Dynamic Competitive Paragraph Generation and Ordering: A Position-Aware Framework for Candidate Selection, Global Reordering, and Coherent Text Integration
Abstract
Autoregressive language models typically generate text through a sequential token-level process. Although this paradigm supports fluent text generation, the ordering of content in long-form documents remains a challenging problem involving content selection, discourse planning, redundancy control, and global coherence. Existing research has investigated content planning, sentence-order permutation, iterative refinement, and paragraph-level generation. However, these approaches do not necessarily implement a unified mechanism in which candidate paragraphs dynamically compete for discourse positions, update their competitive status as the candidate set evolves, and undergo joint selection and global ordering before final document realization.
This paper proposes Dynamic Competitive Paragraph Generation and Ordering (DCPGO), a conceptual framework that treats paragraph generation as a coupled process of candidate construction, position-aware competition, iterative competition updates, global sequence optimization, and coherent document integration. Each candidate paragraph is evaluated according to multiple criteria, including task relevance, information value, distinctiveness, factual support, discourse suitability, redundancy, dependency compatibility, and generation cost. Competition occurs between candidate paragraphs and potential document positions rather than solely among candidate texts in isolation. The resulting position assignments are then jointly optimized to account for coverage, ordering constraints, transitions, and global document quality. A final integration stage generates connective material and repairs local inconsistencies without unnecessarily rewriting selected content.
The framework defines a position-aware utility function, a pairwise competitive interaction model, a global ordering objective, an iterative algorithm, and a multi-objective training formulation. It also outlines an experimental protocol comparing DCPGO with conventional autoregressive generation, plan-based generation, generate-then-rerank methods, and non-left-to-right paragraph generation. The central hypothesis is that explicit dynamic competition and joint ordering may improve document coherence, content coverage, and redundancy control under appropriate computational budgets. These benefits remain empirical questions requiring systematic evaluation.
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BibTeX
@misc{zhang2026dynamic,
title = {{Dynamic Competitive Paragraph Generation and Ordering: A Position-Aware Framework for Candidate Selection, Global Reordering, and Coherent Text Integration}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23614852},
url = {https://zenodo.org/records/23614852},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamic Competitive Paragraph Generation and Ordering: A Position-Aware Framework for Candidate Selection, Global Reordering, and Coherent Text Integration
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23614852
UR - https://zenodo.org/records/23614852
AB - Autoregressive language models typically generate text through a sequential token-level process. Although this paradigm supports fluent text generation, the ordering of content in long-form documents remains a challenging problem involving content selection, discourse planning, redundancy control, and global coherence. Existing research has investigated content planning, sentence-order permutation, iterative refinement, and paragraph-level generation. However, these approaches do not necessarily implement a unified mechanism in which candidate paragraphs dynamically compete for discourse positions, update their competitive status as the candidate set evolves, and undergo joint selection and global ordering before final document realization. This paper proposes Dynamic Competitive Paragraph Generation and Ordering (DCPGO), a conceptual framework that treats paragraph generation as a coupled process of candidate construction, position-aware competition, iterative competition updates, global sequence optimization, and coherent document integration. Each candidate paragraph is evaluated according to multiple criteria, including task relevance, information value, distinctiveness, factual support, discourse suitability, redundancy, dependency compatibility, and generation cost. Competition occurs between candidate paragraphs and potential document positions rather than solely among candidate texts in isolation. The resulting position assignments are then jointly optimized to account for coverage, ordering constraints, transitions, and global document quality. A final integration stage generates connective material and repairs local inconsistencies without unnecessarily rewriting selected content. The framework defines a position-aware utility function, a pairwise competitive interaction model, a global ordering objective, an iterative algorithm, and a multi-objective training formulation. It also outlines an experimental protocol comparing DCPGO with conventional autoregressive generation, plan-based generation, generate-then-rerank methods, and non-left-to-right paragraph generation. The central hypothesis is that explicit dynamic competition and joint ordering may improve document coherence, content coverage, and redundancy control under appropriate computational budgets. These benefits remain empirical questions requiring systematic evaluation.
ER -