Dynamic Competition for Sentence Positions in Text Generation: A Candidate–Position Matching Framework for Adaptive Sentence Generation and Global Text Organization
Abstract
Autoregressive language models typically generate text through a sequential token-level decoding process. Although effective, this paradigm does not explicitly treat sentence positions as competing destinations for candidate sentences. Existing research has explored sentence ordering, content planning, iterative refinement, insertion-based generation, and non-autoregressive sentence ordering. However, an opportunity remains to investigate a unified generation framework in which candidate sentences dynamically compete for document positions, their assignments can be revised as new candidates become available, and the resulting document is optimized jointly for local sentence quality and global coherence.
This paper proposes Dynamic Competition for Sentence Positions (DCSP), a conceptual framework for sentence-level text generation and organization. DCSP separates candidate generation from sentence-position assignment. A candidate pool is constructed through a language model or another sentence-generation mechanism. Each candidate is evaluated against multiple potential positions using criteria including task relevance, information contribution, coherence, dependency satisfaction, redundancy, and positional suitability. A competition controller then assigns candidates to positions, permits reassignment when the global objective improves, and selects a final ordered sequence subject to semantic and structural constraints. The framework accommodates fixed-length and variable-length documents, as well as sequential, iterative, and partially parallel implementations.
A formal objective is introduced to combine candidate quality, candidate–position compatibility, inter-sentence coherence, coverage, redundancy penalties, and computational costs. The paper also outlines a training strategy, an algorithm, benchmark tasks, baseline comparisons, evaluation metrics, and falsifiable hypotheses. The proposed framework is a research design rather than an empirically validated model. Its potential contribution lies not in sentence ordering alone, which has been studied previously, but in explicitly integrating dynamic candidate–position competition, reassignment, and generation under a unified objective. The effectiveness and novelty of this integration require empirical testing against existing sentence-ordering and text-planning methods.
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BibTeX
@misc{zhang2026dynamic,
title = {{Dynamic Competition for Sentence Positions in Text Generation: A Candidate–Position Matching Framework for Adaptive Sentence Generation and Global Text Organization}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23614698},
url = {https://zenodo.org/records/23614698},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamic Competition for Sentence Positions in Text Generation: A Candidate–Position Matching Framework for Adaptive Sentence Generation and Global Text Organization
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23614698
UR - https://zenodo.org/records/23614698
AB - Autoregressive language models typically generate text through a sequential token-level decoding process. Although effective, this paradigm does not explicitly treat sentence positions as competing destinations for candidate sentences. Existing research has explored sentence ordering, content planning, iterative refinement, insertion-based generation, and non-autoregressive sentence ordering. However, an opportunity remains to investigate a unified generation framework in which candidate sentences dynamically compete for document positions, their assignments can be revised as new candidates become available, and the resulting document is optimized jointly for local sentence quality and global coherence. This paper proposes Dynamic Competition for Sentence Positions (DCSP), a conceptual framework for sentence-level text generation and organization. DCSP separates candidate generation from sentence-position assignment. A candidate pool is constructed through a language model or another sentence-generation mechanism. Each candidate is evaluated against multiple potential positions using criteria including task relevance, information contribution, coherence, dependency satisfaction, redundancy, and positional suitability. A competition controller then assigns candidates to positions, permits reassignment when the global objective improves, and selects a final ordered sequence subject to semantic and structural constraints. The framework accommodates fixed-length and variable-length documents, as well as sequential, iterative, and partially parallel implementations. A formal objective is introduced to combine candidate quality, candidate–position compatibility, inter-sentence coherence, coverage, redundancy penalties, and computational costs. The paper also outlines a training strategy, an algorithm, benchmark tasks, baseline comparisons, evaluation metrics, and falsifiable hypotheses. The proposed framework is a research design rather than an empirically validated model. Its potential contribution lies not in sentence ordering alone, which has been studied previously, but in explicitly integrating dynamic candidate–position competition, reassignment, and generation under a unified objective. The effectiveness and novelty of this integration require empirical testing against existing sentence-ordering and text-planning methods.
ER -