Some failures may reflect prompt design rather than model limitations, but the underlying issue is one of reasoning rather than instruction-following.

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• Some failures may reflect prompt design rather than model limitations, though code agents avoid errors without promptsclaim0.895
  noted as a possible confound
• Do the documented failures reflect fundamental limitations or a cost-efficiency tradeoff of smaller models?question0.799
  question for future work on frontier models
• Our present forms of planning, design, construction, and production are deeply flawed because they do not include step-by-step adaptation and cannot in principle do so as they are.claim0.790
  Sweeping indictment of current production systems.
• If loss keeps going down on the test set, in the limit the model must be learning to interpret and predict all patterns represented in language, including common-sense reasoning, goal-directed optimization, and deployment of the sum of recorded human knowledge.hypothesis0.778
  Extrapolation of scaling predictive models to AGI.
• Prompt and context design are cognitive-control operations: they toggle latent competencies rather than teaching the model from scratch.claim0.771
  Assertion about the nature of prompt engineering.
• Task instructions can be transcribed into prior beliefs of a generative model, making instruction-following an instance of prior belief specification.claim0.770
  Practical implication showing task instructions are equivalent to inducing prior beliefs in experimental settings
• We stress that in today’s models, this capacity is highly unreliable and context-dependent; however, it may continue to develop with further improvements to model capabilities.quote0.769
  Caveat and forward-looking statement from the abstract.
• Practical context length limitations in language models lead to forgetting outside the window, constraining coherence over time.claim0.768
  Claim about engineering constraint reinforcing the theoretical no-order result