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feat(survey): add IETF landscape survey (kappa, phase0, rerate), gaps update; bump wimse-ect; gitignore run logs

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Christian Nennemann
2026-05-25 12:35:31 +02:00
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\section{The Landscape}
\label{sec:landscape}
\subsection{Temporal dynamics}
\label{sec:temporal}
Figure~\ref{fig:temporal} shows monthly submission counts for the clean corpus.
Activity is negligible through most of 2024 (mean 3.7 drafts/month over June
2024--May 2025) and inflects sharply in late 2025: October 2025 jumps to 33
drafts, and monthly counts since June 2025 average 38.8. The peak month is
March 2026 with 106 drafts---roughly $35\times$ a typical 2024 month. We report
this as a peak-to-baseline ratio rather than an ``average growth rate'': the
distribution is dominated by a recent spike, not steady exponential growth. The
final two months (April--May 2026) dip relative to the March peak; because
drafts continue to be submitted and indexed, the tail of the curve should be
read as provisional rather than as a downturn.
\begin{figure}[h]
\centering
\begin{tikzpicture}
\node[left,font=\scriptsize] at (-0.35,2.0) {\rotatebox{90}{Drafts/month}};
\draw[gray!40] (-0.1,0.000) -- (11.60,0.000); \node[left,font=\scriptsize] at (-0.1,0.000) {0};
\draw[gray!40] (-0.1,0.943) -- (11.60,0.943); \node[left,font=\scriptsize] at (-0.1,0.943) {25};
\draw[gray!40] (-0.1,1.887) -- (11.60,1.887); \node[left,font=\scriptsize] at (-0.1,1.887) {50};
\draw[gray!40] (-0.1,2.830) -- (11.60,2.830); \node[left,font=\scriptsize] at (-0.1,2.830) {75};
\draw[gray!40] (-0.1,3.774) -- (11.60,3.774); \node[left,font=\scriptsize] at (-0.1,3.774) {100};
\fill[blue!60!black] (0.00,0) rectangle (0.30,0.151);
\fill[blue!60!black] (0.40,0) rectangle (0.70,0.075);
\fill[blue!60!black] (0.80,0) rectangle (1.10,0.038);
\fill[blue!60!black] (1.20,0) rectangle (1.50,0.189);
\fill[blue!60!black] (1.60,0) rectangle (1.90,0.113);
\fill[blue!60!black] (2.00,0) rectangle (2.30,0.038);
\fill[blue!60!black] (2.40,0) rectangle (2.70,0.075);
\fill[blue!60!black] (2.80,0) rectangle (3.10,0.038);
\fill[blue!60!black] (3.20,0) rectangle (3.50,0.377);
\fill[blue!60!black] (3.60,0) rectangle (3.90,0.038);
\fill[blue!60!black] (4.00,0) rectangle (4.30,0.189);
\fill[blue!60!black] (4.40,0) rectangle (4.70,0.113);
\fill[blue!60!black] (4.80,0) rectangle (5.10,0.189);
\fill[blue!60!black] (5.20,0) rectangle (5.50,0.038);
\fill[blue!60!black] (5.60,0) rectangle (5.90,0.075);
\fill[blue!60!black] (6.00,0) rectangle (6.30,0.264);
\fill[blue!60!black] (6.40,0) rectangle (6.70,0.226);
\fill[blue!60!black] (6.80,0) rectangle (7.10,0.151);
\fill[blue!60!black] (7.20,0) rectangle (7.50,0.226);
\fill[blue!60!black] (7.60,0) rectangle (7.90,0.226);
\fill[blue!60!black] (8.00,0) rectangle (8.30,0.302);
\fill[blue!60!black] (8.40,0) rectangle (8.70,1.245);
\fill[blue!60!black] (8.80,0) rectangle (9.10,0.830);
\fill[blue!60!black] (9.20,0) rectangle (9.50,0.415);
\fill[blue!60!black] (9.60,0) rectangle (9.90,1.925);
\fill[blue!60!black] (10.00,0) rectangle (10.30,2.491);
\fill[blue!60!black] (10.40,0) rectangle (10.70,4.000);
\fill[blue!60!black] (10.80,0) rectangle (11.10,2.943);
\fill[blue!60!black] (11.20,0) rectangle (11.50,2.792);
\node[below,font=\scriptsize] at (0.15,-0.05) {2024-01};
\node[below,font=\scriptsize] at (2.55,-0.05) {2024-07};
\node[below,font=\scriptsize] at (4.95,-0.05) {2025-01};
\node[below,font=\scriptsize] at (7.35,-0.05) {2025-07};
\node[below,font=\scriptsize] at (9.75,-0.05) {2026-01};
\node[below,font=\scriptsize] at (11.35,-0.05) {2026-05};
\end{tikzpicture}
\caption{Monthly counts of AI/agent IETF Internet-Drafts, January 2024--May
2026 ($n=524$). The April--May 2026 tail is provisional (indexing lag).}
\label{fig:temporal}
\end{figure}
\subsection{Category distribution}
\label{sec:categories}
Table~\ref{tab:categories} gives the distribution of drafts by primary
category. The field is concentrated in identity/authentication (141 drafts,
$26.9\%$), agent-to-agent communication protocols (108, $20.6\%$), and
autonomous network operations (64, $12.2\%$). The sparsest areas are
human-agent interaction (5), the residual ``Other'' bucket (9), and model
serving/inference (16); we report these descriptively and draw no normative
conclusion about whether sparse areas \emph{ought} to receive more attention.
Categories are not mutually exclusive: $92.9\%$ of drafts carry more than one
category, reflecting genuine thematic overlap (e.g.\ a draft may concern both
agent identity and authorization). We use the primary (first) category for the
distribution.
\begin{table}[h]
\centering
\begin{tabular}{lr}
\toprule
\textbf{Primary category} & \textbf{Drafts} \\
\midrule
Agent identity / authentication & 141 \\
Agent-to-agent (A2A) protocols & 108 \\
Autonomous network operations & 64 \\
ML traffic management & 48 \\
Data formats / interoperability & 44 \\
Agent discovery / registration & 35 \\
Policy / governance & 30 \\
AI safety / alignment & 24 \\
Model serving / inference & 16 \\
Other AI/agent & 9 \\
Human-agent interaction & 5 \\
\midrule
\textbf{Total} & \textbf{524} \\
\bottomrule
\end{tabular}
\caption{Distribution of the clean IETF corpus by primary category.
$92.9\%$ of drafts also carry one or more secondary categories.}
\label{tab:categories}
\end{table}
\subsection{Authorship and working-group structure}
\label{sec:authors}
The corpus is authored by 619 distinct individuals and is \emph{not}
concentrated: the ten most prolific authors together account for only $10.9\%$
of drafts. The most active contributors---Bing Liu (22 drafts), Nan Geng (21),
and Zhenbin Li (20)---form a cluster around autonomous network operations.
The strongest structural signal is the maturity of the work. Of the 524 drafts,
\textbf{456 ($87\%$) are individual submissions} not adopted by any IETF working
group; only 28 distinct working groups appear across the remainder. The area is
thus overwhelmingly \emph{pre-standardization}: a large volume of individual
proposals competing for attention, with comparatively little that has cleared
the bar of working-group adoption.
\subsection{Semantic redundancy}
\label{sec:redundancy}
Pairwise cosine similarity over the 137{,}026 document pairs has a mean of
$0.711$ (p90 $0.790$, p99 $0.850$, max $1.000$). We define a near-duplicate as a
pair with cosine $>0.9$: there are 125 such pairs, and \textbf{170 drafts
($32.4\%$) have at least one near-duplicate} in the corpus. The four pairs at
cosine $\approx 1.0$ are legitimate---they are individual versus
working-group-adopted versions of the same document (for example
\texttt{draft-fv-rats-ear} and \texttt{draft-ietf-rats-ear})---rather than data
artifacts. Combined with the dominance of individual submissions
(Section~\ref{sec:authors}), the high redundancy is consistent with a young,
crowded design space in which many authors propose overlapping solutions to the
same problems before consolidation occurs.