Meta Encoder–Decoder Models: A Framework for Generating, Configuring, and Optimizing Encoder–Decoder Architectures
Abstract
Encoder–decoder architectures constitute a fundamental design paradigm in artificial intelligence, supporting tasks that require the transformation of input representations into structured outputs. Conventional encoder–decoder models learn to map inputs to outputs through an encoder, an intermediate representation, and a decoder. However, the architecture, information-transfer mechanism, and optimization strategy are typically specified or searched for separately from the task execution process. This paper introduces the concept of Meta Encoder–Decoder Models (MEDMs), a framework in which an encoder–decoder system operates at a meta-level to generate, configure, select, modify, and optimize other encoder–decoder systems. Rather than merely generating task outputs, a meta encoder–decoder model generates or transforms the mechanisms responsible for producing those outputs.
The proposed framework represents an encoder–decoder system as a configurable object containing its architecture, parameters, representation interface, information-flow constraints, and optimization procedures. Given a task description, data characteristics, resource constraints, and performance feedback, a meta-level system produces a candidate encoder–decoder configuration and evaluates its expected utility. The framework distinguishes architectural generation, parameter generation, component selection, interface adaptation, and iterative structural modification. It also introduces a cost-aware objective that jointly considers task performance, computational expenditure, latency, memory requirements, and robustness. A research agenda is presented for evaluating the framework against conventional encoder–decoder models, neural architecture search, hypernetworks, and meta-learning approaches.
This paper is conceptual and methodological. It does not claim experimentally demonstrated improvements or establish that meta-level generation of encoder–decoder systems is entirely unprecedented. Instead, it formalizes a unified research direction and identifies the conditions under which its practical contributions can be tested.
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BibTeX
@misc{zhang2026meta,
title = {{Meta Encoder–Decoder Models: A Framework for Generating, Configuring, and Optimizing Encoder–Decoder Architectures}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23529902},
url = {https://zenodo.org/records/23529902},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Meta Encoder–Decoder Models: A Framework for Generating, Configuring, and Optimizing Encoder–Decoder Architectures
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23529902
UR - https://zenodo.org/records/23529902
AB - Encoder–decoder architectures constitute a fundamental design paradigm in artificial intelligence, supporting tasks that require the transformation of input representations into structured outputs. Conventional encoder–decoder models learn to map inputs to outputs through an encoder, an intermediate representation, and a decoder. However, the architecture, information-transfer mechanism, and optimization strategy are typically specified or searched for separately from the task execution process. This paper introduces the concept of Meta Encoder–Decoder Models (MEDMs), a framework in which an encoder–decoder system operates at a meta-level to generate, configure, select, modify, and optimize other encoder–decoder systems. Rather than merely generating task outputs, a meta encoder–decoder model generates or transforms the mechanisms responsible for producing those outputs. The proposed framework represents an encoder–decoder system as a configurable object containing its architecture, parameters, representation interface, information-flow constraints, and optimization procedures. Given a task description, data characteristics, resource constraints, and performance feedback, a meta-level system produces a candidate encoder–decoder configuration and evaluates its expected utility. The framework distinguishes architectural generation, parameter generation, component selection, interface adaptation, and iterative structural modification. It also introduces a cost-aware objective that jointly considers task performance, computational expenditure, latency, memory requirements, and robustness. A research agenda is presented for evaluating the framework against conventional encoder–decoder models, neural architecture search, hypernetworks, and meta-learning approaches. This paper is conceptual and methodological. It does not claim experimentally demonstrated improvements or establish that meta-level generation of encoder–decoder systems is entirely unprecedented. Instead, it formalizes a unified research direction and identifies the conditions under which its practical contributions can be tested.
ER -