# RFSeek and Ye Shall Find

arXiv: https://arxiv.org/abs/2509.10216 · HTML: https://mlsanity.com/paper/2509.10216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Semi-Automated Protocol Disambiguation and Code Generation](https://mlsanity.com/paper/2010.04801/) (2020)
- [Prognosis: Closed-Box Analysis of Network Protocol Implementations](https://mlsanity.com/paper/2201.02577/) (2021)
- [Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols](https://mlsanity.com/paper/1810.04755/) (2018)

## Abstract (arXiv)

Requests for Comments (RFCs) are extensive specification documents for network protocols, but their prose-based format and their considerable length often impede precise operational understanding. We present RFSeek, an interactive tool that automatically extracts visual summaries of protocol logic from RFCs. RFSeek leverages large language models (LLMs) to generate provenance-linked, explorable diagrams, surfacing both official state machines and additional logic found only in the RFC text. Compared to existing RFC visualizations, RFSeek's visual summaries are more transparent and easier to audit against their textual source. We showcase the tool's potential through a series of use cases, including guided knowledge extraction and semantic diffing, applied to protocols such as TCP, QUIC, PPTP, and DCCP. In practice, RFSeek not only reconstructs the RFC diagrams included in some specifications, but, more interestingly, also uncovers important logic such as nodes or edges described in the text but missing from those diagrams. RFSeek further derives new visualization diagrams for complex RFCs, with QUIC as a representative case. Our approach, which we term Summary Visualization, highlights a promising direction: combining LLMs with formal, user-customized visualizations to enhance protocol comprehension and support robust implementations.