arXiv:2605.15454v2 Announce Type: replace Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for more steps or following a different internal trajectory. We study this distinction through hidden-state trajectories during chain-of-thought generation across competitive programming, mathematics, and Boolean satisfiability. Raw trajectory geometry is strongly shaped by generation length: longer generations mechanically alter path statistics, so difficulty-dependent comparisons are misl
The increasing complexity and opacity of large language models necessitate deeper understanding of their internal reasoning processes, especially as they tackle more challenging problems.
This research provides a more sophisticated framework for evaluating and understanding AI model performance beyond superficial output length, moving towards deciphering actual internal computational trajectories.
The focus potentially shifts from mere length of thought chains to the qualitative nature of internal model 'movements' during problem-solving, impacting how models are designed, trained, and benchmarked.
- · AI researchers focusing on interpretability
- · Developers of explainable AI (XAI) tools
- · Sectors requiring high-assurance AI (e.g., defense, finance)
- · Benchmarks relying solely on output length as a proxy for reasoning
- · Generative AI models with poor internal trajectory efficiency
- · Interpretability methods that do not consider hidden states
New metrics and methodologies will emerge to analyze and compare AI model reasoning paths.
This understanding could lead to more efficient and robust AI architectures that genuinely 'think' differently and more effectively.
Advanced AI systems, better understood internally, could accelerate progress in agentic systems and complex problem-solving domains.
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Read at arXiv cs.CL