Dynamic Position-Wise Competitive Word Generation and Reordering: A Framework for Competitive Lexical Selection and Sequence Reconstruction
Abstract
Autoregressive language models typically generate text incrementally, conditioning each new token on a previously generated prefix. Although effective, this process couples lexical selection to a predetermined left-to-right generation trajectory. This paper proposes Dynamic Position-Wise Competitive Word Generation and Reordering (DPC-WGR), a conceptual framework in which candidate words compete dynamically for output positions before the final sequence is assembled. Instead of treating the next-token decision as the sole unit of generation, the framework maintains a set of candidate lexical items, evaluates their suitability for multiple positions, resolves competition among candidates and positions, and reconstructs the output sequence according to the resulting assignments.
The proposed framework integrates five operations: candidate generation, position-conditioned evaluation, competitive allocation, iterative reassignment, and sequence reconstruction. A multi-criteria scoring function can incorporate contextual compatibility, task relevance, semantic contribution, dependency compatibility, redundancy, and positional coherence. A constrained assignment mechanism is introduced to prevent incompatible candidates from occupying the same position, while an iterative refinement process allows candidate replacement and position reassignment before finalization. The framework can operate as a modification of an existing autoregressive language model or as a more extensively redesigned iterative generation architecture.
The paper formalizes the proposed mechanism, presents an optimization objective and an algorithmic procedure, and establishes an experimental protocol for evaluating its effectiveness against conventional autoregressive decoding and existing insertion-based generation methods. The central hypothesis is that competitive position assignment may improve global lexical organization in tasks where the importance of a word cannot be adequately assessed from its immediate left-to-right generation context. This hypothesis remains to be tested experimentally. The paper identifies the principal technical challenges, including combinatorial search, grammatical dependencies, candidate-set incompleteness, and the risk that local competition produces globally incoherent sequences.
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BibTeX
@misc{zhang2026dynamic,
title = {{Dynamic Position-Wise Competitive Word Generation and Reordering: A Framework for Competitive Lexical Selection and Sequence Reconstruction}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23614735},
url = {https://zenodo.org/records/23614735},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Dynamic Position-Wise Competitive Word Generation and Reordering: A Framework for Competitive Lexical Selection and Sequence Reconstruction
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23614735
UR - https://zenodo.org/records/23614735
AB - Autoregressive language models typically generate text incrementally, conditioning each new token on a previously generated prefix. Although effective, this process couples lexical selection to a predetermined left-to-right generation trajectory. This paper proposes Dynamic Position-Wise Competitive Word Generation and Reordering (DPC-WGR), a conceptual framework in which candidate words compete dynamically for output positions before the final sequence is assembled. Instead of treating the next-token decision as the sole unit of generation, the framework maintains a set of candidate lexical items, evaluates their suitability for multiple positions, resolves competition among candidates and positions, and reconstructs the output sequence according to the resulting assignments. The proposed framework integrates five operations: candidate generation, position-conditioned evaluation, competitive allocation, iterative reassignment, and sequence reconstruction. A multi-criteria scoring function can incorporate contextual compatibility, task relevance, semantic contribution, dependency compatibility, redundancy, and positional coherence. A constrained assignment mechanism is introduced to prevent incompatible candidates from occupying the same position, while an iterative refinement process allows candidate replacement and position reassignment before finalization. The framework can operate as a modification of an existing autoregressive language model or as a more extensively redesigned iterative generation architecture. The paper formalizes the proposed mechanism, presents an optimization objective and an algorithmic procedure, and establishes an experimental protocol for evaluating its effectiveness against conventional autoregressive decoding and existing insertion-based generation methods. The central hypothesis is that competitive position assignment may improve global lexical organization in tasks where the importance of a word cannot be adequately assessed from its immediate left-to-right generation context. This hypothesis remains to be tested experimentally. The paper identifies the principal technical challenges, including combinatorial search, grammatical dependencies, candidate-set incompleteness, and the risk that local competition produces globally incoherent sequences.
ER -