Reasoning slowdowns are an inevitable consequence of problem hardness in modern AI models

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• "Here, we show that reasoning models exhibit transient chaos, a physical consequence of the computational complexity of difficult tasks."quote0.803
  Load-bearing abstract sentence stating the paper's central discovery.
• Hardness-induced fractal basins are ubiquitous in modern reasoning models, emerging even without hand-designed task-specific solvers, across highly distinct architectures and tasksclaim0.789
  The paper's generalizing claim distinguishing its contribution from prior narrow demonstrations (e.g., Ercsey-Ravasz & Toroczkai's SAT solver).
• In the case of AI, inferences from behavior to features of internal processes are often unreliable, as demonstrated by recent LLMs.claim0.788
  Justifies using internal indicators rather than behavioral tests for AI consciousness
• Most reasoning models are expected to exhibit some form of doubly transient chaos, because properties like convergence-to-fixed-answers act as a dissipation-like mechanismhypothesis0.774
  Forward-looking predictive claim about reasoning models generally, based on the analogy to damped physical systems.
• Complexity can be treated as a physical quantity with consequences for AI inferenceclaim0.767
  Closing philosophical claim situating the results within physical-computation theory.
• Researcher bias and the hardware lottery contribute to apparent convergence in AI models beyond the proposed theoretical pressuresclaim0.760
  Alternative explanation for observed convergence: AI community designs systems to mimic human reasoning
• Modern machines, including quantum computers and machine learning systems, achieve reliable function through uncertain events, making unpredictability a feature rather than a defect.claim0.759
  Argument that predictability is no longer an essential property distinguishing machines from life
• Algorithmic and yes-no thinking has destroyed subtle adaptive processes in real-world buildingclaim0.758