[Paper] A Versatile Multimodal Agent for Multimedia Content Generation

Source: arXiv - 2601.03250v1

Overview

The paper presents MultiMedia‑Agent, a unified AI system that can take heterogeneous image and video inputs and automatically produce rich, multimodal outputs (video, audio, text, etc.) end‑to‑end. By combining a data‑generation pipeline, a library of specialized creation tools, and a novel training regime grounded in skill‑acquisition theory, the authors demonstrate that a single agent can outperform a collection of task‑specific generative models.

Key Contributions

• Unified multimodal generation framework – integrates vision, audio, and language tools into one agent capable of handling complex content‑creation pipelines.
• Skill‑acquisition‑inspired training – curates training data and designs a three‑stage fine‑tuning process (base → success‑plan → preference optimization) that mimics how humans acquire and refine creative skills.
• Two‑stage plan correlation strategy – combines self‑correlation (the agent evaluates its own plan) with model‑preference correlation (aligns plans with human‑rated preferences) to produce higher‑quality execution plans.
• Comprehensive evaluation suite – introduces metrics that measure not only output fidelity but also alignment with user preferences across modalities.
• Empirical superiority – shows that MultiMedia‑Agent consistently generates more coherent and appealing multimedia content than state‑of‑the‑art task‑specific generators.

Methodology

1. Data Generation Pipeline – synthetic multimodal datasets are created by pairing raw visual inputs with automatically generated audio, subtitles, and narration using existing generative models. Human annotators then rank the quality of these multimodal bundles, providing preference signals.
2. Tool Library – a modular collection of pre‑trained models (e.g., image‑to‑video, text‑to‑speech, music synthesis) that the agent can invoke via a unified API. Each tool is wrapped as a “skill” the agent can call.
3. Plan Construction & Correlation – the agent first drafts a high‑level plan (what tools to use, in what order).
   • Self‑correlation: the agent predicts the expected quality of its own plan using a learned evaluator.
   • Model‑preference correlation: the plan is compared against the human‑rated preference data; mismatches are penalized.
4. Three‑Stage Training
   • Base Training – the agent learns to map inputs to tool‑selection sequences using the raw synthetic data.
   • Success‑Plan Fine‑tuning – only the top‑ranked (human‑preferred) plans are used to refine the policy, encouraging the agent to imitate successful strategies.
   • Preference Optimization – a reinforcement‑learning‑style step that directly optimizes the preference alignment metric, ensuring the final outputs match what users deem “good”.

Results & Findings

• Quantitative gains: Across benchmark tasks (video captioning → video generation, image → music video, etc.), MultiMedia‑Agent improves BLEU/ROUGE for textual components by ~12 % and MOS (Mean Opinion Score) for audio/video quality by ~0.6 points compared to the best single‑modality baselines.
• Preference alignment: The preference‑optimization stage raises the proportion of outputs that receive top‑3 human rankings from 38 % (baseline) to 71 %.
• Ablation studies confirm that both the two‑stage correlation and the three‑stage training pipeline contribute significantly; removing either drops performance by 8–10 %.

Practical Implications

• End‑to‑end content pipelines – developers can replace a chain of separate models (e.g., separate video editor, TTS engine, subtitle generator) with a single API call to MultiMedia‑Agent, reducing integration overhead.
• Rapid prototyping for media startups – the agent can automatically generate teaser videos, podcasts, or interactive ads from a handful of raw assets, accelerating time‑to‑market.
• Personalized media creation – because the system is trained to align with user preferences, it can be fine‑tuned on a brand’s style guide, enabling on‑demand generation of brand‑consistent multimedia assets.
• Tool‑library extensibility – new generative models (e.g., diffusion‑based video synthesis) can be added as “skills” without retraining the whole agent, making the platform future‑proof.

Limitations & Future Work

• Scalability of preference data – the current pipeline relies on human‑rated synthetic plans; scaling this to massive, diverse domains may be costly.
• Tool dependency – the agent’s performance is bounded by the quality of the underlying tools; failures in any component (e.g., poor TTS) propagate to the final output.
• Real‑time constraints – generating full multimedia sequences still incurs noticeable latency, limiting use cases that require instant feedback.
• Future directions suggested by the authors include:
  1. Incorporating active learning to reduce human annotation effort.
  2. Exploring hierarchical planning for longer‑form content (e.g., full‑length films).
  3. Tighter integration with interactive editing interfaces so developers can intervene mid‑generation.

Authors

• Daohan Zhang
• Wenlin Yao
• Xiaoyang Wang
• Yebowen Hu
• Jiebo Luo
• Dong Yu

Paper Information

• arXiv ID: 2601.03250v1
• Categories: cs.CV
• Published: January 6, 2026
• PDF: Download PDF