\section{Related Work}
\label{sec:related}

\paragraph{Quantitative analysis of IETF activity.}
Several studies have examined the IETF corpus empirically. McQuistin
et al.\ characterised the IETF through the lens of RFC deployment,
using a dataset of 8,711 RFCs, 4,512 authors, and 2.4 million emails
to measure shifts in publication rate, author concentration, and
cross-document dependency over time~\cite{mcquistin2021characterising}.
Zhang et al.\ extended this line of work with a longitudinal analysis
of author affiliations across 2001--2023, covering 73,764 individuals
and finding that organisational diversity peaked and then
stagnated~\cite{zhang2025affiliations}. The IAB's own workshop on
Analyzing IETF Data (AID, 2021) surveyed open questions about
what drives drafts to become RFCs and how community diversity
evolves~\cite{tenoever2022aid}. More recently, Jim\'enez applied LLMs
directly to IETF working-group records to automate the generation of
summary reports~\cite{jimenez2024automating}. Our work complements this
body of evidence by providing the first quantitative survey focused
\emph{exclusively on the AI/agent topic area}: we document the growth
trajectory, authorship structure, and thematic distribution of a
cohort that did not exist in large numbers before 2025, and we do
so at the Internet-Draft stage rather than the post-publication RFC
stage.

\paragraph{LLM-assisted text classification and annotation.}
The use of LLMs as automated annotators is well established.
Gilardi et al.\ showed that ChatGPT outperforms crowd-workers on
political-science annotation tasks (stance, relevance, frames)
across 6,183 tweets, with zero-shot accuracy exceeding MTurk workers
by roughly 25 percentage points on average~\cite{gilardi2023chatgpt}.
Tan et al.\ survey the broader landscape of LLMs for data annotation
and synthesis, covering annotation generation, quality assessment, and
downstream utilisation~\cite{tan2024survey}. Yang et al.\ survey
emerging AI-agent communication protocols---the class of specification
documents our corpus comprises---providing context for the
standardisation subjects under analysis~\cite{yang2025agentprotocols}.
Our contribution in this dimension is not classification per se but
the application of the LLM-annotator pipeline to a \emph{technical
standards corpus} rather than short social-media texts, and the
explicit reliability gate described next.

\paragraph{Inter-rater reliability of LLM labels.}
The reproducibility of LLM judgments is contested.
Reiss identified prompt-sensitivity as a reliability hazard, showing
that minor wording changes or repeated identical inputs can shift
ChatGPT's classification output below the thresholds conventionally
required for scientific use~\cite{reiss2023reliability}. Wang et al.\
found that ChatGPT correlates well with human judgments on several
natural-language-generation evaluation dimensions but that the
strength of correlation varies substantially across
tasks~\cite{wang2023nlgeval}. These results motivate---but do not
themselves perform---inter-rater reliability analysis using
established statistics. We use Cohen's $\kappa$~\cite{cohen1960kappa}
with the Landis--Koch interpretation scale~\cite{landis1977kappa} to
report agreement both between two independent LLM re-rating runs and
between those runs and the production labels. To our knowledge, no
prior quantitative survey of an IETF (or comparable standards-body)
corpus has reported this reliability decomposition; the closest
precedent is Jim\'enez~\cite{jimenez2024automating}, who generates
LLM summaries of IETF records but does not measure label
reproducibility.

\paragraph{Embedding-based semantic analysis of document corpora.}
We use cosine similarity over dense text embeddings for the redundancy
analysis. Reimers and Gurevych established that sentence-level
embeddings computed with siamese BERT networks provide efficient and
accurate representations for semantic similarity
search~\cite{reimers2019sbert}; more recent work by Nussbaum et al.\
produces the \texttt{nomic-embed-text} model used in our
pipeline~\cite{nussbaum2024nomic}. The application of embedding-based
near-duplicate detection to a \emph{standards corpus} is, to our
knowledge, novel: prior IETF studies (e.g.,
\cite{mcquistin2021characterising}) rely on structural metadata---author
lists, citation graphs, working-group membership---rather than
semantic similarity over document content.