Designing a Fair Benchmark for Coding Agents
💡A practical blueprint for separating model capability from coding-agent harness quality.
⚡ 30-Second TL;DR
What Changed
The proposed 2×2 experiment compares frontier monolith, routed monolith, frontier decomposed, and routed decomposed workflows.
Why It Matters
This framework could help teams identify whether coding-agent failures come from model capability or orchestration design rather than collapsing both into one score. A preregistered, outcome-based evaluation would make comparisons between agent systems more credible and easier to reproduce.
What To Do Next
Implement a shared-budget validator harness for the four experimental cells and preregister its acceptance criteria, retry limits, and failure taxonomy before running agents.
Key Points
- •The proposed 2×2 experiment compares frontier monolith, routed monolith, frontier decomposed, and routed decomposed workflows.
- •All cells would use identical source revisions, tools, retry budgets, validators, final acceptance criteria, and independent verifiers.
- •Primary metrics include cost per accepted change, false acceptance, false rejection, first-pass yield, verification time, and reproducibility.
- •The main unresolved issue is how to normalize budgets without unfairly subsidizing decomposition or hiding slice-level capacity needs.
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Original source: Reddit r/MachineLearning ↗
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