From Conversational Entropy to Cognitive Negentropy

Current Large Language Model (LLM) architectures, while powerful, suffer from a structural bottleneck: the persistence of a linear, chronological interaction flow. This "chat-based" paradigm, inherited from instant messaging, induces exponential informational entropy and context fragmentation during high-complexity research projects. This paper identifies a critical degradation in "Co-Reasoning" efficiency between humans and AI, particularly in fields requiring extreme mathematical and structural precision. To mitigate this "Probabilistic Drift," we propose a paradigm shift toward a "Locus" Architecture Model. Grounded in cognitive psychology and the Method of Loci, this model replaces linear streams with topologically stable, hierarchical Workspaces. By transposing memory spatialization into the digital interface, we enable a non-linear navigation system that aligns with human mental architecture. A core technical innovation presented is the Hierarchical Context Inheritance mechanism. By defining "Global Project Variables" (e.g., phase equations ϕ or physical constants) at a root level, the system ensures their persistent injection across all sub-sessions. This approach drastically reduces token consumption while preserving the semantic integrity of complex datasets. The validity of this model is demonstrated through a rigorous case study: the development of a "Phototronic Brain"—a neuromimetic computing architecture based on polariton condensates. We show how compartmentalizing physical, logical, and algorithmic strata within a "Locus Explorer" eliminates contextual hallucinations and decuples operational efficiency. Ultimately, this transition from a conversational assistant to a functional Exocortex provides the necessary framework for the next generation of human-AI collaborative engineering.