25 lines
1.6 KiB
TeX
25 lines
1.6 KiB
TeX
\begin{abstract}
|
|
Between 2024 and 2026 the Internet Engineering Task Force (IETF) saw a sharp
|
|
rise in Internet-Drafts addressing AI agents and autonomous systems: monthly
|
|
submissions in our corpus grew from an average of 3.7 (June 2024--May 2025) to
|
|
38.8 (since June 2025), peaking at 106 in March 2026---roughly $35\times$ a
|
|
typical 2024 month. We present a quantitative survey of this emerging area
|
|
based on a curated corpus of 524 AI/agent-related IETF Internet-Drafts spanning
|
|
January 2024 to May 2026. We characterise the corpus along four axes: temporal
|
|
submission dynamics, thematic category distribution, authorship and
|
|
working-group structure, and semantic redundancy measured through text
|
|
embeddings. Two findings stand out: the area is overwhelmingly
|
|
\emph{pre-standardization}---87\% of drafts are individual submissions not
|
|
adopted by any working group---and it is semantically dense, with 32\% of
|
|
drafts having a near-duplicate (cosine $>0.9$) elsewhere in the corpus.
|
|
Because the thematic categories are produced by an LLM-assisted pipeline, we
|
|
explicitly quantify their reliability through a two-model re-rating experiment:
|
|
categorical assignment is substantially reproducible (Cohen's $\kappa = 0.65$),
|
|
whereas LLM ordinal \emph{quality} scores such as novelty and overlap are not
|
|
($\kappa_w = 0.13$--$0.21$). We therefore report the category landscape but
|
|
deliberately exclude quality scores from our findings, and we argue this
|
|
distinction is a general caution for the growing practice of LLM-assisted
|
|
corpus analysis. All data, queries, and rating artifacts are released for
|
|
reproduction.
|
|
\end{abstract}
|