AutoPR: Automated Academic Promotion

• AutoPR is a system that transforms dense, multimodal academic content into accessible, factual promotional posts.
• It employs a multi-stage, multi-agent framework that extracts, synthesizes, and adapts text and visuals for specific platform norms.
• Using benchmarks like PRBench, AutoPR optimizes posts based on fidelity, engagement, and alignment, enhancing researcher outreach.

Automatic Promotion (AutoPR) refers to the automated transformation of dense, multimodal academic outputs—such as peer-reviewed articles with their full text, figures, and supplementary materials—into accurate, engaging, and context-optimized promotional posts for public-facing platforms. The primary goal is to reduce the manual burden on researchers and ensure timely, compelling dissemination of scholarly work through rigorous, repeatable processes evaluated on fidelity, engagement, and platform alignment (Chen et al., 10 Oct 2025). AutoPR formalizes promotion as a measurable, multi-objective optimization problem and is supported by new multimodal benchmarks and advanced multi-agent system architectures.

1. Problem Definition and Optimization Objectives

AutoPR is defined as a generative task in which a scholarly document $\mathcal{D}$ (incorporating textual, visual, and supplementary modalities) and a dissemination profile—platform $\mathbb{T}_p$ and audience $\mathbb{T}_a$—serve as the input, and the system outputs a post $P$ optimized for public impact. The core objectives are:

• Fidelity: Maintain factual accuracy and correctly attribute all information.
• Engagement: Maximize attention and appeal, e.g., via compelling hooks, clear narratives, and calls to action.
• Alignment: Adapt tone, structure, and visual style to conform to platform-specific norms (e.g., Twitter threads vs. RedNote posts).

Formally, the task is

$$\hat{P} = \arg\max_P \Pr(P \mid \mathcal{D}, \mathbb{T}_p, \mathbb{T}_a)$$

with the aggregate quality score defined as

$$\max_P \left\{ \alpha_1 S_\text{Fidelity}(P \mid \mathcal{D}) + \alpha_2 S_\text{Align}(P \mid \mathbb{T}_p) + \alpha_3 S_\text{Engage}(P \mid \mathbb{T}_a) \right\}$$

where $S_*$ are expert-annotated scores, and $\alpha_k$ are weighting hyperparameters.

This formalization distills AutoPR into a tractable, measurable research problem and provides a quantitative target for algorithmic improvement (Chen et al., 10 Oct 2025).

2. PRBench: Multimodal Evaluation Benchmark

To support systematic research on AutoPR, PRBench provides a curated dataset and standardized evaluation pipeline:

• Dataset: 512 peer-reviewed articles from diverse disciplines, each paired with a high-quality promotional post curated by human experts.
• Multimodality: Each instance includes raw/full text, visual elements (figures or tables with captions), and supplementary materials as available.
• Expert Annotation: Posts are rated for factual accuracy, narrative hook, clarity, presentation, visual integration, and audience/format alignment.
• Metrics:
  • Fidelity: Authorship/title accuracy and a Factual Checklist Score,

  $$S_\text{Checklist}(P \mid \mathcal{D}) = \frac{\sum_{i=1}^{n} w_i \cdot v(P \mid c_i, \mathcal{D})}{\sum_{i=1}^{n} w_i}$$

  where $c_i$ are factual items, $w_i$ are their weights, and $v(\cdot)$ is a binary or scalar verdict. - Engagement: Narrative strength, logical attractiveness, visual appeal, CTA quality, and human/LLM audience preference. - Alignment: Degree to which style, hashtag strategy, and visual/text integration match target platform conventions.

• Evaluation Protocol: Both scalar rating and pairwise head-to-head preference. LLM-based judging (e.g., Qwen-2.5-VL-72B-Ins) is used for scalable comparison, with calibration against human annotations (Chen et al., 10 Oct 2025).

PRBench enables repeatable measurement of system progress and comparative performance of AutoPR pipelines.

3. System Architecture: PRAgent Multi-Agent Framework

PRAgent is a multi-agent system that operationalizes the AutoPR pipeline in three structured stages:

Stage 1: Content Extraction and Structuring

• Textual Preprocessing: Hierarchical summarization is applied to the raw document text, yielding

$$\mathcal{D}_t^{(\text{sum})} = \text{Summarize}(\text{Parse}(\mathcal{D}_t^{(\text{raw})}))$$

• Visual Extraction: PDF-to-image conversion (PDF2Img) is followed by layout segmentation (e.g., DocLayout-YOLO), and figures are paired with captions using nearest-neighbor mapping:

$$\mathbb{V}_\text{(paired)} = \text{Pair}(\text{LayoutSeg}(\text{PDF2Img}(\mathcal{D})))$$

Stage 2: Multi-Agent Content Synthesis

• Logical Draft Agent: Converts summarized text into a structured, fact-focused draft containing problem statement, core contributions, method, and results.
• Visual Analysis Agent: Interprets each (figure, caption) pair to generate expert commentary for visual assets.
• Textual Enriching & Combination Agents: Synthesize an engaging narrative, incorporate hooks, and placeholder visual references into the draft.

Stage 3: Platform-Specific Adaptation and Orchestration

• Orchestration Agent: Adapts the draft for target platform conventions, e.g. splitting threads for X/Twitter, emphasizing scannability for RedNote, or adopting context-appropriate hashtags and mentions.
• Final Packaging: All components are combined into a publication-ready post, with visual and text-modalities interleaved or linked as required.

This modular multi-agent approach enables explicit reasoning over each dimension—content fidelity, multimodal enrichment, and channel alignment—which is critical for measurable performance gains (Chen et al., 10 Oct 2025).

4. Experimental Results and Performance Metrics

AutoPR systems are evaluated using PRBench, focusing on three axes:

• Fidelity: Metrics demonstrate improvements in factual completeness (e.g., authorship/title fidelity, Factual Checklist Scores).
• Engagement: PRAgent yields substantial increases in reader engagement: total watch time (+604%), likes (+438%), and overall audience interaction (≥2.9×) over direct LLM prompting.
• Alignment: The system significantly improves alignment with platform norms, as assessed by experts and LLM judges.

Detailed evaluation tables compare the performance of direct LLM baselines (various GPT, Qwen, InternVL variants) with PRAgent and ablated versions, consistently demonstrating the benefits of multi-stage, multi-agent orchestration (Chen et al., 10 Oct 2025).

5. Ablation Studies and Component Analysis

Systematic ablations pinpoint the importance of each PRAgent stage:

Stage Omitted	Fidelity	Alignment	Engagement
None (full system)	Highest: e.g. 70.76%	Highest: e.g. 81.25	Highest
Stage 1 (Content Extraction)	↓ to 66.38	–	–
Stage 2 (Content Synthesis)	Moderate drop	↓	↓
Stage 3 (Platform Adaptation)	↓ to 62.94	↓ to 71.36	Largest drop

The largest performance declines are observed when platform-specific adaptation is omitted, indicating that channel and timing modeling are critical for real-world engagement. Comparisons with a naive visual baseline (first-page screenshot) confirm that intelligent visual processing substantially boosts visual-text integration and appeal.

6. Implications, Limitations, and Future Research

AutoPR represents a shift toward scalable, reliable scholarly communication by automating the production of high-quality, platform-aligned promotional content. Notable implications include:

• Scalability: Reduces the time and expertise required from researchers for effective self-promotion.
• Bridging Communities: Translates technical material into accessible public narratives, potentially driving citation and influence.
• Tunable Outputs: Allows for continuous system improvement using well-defined, granular evaluation criteria.

The framework opens several research avenues:

• Enhanced Orchestration: Improved agent cooperation for longer or more complex documents.
• Dynamic Adaptation: Real-time learning from downstream user feedback (e.g., social media analytics).
• Benchmark Expansion: Inclusion of more disciplines and platform types to gauge generalizability.
• Rich Evaluation: Improved LLM judge calibration for subjective metrics (e.g., aesthetics) and deeper integration of emerging LLM architectures.

A plausible implication is that as AutoPR benchmarks and frameworks evolve, they will inform the broader automated science communication domain, enabling not only individual scholar promotion but also institutional and consortium-wide dissemination at scale (Chen et al., 10 Oct 2025).