: gating model outputs may be essential for reliable engineering workflows, especially when moving from literature review to concrete system design. The author also says the project is part of a broader MCP system and invites others to test or contribute.
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