Dimension-Constrained Organizational Relay for Long-Context LLMs

Long-context modeling is important for long-horizon generation tasks, yet larger context windows do not necessarily ensure stable organizational continuity. We propose Dimension-Constrained Organizational Relay (DCOR), a workflow-level framework that reformulates long-horizon generation as the ordered propagation of finite organizational states rather than repeated replay of complete token histories. DCOR introduces Order and Dimension to represent sequential organizational evolution and task-oriented constraints, and comprises Organizational Set Extraction, Dimension-Constrained Generation, Organizational Relay, and Organizational Convergence. We evaluate DCOR on cumulative multi-instance advertisement generation and long-form article generation. Across ten advertisement runs, DCOR generated 259–315 structured creative blocks per run, averaging 279.3 ± 17.9, while maintaining the required format and producing no exact duplicate blocks. At the matched 30-block scale, its main advantage was cumulative structured production rather than the lowest character-level repetition. In long-form generation, DCOR achieved the lowest mean character-level 4-gram and 6-gram repetition compared with Direct Generation, Rolling-Summary Generation, Hierarchical-Outline Generation, and Neural RAG-Memory, while maintaining high Distinct-2 and Distinct-3 values. Ablation results further showed that removing Dimension, Organizational Relay, or Order increased repetition and reduced sustained multi-section expansion. These findings support organizational-state propagation as a complementary mechanism for structured long-horizon generation under restricted local-context conditions.