The author argues that the common belief that the workflow around a language model, rather than the model itself, constitutes the core system is flawed. While scaffolding like step-by-step plans and critics can be useful during training, they often mask the model's true capabilities when removed for evaluation. The AutoScaffold paper demonstrates that models can internalize learning from scaffolding, performing well even after hints and decompositions are stripped away, suggesting the scaffolding acted as a curriculum rather than a crutch. This contrasts with typical agent demos that measure the effectiveness of the surrounding harness (e.g., retry loops, hardcoded hints) rather than the model's inherent reasoning ability. AI
IMPACT Highlights the need to distinguish between a model's reasoning ability and the effectiveness of its surrounding framework in evaluations.
RANK_REASON The item is an opinion piece discussing the evaluation of AI agents and referencing a research paper.
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