Key takeaways
- Novelty is not deployability.
- Reproduce the claim before adapting it.
- Test transfer on your constraints and failure costs.
Separate the claim from the story
Identify the precise claim, baseline, metric, dataset and intervention. Marketing summaries often widen a narrow experimental result into a general capability claim. Keep the original scope intact.
Inspect the evidence boundary
Check data leakage, baseline strength, ablations, variance, compute budget, evaluator design and unavailable implementation details. A result can be legitimate yet impossible to reproduce or irrelevant to your workload.
Climb the evidence ladder
Move through paper review, minimal reproduction, representative offline test, shadow deployment and constrained production trial. Define a stop condition at each step so curiosity does not become an unbounded platform project.
Document negative results
Record why transfer failed: data mismatch, latency, cost, operational complexity or weak effect. Negative evidence prevents another team from repeating the same attractive experiment six months later.
Primary sources
- Artifact Review and BadgingAssociation for Computing Machinery
Limitations
This is an AccessAllGPT evidence framework, not a reproduction study. Some frontier work lacks code or compute-accessible reproduction paths; uncertainty should be stated rather than filled with inference.
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