AutoPR System: Automated Research Promotion

• AutoPR is the automated transformation of research papers into public-facing promotional posts optimized for factual accuracy, engagement, and platform-specific alignment.
• The PRAgent system employs a multi-agent workflow for content extraction, multi-stage synthesis, and adaptive presentation to tailor content for different social media norms.
• Experimental evaluations using PRBench demonstrate substantial improvements in engagement metrics, validating the benefits of the modular, multi-objective optimization approach.

AutoPR is defined as the automated transformation of research papers into public-facing, platform-optimized promotional posts that are both factually accurate and engaging. The task is cast as a multi-objective optimization problem balancing Fidelity (factual correctness and tone), Engagement (audience appeal and targeting), and Alignment (adherence to channel-specific norms). The core system, PRAgent, operationalizes this with a multi-agent workflow for content extraction, multi-stage synthesis, and adaptive, platform-specific presentation. Evaluation is standardized via PRBench, a benchmark pairing 512 peer-reviewed articles with expertly curated promotional content and annotated along the aforementioned axes. Experimental results indicate substantial improvements over direct LLM-based approaches, with ablations confirming the necessity of each agentic stage and platform modeling (Chen et al., 10 Oct 2025).

1. Task Formulation and Objective Criteria

The AutoPR task is to automate the conversion of a research document

$\mathbb{D} = (D_T, D_V, D_S)$

where $D_T$ is the textual content, $D_V$ visual elements and captions, and $D_S$ supplementary material, into a promotional post $P$ tailored to a dissemination target $(\mathbb{T}_P, \mathbb{T}_A)$, comprising the distribution platform and intended audience. The generation problem is formalized as

$$\hat{P} = \arg\max_{P} \Pr(P \mid \mathbb{D}, \mathbb{T}_P, \mathbb{T}_A)$$

subject to multi-objective constraints. The overall scoring function is:

$$\mathcal{F}(P) = \alpha_1 S_\text{Fidelity}(P \mid \mathbb{D}) + \alpha_2 S_\text{Align}(P \mid \mathbb{T}_P) + \alpha_3 S_\text{Engage}(P \mid \mathbb{T}_A),$$

with non-negative weights $\alpha_i$ controlling the balance among metrics.

This multi-objective approach is necessitated by conflicting requirements: for instance, aggressive engagement hooks or platform stylization may risk factual distortion or inappropriate tone, while strict adherence to accuracy may reduce widespread appeal.

2. Benchmark: PRBench Composition and Evaluation

PRBench is a curated multimodal benchmark constructed to standardize evaluation for AutoPR systems. Each of its 512 samples consists of:

• A peer-reviewed article (with PDF, metadata, and, where available, figures and supplementary assets).
• A reference promotional post, mined from diverse academic social platforms (examples include Twitter/X and RedNote), which is then manually validated for direct relevance and human authorship.

Quality assessment employs a rubric with three principal axes, each further subdivided:

Axis	Example Sub-criteria	Judgment Approach
Fidelity	Authorship accuracy, factual checklist score	LLM-based + expert validation
Engagement	Hook strength, narrative attractivity, CTA	Annotator rubric
Alignment	Norm compliance, hashtags, layout, images	Annotator rubric

For Fidelity, the system employs a weighted checklist metric:

$$S_\text{Checklist}(P \mid \mathbb{D}) = \frac{\sum_i w_i \cdot v(P \mid c_i, \mathbb{D})}{\sum_i w_i}$$

with $w_i$ the expert-assigned importance of fact $c_i$ and $v$ the LLM-assisted factuality assessment.

3. PRAgent System Architecture and Workflow

The PRAgent framework decomposes AutoPR into a staged, multi-agent pipeline:

Stage 1: Content Extraction and Structuring

• Converts PDF or raw document input into machine-readable, hierarchically structured text via parsing and summarization: $$D_T^\text{sum} = \text{Summarize}(\text{Parse}(D_T^\text{raw}))$$.
• Visual data is processed by agents that render high-resolution page images, segment layout elements (e.g., via DocLayout-YOLO), and pair figures with captions using nearest-neighbor matching.

Stage 2: Multi-Agent Synthesis

• The Logical Draft Agent distills the summarized text into a draft that covers research problem, contributions, methodology, and findings.
• The Visual Analysis Agent uses a multimodal LLM to interpret each detected figure and its caption, composing textual interpretations.
• The Textual Enriching Agent adapts and stylizes the draft for social media conventions (e.g., simplifying technical language, adding hooks).
• The Visual-Text-Interleaved Combination Agent integrates selected visuals into the narrative, interleaving images and text coherently.

Stage 3: Platform-Specific Adaptation

• The Orchestrator Agent applies platform-specific adaptation using customizable instructions to refine tone, formatting, hashtag utilization, and user mentions, ensuring maximal alignment with prevailing platform norms.

The pipeline is explicitly modular, allowing for each agent’s configuration and extension, which enables scaling to new platforms or adaptation to emerging content trends.

4. Experimental Results and Quantitative Gains

Empirical evaluation is conducted both on controlled PRBench samples and in live deployments across academic social platforms. Headline findings include:

• On PRBench-Core and real-world deployments, PRAgent achieves a 604% increase in total watch time, a 438% increase in “likes,” and an average 2.9× overall engagement improvement compared to direct LLM prompting.
• Ablation studies indicate:
  • Removal of Stage 1 (content extraction) leads to pronounced Fidelity degradation.
  • Skipping Stage 2 (multi-agent synthesis) harms both Engagement and Alignment.
  • Omission of Stage 3 (platform adaptation) produces the strongest drop in Alignment, illustrating the critical role of targeted modeling and adaptation.

These effects are statistically validated by human preference studies and corroborated by altmetric data.

5. Key Methodological Details and Innovations

Critical components driving PRAgent’s performance are:

• Hierarchical Summarization: The document is recursively summarized to preserve factual content while reducing length, addressing token length limitations and information overload in generative models.
• Multimodal Figure Processing: DocLayout-YOLO and associated vision modules segment, extract, and intelligently pair images and captions. Visual representations are further analyzed by the Visual Analysis Agent for inclusion in text+image narratives.
• Collaborative Agent Design: Specialized agents collaborate in synthesis, supporting division of labor for logical (problem-claim-evidence structure), stylistic (hook, call-to-action), and visualization (interleaved images) tasks.
• Platform Conditioning: The system supports channel-specific adaptation via prompt engineering and agent control. For instance, a Twitter-targeted post will use concise threads, popular hashtags, and mention strategies, while a RedNote post may privilege longer, visually rich stories.

6. Implications and Future Directions

AutoPR, operationalized by frameworks such as PRAgent and assessed via PRBench, provides a tractable and measurable solution to the increasing need for scalable scholarly promotion. Systematic automation carries several consequences:

• Democratization: Researchers with limited promotional expertise or resources can now access quality, targeted promotion.
• Scalability: The agentic and modular structure facilitates rapid adaptation to new social platforms and content norms as dissemination channels evolve.
• Ecosystem Impact: Increased throughput and quality of public-facing research summaries may affect altmetric indices and traditional citation metrics by increasing exposure and downstream scholarly impact.

Future work will likely address: greater generalization to new platforms, cross-lingual content generation, fine-grained control over style and policy, more sophisticated human-AI collaboration via interactive agent systems, and expanded evaluation of impact on both traditional and alternative metrics.

In summary, AutoPR is positioned as a foundational technology for automated, scalable, and platform-aware research promotion, underpinned by robust agentic design, modular content synthesis, and rigorous benchmark-driven evaluation (Chen et al., 10 Oct 2025).