This arXiv paper explores the critical issue of model collapse arising from the use of AI-synthesized data in training next-generation generative AI models…
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I am working on an evaluation design and would appreciate criticism before running it. Most coding-agent benchmarks collapse the model and its harness into one score. If a run fails, it is difficult to tell whether the cause was model capability, context assembly, task decomposition, tool design, retry policy, or the acceptance gate. A model can also look worse because the harness truncated its output, or look better because the gate only checked for plausible surface markers. The experiment I am considering crosses two independent variables: Workflow: one monolithic task versus decomposition into bounded slices with explicit contracts and acceptance criteria. Model policy: frontier-only versus cheapest-capable with escalation after a capability-graded failure. That produces four cells: fr
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