Architectural prerequisites versus scaling for specific reasoning behaviors

Ascertain whether specific reasoning behaviors in large language models require architectural prerequisites or instead emerge purely from model scaling.

Background

The paper contrasts training and scaling effects and notes that some reasoning behaviors may depend on architectural features rather than emerge solely from increasing model size.

Resolving this uncertainty would guide architecture design and scaling strategies aimed at eliciting targeted cognitive elements.

References

We cannot explain why RL produces verification but not meta-cognitive monitoring, or whether architectural prerequisites exist for specific behaviors versus emerging from scale \citep{le2025reasoning, das2025can}.

— Cognitive Foundations for Reasoning and Their Manifestation in LLMs  (2511.16660 - Kargupta et al., 20 Nov 2025) in Section: Opportunities and Challenges — Predicting cognitive capabilities from training procedure

As these regularities were hypothesized, we read them as tentative and leave their confirmation to future work.

— A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agents  (2609.19843 - Halimeh et al., 17 Sep 2026) in Section 5.2, Post hoc Exploratory Analysis