Context-Grounded Reasoning Emergence in Large Language Models

Abstract

Large language models demonstrate reasoning abilities that appear to emerge when prompts provide
structured context, yet degrade when contextual cues are incomplete or fragmented. This study
examines context-grounded reasoning as a dynamic interaction between model priors, prompt
scaffolding, memory continuity, retrieval-augmented evidence, and optional tool-use during inference.
Experimental evaluation across multi-step analytical tasks shows that models do not inherently reason
in a generalized sense; instead, they construct reasoning chains incrementally, guided by the structure
and stability of the surrounding context. Maintaining continuous conversational state improves
referential coherence, while injecting retrieved evidence constrains reasoning pathways to verifiable
information. Introducing tool affordances enables meta-reasoning behaviors where the model chooses
when to delegate computation or verification steps externally. The results indicate that reliable
reasoning is environment-induced, emerging when context is deliberately shaped rather than assumed.
The study concludes that reasoning performance in real-world deployments should be engineered
through prompt templates, memory management, retrieval grounding, and correction loops, rather than
relying solely on model size or pretraining scale.