Cost-efficient models lack not individual skills but their reliable integration under competitive pressure.

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• 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.quote0.798
  Caveat and forward-looking statement from the abstract.
• Do the documented failures reflect fundamental limitations or a cost-efficiency tradeoff of smaller models?question0.790
  question for future work on frontier models
• Can models sustain strategic coherence over time, manage resource constraints, and adapt interactively in multi-agent environments with conflicting incentives?question0.777
  broader framing question for the benchmark
• Benchmarks of this kind test whether models can sustain strategic coherence over time, manage resource constraints, and adapt interactively — capabilities that static benchmarks do not measure.claim0.776
  Broader methodological claim about the need for multi-agent, long-horizon benchmarks.
• Scale is sufficient but not necessarily efficient to reach high levels of intelligence; different methods can scale with different efficiency levelsclaim0.776
  Implication of PRH for 'scale is all you need' argument
• Models that are competent all represent data in a similar way; all strong models are alike, each weak model is weak in its own wayclaim0.775
  Author's interpretation of the VTAB alignment results echoing Tolstoy
• Different models cannot converge to the same representation if they have access to fundamentally different information; convergence is capped by mutual information between input signalsclaim0.769
  Key limitation of the PRH for non-bijective observations
• There are fewer representations competent for N tasks than M<N tasks, so training more general models should yield fewer possible solutionshypothesis0.768
  Selective pressure toward convergence via task generality