Stateful Long-Narrative Reasoning with Cognitive Memory Organization and Evidence Grounding

Abstract

The comprehension and analysis of extended textual narratives present significant challenges for contemporary computational systems. Standard approaches largely rely on stateless processing windows that fail to maintain coherent longitudinal representations of characters, events, and thematic developments over hundreds of thousands of words. This paper proposes a novel framework for stateful long-narrative reasoning that integrates a cognitive memory organization system with rigorous evidence grounding mechanisms. Drawing inspiration from human cognitive architecture, the proposed system segments memory into working, episodic, and semantic tiers, allowing for dynamic updates and retrieval based on contextual relevance. Furthermore, the framework incorporates an evidence grounding module that tethers synthesized reasoning outputs to specific textual citations, thereby mitigating the generation of unsubstantiated claims. Through extensive theoretical analysis and empirical simulations, we demonstrate that the transition from stateless, context-window-bound processing to a stateful memory architecture drastically improves narrative comprehension fidelity. The integration of cognitive memory hierarchies allows the system to consolidate transient events into permanent factual knowledge, while the evidence grounding mechanism ensures high traceability and reliability of the final output. This research provides a foundational blueprint for developing advanced reasoning systems capable of navigating complex, book-length documents in domains ranging from literary analysis to legal and medical record review.