Predictive mapping from training procedures to cognitive capabilities

Determine predictive relationships between specific large language model training procedures (e.g., supervised fine-tuning, process supervision, reinforcement learning) and the emergence of particular reasoning capabilities (e.g., meta-cognitive monitoring, representational restructuring) so that one can know a priori which training produces which cognitive capabilities.

Background

The paper introduces a taxonomy of 28 cognitive elements and analyzes 171K model reasoning traces to study how these elements manifest and correlate with success. Despite extensive empirical comparisons and interventions via test-time reasoning guidance, the authors emphasize that there is currently no principled way to predict which training setup yields which cognitive capabilities.

They note strong variations across training paradigms and model families and argue that moving from post-hoc observation to theory-driven experimentation requires establishing predictive mappings from training procedures to emergent cognitive elements.

References

Overall, our analyses expose fundamental gaps: we cannot know which training produces which cognitive capabilities a priori, cannot ensure behaviors transfer beyond training distributions, and cannot validate whether observed patterns reflect genuine cognitive mechanisms or spurious reasoning shortcuts.

— Cognitive Foundations for Reasoning and Their Manifestation in LLMs  (2511.16660 - Kargupta et al., 20 Nov 2025) in Section: Opportunities and Challenges (opening paragraph)

Stage~2 helps on the reasoning-essential task and is neutral on the knowledge-heavy one; we do not yet know how to predict its benefit without running it.

— OpenJev-RLCD: A Working RLCD Implementation  (2609.38850 - Gao et al., 30 Sep 2026) in Section 6, Limitations

The convergence of these studies across different methods and systems implies that there is, in fact, value in having foundational spatial awareness, but they do not prove that a strict training order is required.

— Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models  (2610.19108 - Mallahnezhad et al., 13 Oct 2026) in Section 6, Cross-Dataset Observations