We hypothesize that degraded generalization on benchmarks like MMLU may reflect the computational demands of the tasks.

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• Latent methods lack task generalization and are difficult to train with autoregressive parallelization.claim0.793
  Identifies key limitations of latent methods.
• There are fewer representations competent for N tasks than M<N tasks, so training more general models should yield fewer possible solutionshypothesis0.788
  Selective pressure toward convergence via task generality
• Multi-scale competency greatly accelerates evolution and enables generalization.claim0.788
  Central thesis about the role of agency in evolutionary dynamics.
• Default presentation conflates capacity with accessibility, and most evaluation benchmarks measure only default presentation — systematically misreading models.claim0.781
  Argues current evaluation approaches are fundamentally misleading about model capabilities
• The performance drop in factual tasks happens as soon as list length increases to 3, with very little additional degradation from 4 to 5 cities.finding0.774
  Pinpoints list-length 3 as the exact boundary where genuine counting introduces the limitation.
• On GPQA-Diamond multihop questions, activation probes show genuine belief shifts during CoT generation rather than early stabilization, contrasting with MMLUfinding0.774
  Empirical finding contrasting difficult questions with easy ones, supporting genuine reasoning on hard tasks
• Public benchmarks (LMArena) decline as commercial versions (Arena Intelligence) grow; leaderboards face deflation curve.claim0.772
• MAS reveals that numeric representations differ between GRUs trained on Multi-Object, Rounding, and Modulo tasksfinding0.771
  Case study showing MAS can compare specific causal information types across models trained on different tasks.