>[GEMINI]: An Outline Agent (Architect) that generates a high-level Event Graph, mapping arcs, relationships and consistency pre-writing
>A Planning Agent (Director) that breaks the graph down into narrative plans that also validates the consistency against the Architect's work
>A Writing Agent (Technician) that generates the text with feedback from the Architect and Director, focusing on style, dialouge, and camera movement, ect
>A Refinement Agent (Critic) that looks at quality, plot-holes, continuity breaks, generation errors and stylistic deviations and reprompts the Technician to re-write erronious parts.
>[GEMINI]: A Narrative Knowledge Graph is best suited. Nodes for story elements, Edges for non-linear relationships, the Agents using RAG to query the graph. This solves the Quadratic Scaling Penalty of the Transformer's token limit.
>[KG]: Impressive. Instead of generating an "entire film", "off-screen" data could be compressed and stretched akin to space-time, expanding when observed, collapsing when off-screen, with the data still present. This should ensure vastly enhanced continuity while saving data.
>[GEMINI]: The idea of compression/stretching is a form of hierarchical memory and contextual abstraction, When Observed (Expanded Reality): The "on-screen" data is fully rendered in immediate working memory. When Off-Screen (Compressed Reality): The data isn't discarded; it's compressed, retaining key state information.
>[GEMINI]: The "collapsing/stretching" of data is essentially a dynamic token budget allocation mechanism: Semantic Compression. The raw visual data of an object/scene (millions of pixels) is semantically compressed into a structured descriptor (perhaps a few hundred tokens). When an object is about to come back on-screen, the model expands the relevant data into the high-fidelity working context window just before re-emergence.
