Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration
Jiaheng Liu, Yuanxing Zhang, Shihao Li, Xinping Lei

TL;DR
Vibe AIGC introduces a hierarchical multi-agent orchestration paradigm that enables users to specify high-level vibes, which are then systematically translated into executable workflows, bridging the gap between human intent and AI execution.
Contribution
The paper proposes a novel agentic orchestration framework for content generation, moving beyond prompt engineering to systematic, hierarchical workflows driven by high-level user vibes.
Findings
Enables high-level user vibes to be decomposed into executable agentic pipelines.
Bridges the intent-execution gap in AI content generation.
Transforms AI from a stochastic inference engine to a system-level engineering partner.
Abstract
For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a ``usability ceiling'' manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator's high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the \textbf{Vibe AIGC}, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows. Under this paradigm, the user's role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized…
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Taxonomy
TopicsDigital Humanities and Scholarship · Language and cultural evolution · Multimodal Machine Learning Applications
