Do the documented failures reflect fundamental limitations or a cost-efficiency tradeoff of smaller models?

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• Some failures may reflect prompt design rather than model limitations, but the underlying issue is one of reasoning rather than instruction-following.claim0.799
  Acknowledges the confound of not explicitly instructing models to track wealth, yet points to reasoning gaps given code agents avoid errors without prompts.
• Cost-efficient models lack not individual skills but their reliable integration under competitive pressure.claim0.790
  Interpretation that the tested LLMs have the necessary subskills but cannot coordinate them in the adversarial game.
• Some failures may reflect prompt design rather than model limitations, though code agents avoid errors without promptsclaim0.783
  noted as a possible confound
• 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.quote0.782
  Caveat and forward-looking statement from the abstract.
• The benchmark’s diagnostic value lies in identifying why a model loses, not just that it losesclaim0.765
  argues for fine-grained behavioral analysis over aggregate rankings
• Bigger models are more likely to converge to a shared representation than smaller modelshypothesis0.760
  Selective pressure toward convergence via model capacity
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.758
  Opening sentence defining self-evidencing.
• Behavioral evidence from closed-weight models cannot definitively rule out that self-reports reflect training artifacts or sophisticated simulation rather than genuine self-awarenessclaim0.757
  Primary limitation acknowledged by the authors; strongest evidence would require mechanistic activation analysis