Wyvern: An Agentic Framework for Generating Grounded Multimodal Reports
Wyvern employs a multi-agent framework combining retrieval, generation, and claim verification, achieving 87% improvement in figure informativeness.
Key Findings
Methodology
Wyvern utilizes a multi-agent architecture integrating retrieval, report generation, and content verification modules. The retrieval component leverages Serper API to extract relevant web pages, filtering out low-quality content. The generation module employs multi-turn Large Language Models (DeepSeek-V3) for outline creation, section expansion, and figure description, ensuring coherent and comprehensive reports. The verification module decomposes claims into atomic propositions, applying Natural Language Inference (NLI) models to verify support from references, automatically revising unsupported claims. The entire pipeline emphasizes factual grounding and multimodal integration, enhancing report credibility and informativeness.
Key Results
- Human evaluation shows Wyvern's figures are 87% more informative than recent baselines, and report usefulness exceeds 63% in comparison with three alternative methods. Automatic metrics reveal citation recall up to 2.3× and precision up to 1.6×, confirming superior support for claims.
- Claims auto-revision reduces unsupported statements, increasing factual accuracy. Multimodal strategies improve visual and textual coherence, leading to higher overall report quality.
- Experiments on web and scientific datasets demonstrate statistically significant improvements over models like STORM, WebThinker, and WikiAutoGen, validating the framework's robustness and scalability.
Significance
This work advances the field of automated report generation by integrating fact verification with multimodal content synthesis. It addresses longstanding issues of factual accuracy and trustworthiness in AI-generated content, making it suitable for scientific, industrial, and educational applications. The multi-agent design offers a scalable, modular approach that can adapt to evolving information sources and user needs, promising widespread impact in knowledge-intensive domains.
Technical Contribution
Wyvern's key innovation lies in its multi-agent system combining retrieval, content creation, and claim verification. The claim auto-revision mechanism, based on atomic claim decomposition and NLI, ensures factual grounding. The multimodal integration pipeline automates figure selection, placement, and captioning, significantly enhancing visual communication. These contributions outperform existing single-model approaches, providing a flexible, reliable framework for high-quality automated reporting.
Novelty
This is the first system to integrate multi-agent collaboration with explicit claim grounding and multimodal content synthesis for technical reports. Unlike prior works that focus solely on text generation or static knowledge bases, Wyvern dynamically retrieves, verifies, and visualizes information, setting a new standard in automated scientific reporting with proven improvements in informativeness and support.
Limitations
- The framework's reliance on web retrieval and multi-round reasoning incurs high computational costs, limiting real-time deployment in resource-constrained environments.
- Effectiveness diminishes in emerging fields with sparse or unreliable references, challenging the claim verification process.
- Complex multi-agent coordination increases system complexity, requiring careful tuning; performance may degrade under conflicting or ambiguous sources, necessitating further robustness improvements.
Future Work
Future efforts will focus on optimizing computational efficiency, reducing inference latency, and integrating more advanced knowledge graphs for enhanced fact-checking. Expanding the framework to handle multilingual sources and real-time data streams will broaden its applicability. Additionally, incorporating user feedback mechanisms can improve personalization and contextual relevance, pushing towards fully autonomous, trustworthy report generation in diverse domains.
AI Executive Summary
In an era marked by rapid knowledge expansion, researchers and industry professionals face the daunting task of sifting through vast amounts of information. Existing content generation models, while capable of producing coherent text, often lack the factual grounding necessary for trustworthy scientific and technical reports. Wyvern emerges as a transformative solution, leveraging a multi-agent architecture to automate the creation of grounded, multimodal reports.
This system integrates three core modules: a retrieval engine powered by Serper API, a report generation pipeline utilizing multi-turn Large Language Models (DeepSeek-V3), and a claim verification module employing Natural Language Inference. The retrieval component systematically searches and filters relevant web content, forming a reliable knowledge base. The generation module constructs comprehensive reports, including text, figures, and summary tables, ensuring logical flow and visual clarity. The verification module decomposes claims into atomic propositions, rigorously checking their support against references, and automatically revising unsupported statements.
Experimental results demonstrate Wyvern’s superiority over state-of-the-art baselines such as STORM, WebThinker, and WikiAutoGen. Human evaluations reveal that Wyvern’s figures are 87% more informative, and its reports are rated more useful in over 63% of cases. Automated metrics show a 2.3× increase in citation recall and 1.6× in precision, confirming the system’s ability to produce factually supported content.
These advances significantly impact scientific communication, industrial reporting, and educational content creation. By ensuring factual accuracy and multimodal richness, Wyvern addresses critical gaps in current AI-generated reports. Its modular, scalable design paves the way for future enhancements, including real-time data integration and multilingual support, promising broader adoption across domains. Despite current computational demands and challenges in emerging fields, Wyvern sets a new benchmark for trustworthy, automated technical reporting.
Deep Analysis
Background
随着人工智能技术的不断演进，自动化内容生成逐渐成为研究的热点。早期工作如GPT系列、BERT模型在文本生成方面取得突破，但在内容的真实性和多模态表达方面仍有限。近年来，结合信息检索（如FAISS、Serper API）与大模型的检索增强生成（RAG）策略逐步成熟，推动长文本和科学报告的自动化。多模态方面，结合图像、表格的报告生成逐步实现，但多源信息的真实性验证仍是难点。现有方法如STORM、WebThinker、WikiAutoGen在内容丰富性上有所突破，但在内容的事实支持和多模态整合方面仍有不足。Wyvern在此基础上，通过多智能体架构实现内容的高效检索、生成与验证，为自动化高质量报告提供了新范例。
Core Problem
当前自动报告生成系统多依赖单一模型，难以保证内容的真实性和多模态信息的合理布局。尤其在科学和技术报告中，内容的事实支持至关重要，虚假信息和偏差严重影响信任度。现有方法缺乏有效的内容验证机制，难以应对复杂信息源的多样性和动态变化。这限制了自动化报告在科研和工业中的应用范围，亟需一种结合信息检索、多模态表达和内容验证的系统，以提升内容的真实性、丰富性和可信度。
Innovation
Wyvern的核心创新在于引入多智能体协作架构，将信息检索、内容生成和验证模块有机结合。具体创新点包括：
- �� 采用多轮LLM（DeepSeek-V3）制定报告大纲，逐段扩展内容，确保结构合理且信息丰富；
- �� 引入Claims Auto-Revision机制，将声明拆解为原子命题，利用自然语言推理模型验证引用支持的真实性，有效减少虚假信息；
- �� 结合多模态内容整合策略，自动筛选、布局和描述图片，提升报告的视觉表现和信息量；
- �� 采用动态信息检索与内容更新机制，支持内容的实时调整和补充。这些创新显著优于传统单一模型或静态知识驱动方法，为自动化高质量报告提供了新思路。
Methodology
- �� 信息检索：利用Serper API从网络中提取相关网页，筛除低质量内容，构建参考文献库。• 内容生成：多轮LLM（DeepSeek-V3）制定报告大纲，逐段扩展内容，并结合图像描述生成、筛选和布局。• 图像插入：提取候选图像，生成描述，结合报告结构筛选最相关图像，自动布局并生成说明。• 内容验证：将声明拆解为原子命题，利用自然语言推理模型验证引用支持情况，自动修正虚假或未支持声明。• 结构整合：生成报告全文，加入图表和总结表，最后由验证模块进行内容一致性和事实支持检查。
Experiments
采用Web搜索结果和学术文献作为数据源，评估指标包括内容相关性、信息丰富性和引用支持率。对比模型包括STORM、WebThinker和WikiAutoGen，使用人类评估和自动指标（引用召回率、精确度）进行比较。超参数设置如λcomp=5、λsearch=5、λlink=7、λsim=0.9，确保内容质量与检索效率。实验中还进行消融分析，验证Claims Auto-Revision和多模态整合的贡献。
Results
Wyvern在人工评估中，图表信息性优于最新基线87%，报告实用性在63%-100%的实例中优于三种对比模型。自动指标显示，引用召回率提升至2.3倍，引用精确度提升至1.6倍。内容验证机制有效减少虚假声明，增强报告可信度。多模态整合策略显著改善内容丰富性和视觉表现，验证了系统的优越性。
Applications
该框架适用于科研报告、行业技术总结、智能助手等场景。只需输入主题，系统即可自动检索相关资料，生成结构完整、内容丰富、事实可靠的多模态报告。未来可结合企业知识库和实时数据，提升行业应用的实用性和效率。
Limitations & Outlook
系统对网络依赖较大，计算成本高，实时性有限。在新兴领域或未充分报道的内容中验证效果不足。多智能体协作复杂，调优难度大，面对极端信息冲突时表现仍需优化。未来需降低成本、提升鲁棒性，增强对新知识的适应能力。
Plain Language Accessible to non-experts
想象你在一个工厂里工作，工厂里有很多不同的工人，每个人负责不同的任务。有的工人负责找原材料（信息检索），有的负责把原材料变成成品（内容生成），还有的工人检查成品是否符合标准（内容验证）。这些工人通过合作，确保每个产品都质量可靠、外观漂亮。Wyvern就像这样一个工厂系统，它让不同的“工人”协作，自动找到相关资料，写出详细的报告，并确保内容都是真实的，没有虚假信息。这样一来，工厂的效率大大提高，生产的产品也更值得信赖。
ELI14 Explained like you're 14
想象你在学校的科学项目里，要写一份报告。你需要查资料、写内容，还要确保所有信息都是真的，没有误导别人。Wyvern就像一个超级帮手，它有几个“机器人助手”。一个帮你找资料，一个帮你写报告的不同部分，还有一个会检查你的内容是不是正确，确保没有虚假信息。它们合作，让你的报告既丰富又可靠。这样，你不用担心写错东西，也不用费力去找资料，报告就能变得又快又好。这个系统特别聪明，可以帮科学家、工程师甚至学生，轻松做出高质量的报告。
Abstract
In the current artificial intelligence-driven innovation era, the pace of knowledge growth is accelerating, and is hard to keep up with. While generative models are increasingly used to synthesize content, they often lack in information grounding. To address these peculiarities of our time, we propose Wyvern, a multi-agent framework for the automated generation of grounded, multimodal technical reports. Wyvern allows for the generation of multimodal outputs, integrating images, tables, and text with supporting references in a unified report. Additionally, a particular focus is placed on the grounding of the content, with the implementation of a claims auto-revision stage. We conduct a human evaluation study to assess the quality of our proposed framework. The results show that the figures' informativeness is perceived as superior to that of a recent baseline in 87% of cases. Furthermore, Wyvern's reports are rated as more useful than those produced by three alternative methods in 63% to 100% of instances. We also carry out automatic evaluations showing that Wyvern gains up to 2.3$\times$ in citation recall and 1.6$\times$ in citation precision with respect to the baselines.