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Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration
Jiaheng Liu, Yuanxing Zhang, Shihao Li, Xinping Lei
TL;DR
Generative AI’s model-centric scaling has improved fidelity but leaves a usability ceiling: high-level creative intent remains difficult to execute reliably through stochastic, black-box single-shot models. The paper proposes Vibe AIGC, which turns a user’s Vibe into hierarchical multi-agent workflows through a Meta-Planner. It concludes that orchestration-centered generation could support more verifiable, consistent creation of complex digital assets, while lacking objective aesthetic verification and resisting compounded errors.
Problem
Single-shot, model-centric generation leaves a gap between multidimensional human intent and reliable execution, limiting complex creative work.
Method
Vibe AIGC treats the user as a Commander and uses a Meta-Planner to decompose high-level intent into executable, verifiable multi-agent workflows.
Results
The paper argues that shifting from stochastic inference to logical orchestration provides a framework for closing the Intent-Execution Gap and addressing the usability ceiling.
Takeaways & Limitations
The proposed direction moves AIGC research beyond model scaling toward orchestration architecture for long-term consistency and verifiable intent.
Takeaways & Limitations
AIGC lacks an objective verification oracle for subjective qualities such as cinematic atmosphere or melancholic pacing.
Abstract
from arXiv · showhide
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 Meta-Planner then functions as a system architect, deconstructing this ``Vibe'' into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets.
1 Introduction
Model-centric scaling has improved visual fidelity but has reached a usability ceiling because single-shot generation leaves high-level human intent disconnected from execution. Vibe AIGC reframes content creation as hierarchical, multi-agent orchestration directed by a Commander and decomposed by a Meta-Planner.
- The Usability Ceiling: Scaling laws have improved visual fidelity, but professional content creation remains a fragile process of stochastic trial-and-error.The paper identifies this as a usability ceiling in cinematic production and complex narrative synthesis.
- The Intent-Execution Gap: The Intent-Execution Gap separates creators’ multidimensional visions from the black-box behavior of single-shot models.Users must otherwise search for keyword combinations that align with a model’s internal weights.
- The Intent-Execution Gap: Flat architectures lack the hierarchical reasoning and iterative verification needed for long-horizon creative tasks.Hallucinated details can force users to regenerate outputs, wasting computation and frustrating creators.
- Paradigm Shift: Vibe AIGC treats complex media generation as a system-level engineering challenge requiring specialized agentic behaviors.This proposal is inspired by Vibe Coding’s use of natural language as a high-level kernel for autonomous system construction.
- Paradigm Shift: The proposed paradigm shifts from Model-Centric Generation to autonomous synthesis of multi-agent workflows driven by high-level human intent.The user becomes a Commander providing aesthetic, logical, and functional intent, while a Meta-Planner decomposes it into executable and verifiable pipelines.
2 Vibe Coding
Vibe Coding raises the abstraction level of programming by letting users express multidimensional intent rather than formal syntax or one-shot commands. It recasts the human as a Commander while agentic workflows recursively translate that intent into interactive, revisable implementation.
- Definition of Vibe Coding: Vibe Coding removes formal syntax by representing intent through a high-level, multidimensional Vibe.The Vibe includes aesthetic preference, functional goals, and systemic constraints.
- Definition of Vibe Coding: Unlike a one-shot prompt, a Vibe is a continuous latent state maintained through dialogue and interpreted as project atmosphere.The system can make autonomous decisions such as selecting dependencies or following an implied design language.
- User as Commander: The Commander provides strategic vision while the AI determines tactical implementation through agentic workflows.This delegation lets users operate at the level of system design rather than manually controlling low-level assets or code.
- Agentic Orchestration: Recursive orchestration addresses the Intent-Execution Gap by decomposing a creative request into specialized, multi-step operations.The example workflow writes a script, analyzes tempo, generates consistency sheets, and oversees editing.
- Agentic Orchestration: High-level feedback can reconfigure workflow logic instead of merely rerolling a random seed.The paper characterizes this transition as moving from stochastic guessing to logical orchestration.
3 Model-centric Generation
Model-centric video generation has advanced across modalities and fidelity, but computational constraints, stochastic prompting, and difficult composite tasks limit reliable control. Unified architectures broaden task coverage yet still face data-quality and performance challenges relative to specialized models.
- Prevailing Architectures: Video generation has progressed from static images to dynamic video with advances in text alignment, visual fidelity, motion plausibility, and realism.Current research focuses mainly on text-to-image, text-to-video, and image-to-video generation.
- Prevailing Architectures: Latent diffusion Transformers compress video into spacetime patches and denoise them conditionally on text prompts.This patch-based representation supports video generation in a lower-dimensional latent space.
- Prevailing Architectures: Video-data computation costs produce training datasets smaller in scale and conceptual breadth than LLM datasets.The resulting knowledge disparity makes prompt engineering important for aligning queries with learned data distributions.
- Granular Control: Video editing systems modify existing content through textual instructions, object operations, or spatial masks.These methods target more precise changes such as object addition, deletion, or modification.
- Granular Control: Composite reference-generation and editing tasks often fall outside current models’ distributions, producing unpredictable or failed outcomes.Users generally cannot decompose complex creative intent into reliably executable discrete operations.
- Unified Architectures: Unified multimodal models frame tasks as next-token prediction, but still face data-quality and exhaustive-task-enumeration challenges.The passage reports that they lag behind specialized DiT models in generation fidelity and semantic alignment on T2V and I2V tasks.
- Application Challenges: Video-generation workflows commonly use short 5 or 10 second segments that require manual assembly and post-processing.Artifacts include color discrepancies, inconsistent character identity, poor realism, incorrect pacing, and lip-synchronization problems.
4 Preliminary Attempts
Preliminary agentic systems demonstrate a transition from model-centric generation toward collaborative workflows across research promotion, graphic design, and music-video production. These efforts show the value of specialized agents but remain fragmented across domains.
- Exploratory studies mark a transition from model-centric generation to agentic orchestration across creative domains.
- Deep Research: Deep Research uses multi-step search, source cross-verification, and synthesis to build semantic context before content generation.
- AutoPR: AutoPR formalizes scholarly promotion as an agentic pipeline instead of fragmented manual coordination across LLM interfaces and platform constraints.
- Poster Copilot: Poster Copilot translates abstract Vibe instructions into layout, palette, and layer parameters while supporting human-in-the-loop editing.
- AutoMV: AutoMV coordinates screenwriting, character management, and video-generation tools to preserve visual and stylistic consistency across a full-length song.
- Together with related systems, these efforts suggest specialized agents can reason, plan, and maintain long-term consistency, but remain fragmented within their domains.
5 Vibe AIGC
Vibe AIGC addresses the limits of fixed workflows by compiling high-level creative intent into self-organizing, hierarchical multi-agent pipelines. A Meta Planner expands, decomposes, and dynamically maps intent to executable tools and workflows.
- Creators provide abstract, multifaceted intentions, while existing SOPs and manual orchestration impose limited generalization or high cognitive overhead.
- Vibe AIGC replaces preset-process execution with autonomous solution construction driven by natural-language meta-instructions.
- The architecture externalizes semantic entropy reduction through a closed loop centered on a Meta Planner and supported by domain-specific knowledge.
- Meta Planner: The Meta Planner receives natural language and translates it into global system scheduling through reasoning-based dynamic construction.
- Hierarchical orchestration: Hierarchical orchestration converts a macro-level creative SOP blueprint into an algorithmic workflow graph and configured execution structure.
- Meta Planner: The Meta Planner acts as a System Architect that translates natural-language intent into executable system architecture rather than merely generating text or routing requests.
- Intent expansion: Domain-expert knowledge expands sparse subjective instructions into actionable creative constraints, addressing intent sparsity.
- Agentic orchestration: The Planner dynamically compiles creative scripts into execution paths by selecting agents, models, and media modules according to task complexity.
6 Limitations
The paper identifies four limitations: future model scaling could reduce the need for orchestration, high-level control may weaken precision, subjective intent lacks objective verification, and recursive agents can compound errors.
- The Bitter Lesson and Model Centrality: If a single foundational model develops a near-perfect world model, complex orchestration may become unnecessary.
- The Paradox of Control: High-level Commander interaction may trade professional pixel-level precision for convenience and risk homogenizing aesthetic output.
- The Verification Crisis: Because Vibes lack formal specifications and universal tests, orchestration may drift into aesthetic hallucination and miss unstated creative intent.
- Compounding Failures: Recursive multi-agent workflows can compound upstream semantic drift into downstream hallucinations, while current systems lack an Aesthetic Compiler for formal debugging.
7 Future directions
The paper calls for infrastructure and evaluation advances to support Vibe AIGC, including intent-consistency benchmarks, specialized agents, interoperability standards, and intent-to-workflow datasets.
- For Researchers: Researchers should develop Creative Unit Tests and benchmarks for Agentic Logic Consistency beyond FID, CLIP, and perplexity.
- For Industry Leaders: Industry should support specialized, lightweight foundation agents for niche creative tasks instead of pursuing a single general-purpose God-model.
- For Software Architects: Software architects should standardize agent interoperability so systems can share Character Bank, Global Style State, and Context Memory.
- For the Data Science Community: Data scientists should curate Reasoning-in-the-Loop datasets mapping high-level creative intent to hierarchical reasoning and multimodal subtasks.
8 Conclusion
The paper concludes that scaling laws alone cannot close the gap between human imagination and machine execution. It advocates Vibe AIGC, which treats content creation as autonomous multi-agent workflow synthesis and elevates users to Commanders.
- Scaling laws alone can no longer bridge the gap between human imagination and machine execution.
- Vibe AIGC reconceptualizes content creation as the autonomous synthesis of multi-agent workflows.
- By elevating users to Commanders, Vibe AIGC is presented as a solution to the usability ceiling.
- The proposed direction prioritizes verifiable intent, long-term consistency, and collaborative human-AI creative processes.