feat(survey): add IETF landscape survey (kappa, phase0, rerate), gaps update; bump wimse-ect; gitignore run logs
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Submodule workspace/drafts/ietf-wimse-ect updated: 3d01cb32b6...1ddad7de73
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|
||||
\begin{thebibliography}{10}
|
||||
|
||||
\bibitem{cohen1960kappa}
|
||||
Jacob Cohen.
|
||||
\newblock A coefficient of agreement for nominal scales.
|
||||
\newblock {\em Educational and Psychological Measurement}, 20(1):37--46, 1960.
|
||||
|
||||
\bibitem{gilardi2023chatgpt}
|
||||
Fabrizio Gilardi, Meysam Alizadeh, and Ma{\"e}l Kubli.
|
||||
\newblock {ChatGPT} outperforms crowd-workers for text-annotation tasks.
|
||||
\newblock {\em Proceedings of the National Academy of Sciences},
|
||||
120(30):e2305016120, 2023.
|
||||
|
||||
\bibitem{jimenez2024automating}
|
||||
Jaime Jim{\'e}nez.
|
||||
\newblock Automating {IETF} insights generation with {AI}, 2024.
|
||||
|
||||
\bibitem{landis1977kappa}
|
||||
J.~Richard Landis and Gary~G. Koch.
|
||||
\newblock The measurement of observer agreement for categorical data.
|
||||
\newblock {\em Biometrics}, 33(1):159--174, 1977.
|
||||
|
||||
\bibitem{mcquistin2021characterising}
|
||||
Stephen McQuistin, Mladen Karan, Prashant Khare, Colin Perkins, Gareth Tyson,
|
||||
Matthew Purver, Patrick Healey, Waleed Iqbal, Junaid Qadir, and Ignacio
|
||||
Castro.
|
||||
\newblock Characterising the {IETF} through the lens of {RFC} deployment.
|
||||
\newblock In {\em Proceedings of the 21st ACM Internet Measurement Conference
|
||||
({IMC} '21)}, pages 137--149, 2021.
|
||||
|
||||
\bibitem{nussbaum2024nomic}
|
||||
Zach Nussbaum, John~X. Morris, Brandon Duderstadt, and Andriy Mulyar.
|
||||
\newblock Nomic embed: Training a reproducible long context text embedder,
|
||||
2024.
|
||||
|
||||
\bibitem{reimers2019sbert}
|
||||
Nils Reimers and Iryna Gurevych.
|
||||
\newblock Sentence-{BERT}: Sentence embeddings using siamese {BERT}-networks.
|
||||
\newblock In {\em Proceedings of the 2019 Conference on Empirical Methods in
|
||||
Natural Language Processing and the 9th International Joint Conference on
|
||||
Natural Language Processing ({EMNLP-IJCNLP} 2019)}, pages 3982--3992, 2019.
|
||||
|
||||
\bibitem{reiss2023reliability}
|
||||
Michael~V. Reiss.
|
||||
\newblock Testing the reliability of {ChatGPT} for text annotation and
|
||||
classification: {A} cautionary remark, 2023.
|
||||
|
||||
\bibitem{tan2024survey}
|
||||
Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita
|
||||
Bhattacharjee, Mansooreh Karami, Jundong Li, Lu~Cheng, and Huan Liu.
|
||||
\newblock Large language models for data annotation and synthesis: {A} survey.
|
||||
\newblock In {\em Proceedings of the 2024 Conference on Empirical Methods in
|
||||
Natural Language Processing ({EMNLP} 2024)}, pages 930--957, Miami, Florida,
|
||||
USA, 2024.
|
||||
|
||||
\bibitem{tenoever2022aid}
|
||||
Niels ten Oever, Corinne Cath, Mirja K{\"u}hlewind, and Colin~S. Perkins.
|
||||
\newblock Report from the {IAB} workshop on analyzing {IETF} data ({AID}) 2021.
|
||||
\newblock RFC 9307, Internet Architecture Board, September 2022.
|
||||
\newblock Informational.
|
||||
|
||||
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|
||||
Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li,
|
||||
Jinan Xu, Jianfeng Qu, and Jie Zhou.
|
||||
\newblock Is {ChatGPT} a good {NLG} evaluator? {A} preliminary study.
|
||||
\newblock In {\em Proceedings of the 4th Workshop on New Frontiers in
|
||||
Summarization ({NewSumm@EMNLP} 2023)}, 2023.
|
||||
\newblock arXiv:2303.04048.
|
||||
|
||||
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|
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Yingxuan Yang, Huacan Chai, Yuanyi Song, Siyuan Qi, Muning Wen, Ning Li, Junwei
|
||||
Liao, Haoyi Hu, Jianghao Lin, Gaowei Chang, Weiwen Liu, Ying Wen, Yong Yu,
|
||||
and Weinan Zhang.
|
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|
||||
|
||||
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|
||||
Yangjun Zhang, Stephen McQuistin, Mladen Karan, Hugo~Enrique Ramirez-Centeno,
|
||||
Colin Perkins, Gareth Tyson, and Ignacio Castro.
|
||||
\newblock Two decades of {IETF} affiliations: Evolution and impact.
|
||||
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|
||||
({ANRW} '25)}, 2025.
|
||||
|
||||
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46
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\BOOKMARK [1][-]{section.3}{\376\377\000C\000o\000r\000p\000u\000s\000\040\000a\000n\000d\000\040\000M\000e\000t\000h\000o\000d}{}% 3
|
||||
\BOOKMARK [2][-]{subsection.3.1}{\376\377\000C\000o\000r\000p\000u\000s\000\040\000c\000o\000n\000s\000t\000r\000u\000c\000t\000i\000o\000n}{section.3}% 4
|
||||
\BOOKMARK [2][-]{subsection.3.2}{\376\377\000T\000h\000e\000m\000a\000t\000i\000c\000\040\000c\000l\000a\000s\000s\000i\000f\000i\000c\000a\000t\000i\000o\000n}{section.3}% 5
|
||||
\BOOKMARK [2][-]{subsection.3.3}{\376\377\000S\000e\000m\000a\000n\000t\000i\000c\000\040\000e\000m\000b\000e\000d\000d\000i\000n\000g\000s}{section.3}% 6
|
||||
\BOOKMARK [2][-]{subsection.3.4}{\376\377\000R\000e\000p\000r\000o\000d\000u\000c\000i\000b\000i\000l\000i\000t\000y}{section.3}% 7
|
||||
\BOOKMARK [1][-]{section.4}{\376\377\000T\000h\000e\000\040\000L\000a\000n\000d\000s\000c\000a\000p\000e}{}% 8
|
||||
\BOOKMARK [2][-]{subsection.4.1}{\376\377\000T\000e\000m\000p\000o\000r\000a\000l\000\040\000d\000y\000n\000a\000m\000i\000c\000s}{section.4}% 9
|
||||
\BOOKMARK [2][-]{subsection.4.2}{\376\377\000C\000a\000t\000e\000g\000o\000r\000y\000\040\000d\000i\000s\000t\000r\000i\000b\000u\000t\000i\000o\000n}{section.4}% 10
|
||||
\BOOKMARK [2][-]{subsection.4.3}{\376\377\000A\000u\000t\000h\000o\000r\000s\000h\000i\000p\000\040\000a\000n\000d\000\040\000w\000o\000r\000k\000i\000n\000g\000-\000g\000r\000o\000u\000p\000\040\000s\000t\000r\000u\000c\000t\000u\000r\000e}{section.4}% 11
|
||||
\BOOKMARK [2][-]{subsection.4.4}{\376\377\000S\000e\000m\000a\000n\000t\000i\000c\000\040\000r\000e\000d\000u\000n\000d\000a\000n\000c\000y}{section.4}% 12
|
||||
\BOOKMARK [1][-]{section.5}{\376\377\000H\000o\000w\000\040\000R\000e\000l\000i\000a\000b\000l\000e\000\040\000A\000r\000e\000\040\000t\000h\000e\000\040\000L\000a\000b\000e\000l\000s\000?}{}% 13
|
||||
\BOOKMARK [1][-]{section.6}{\376\377\000D\000i\000s\000c\000u\000s\000s\000i\000o\000n\000\040\000a\000n\000d\000\040\000L\000i\000m\000i\000t\000a\000t\000i\000o\000n\000s}{}% 14
|
||||
\BOOKMARK [2][-]{subsection.6.1}{\376\377\000I\000m\000p\000l\000i\000c\000a\000t\000i\000o\000n\000s}{section.6}% 15
|
||||
\BOOKMARK [2][-]{subsection.6.2}{\376\377\000L\000i\000m\000i\000t\000a\000t\000i\000o\000n\000s}{section.6}% 16
|
||||
\BOOKMARK [1][-]{section.7}{\376\377\000C\000o\000n\000c\000l\000u\000s\000i\000o\000n\000\040\000a\000n\000d\000\040\000F\000u\000t\000u\000r\000e\000\040\000W\000o\000r\000k}{}% 17
|
||||
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workspace/drafts/landscape-survey/main.pdf
Normal file
BIN
workspace/drafts/landscape-survey/main.pdf
Normal file
Binary file not shown.
58
workspace/drafts/landscape-survey/main.tex
Normal file
58
workspace/drafts/landscape-survey/main.tex
Normal file
@@ -0,0 +1,58 @@
|
||||
\documentclass[11pt,a4paper]{article}
|
||||
|
||||
% ── Packages ──────────────────────────────────────────────────────────────
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{lmodern}
|
||||
\usepackage[margin=1in]{geometry}
|
||||
\usepackage{graphicx}
|
||||
\usepackage{booktabs}
|
||||
\usepackage{hyperref}
|
||||
\usepackage{xcolor}
|
||||
\usepackage{amsmath}
|
||||
\usepackage{float}
|
||||
\usepackage{caption}
|
||||
\usepackage{tabularx}
|
||||
\usepackage{enumitem}
|
||||
\usepackage{tikz}
|
||||
|
||||
\hypersetup{
|
||||
colorlinks=true,
|
||||
linkcolor=blue!60!black,
|
||||
citecolor=blue!60!black,
|
||||
urlcolor=blue!60!black,
|
||||
}
|
||||
|
||||
% ── Title ─────────────────────────────────────────────────────────────────
|
||||
\title{%
|
||||
\textbf{The AI-Agent Standardization Surge at the IETF:\\
|
||||
A Verified Quantitative Survey of 524 Internet-Drafts (2024--2026)}%
|
||||
}
|
||||
\author{
|
||||
Christian Nennemann\\
|
||||
Independent Researcher\\
|
||||
\texttt{ietf@nennemann.de}
|
||||
}
|
||||
\date{May 2026}
|
||||
|
||||
\begin{document}
|
||||
\maketitle
|
||||
|
||||
\input{sections/00-abstract}
|
||||
|
||||
\noindent\textbf{Keywords:} IETF, Internet-Drafts, AI agents, standardization,
|
||||
landscape analysis, LLM-assisted classification, inter-rater reliability,
|
||||
text embeddings
|
||||
|
||||
\input{sections/01-intro}
|
||||
\input{sections/02-related}
|
||||
\input{sections/03-method}
|
||||
\input{sections/04-landscape}
|
||||
\input{sections/05-reliability}
|
||||
\input{sections/06-discussion}
|
||||
\input{sections/07-conclusion}
|
||||
|
||||
\bibliographystyle{plain}
|
||||
\bibliography{references}
|
||||
|
||||
\end{document}
|
||||
164
workspace/drafts/landscape-survey/references.bib
Normal file
164
workspace/drafts/landscape-survey/references.bib
Normal file
@@ -0,0 +1,164 @@
|
||||
% References for the IETF AI/agent landscape survey.
|
||||
% Real, verifiable entries only. Agent-added entries must be checked.
|
||||
|
||||
% ── IETF / Standards quantitative analysis ────────────────────────────────
|
||||
|
||||
@inproceedings{mcquistin2021characterising,
|
||||
author = {McQuistin, Stephen and Karan, Mladen and Khare, Prashant and
|
||||
Perkins, Colin and Tyson, Gareth and Purver, Matthew and
|
||||
Healey, Patrick and Iqbal, Waleed and Qadir, Junaid and
|
||||
Castro, Ignacio},
|
||||
title = {Characterising the {IETF} Through the Lens of {RFC} Deployment},
|
||||
booktitle = {Proceedings of the 21st ACM Internet Measurement Conference
|
||||
({IMC} '21)},
|
||||
year = {2021},
|
||||
pages = {137--149},
|
||||
doi = {10.1145/3487552.3487821},
|
||||
isbn = {9781450391290},
|
||||
}
|
||||
|
||||
@inproceedings{zhang2025affiliations,
|
||||
author = {Zhang, Yangjun and McQuistin, Stephen and Karan, Mladen and
|
||||
Ramirez-Centeno, Hugo Enrique and Perkins, Colin and
|
||||
Tyson, Gareth and Castro, Ignacio},
|
||||
title = {Two Decades of {IETF} Affiliations: Evolution and Impact},
|
||||
booktitle = {Proceedings of the 2025 Applied Networking Research Workshop
|
||||
({ANRW} '25)},
|
||||
year = {2025},
|
||||
doi = {10.1145/3744200.3744757},
|
||||
}
|
||||
|
||||
@techreport{tenoever2022aid,
|
||||
author = {ten Oever, Niels and Cath, Corinne and K{\"u}hlewind, Mirja
|
||||
and Perkins, Colin S.},
|
||||
title = {Report from the {IAB} Workshop on Analyzing {IETF} Data
|
||||
({AID}) 2021},
|
||||
institution = {Internet Architecture Board},
|
||||
type = {RFC},
|
||||
number = {9307},
|
||||
year = {2022},
|
||||
month = sep,
|
||||
issn = {2070-1721},
|
||||
note = {Informational},
|
||||
doi = {10.17487/RFC9307},
|
||||
}
|
||||
|
||||
@misc{jimenez2024automating,
|
||||
author = {Jim{\'e}nez, Jaime},
|
||||
title = {Automating {IETF} Insights Generation with {AI}},
|
||||
year = {2024},
|
||||
eprint = {2410.13301},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.NI},
|
||||
}
|
||||
|
||||
% ── LLM-assisted annotation / classification ──────────────────────────────
|
||||
|
||||
@article{gilardi2023chatgpt,
|
||||
author = {Gilardi, Fabrizio and Alizadeh, Meysam and Kubli, Ma{\"e}l},
|
||||
title = {{ChatGPT} Outperforms Crowd-Workers for Text-Annotation Tasks},
|
||||
journal = {Proceedings of the National Academy of Sciences},
|
||||
year = {2023},
|
||||
volume = {120},
|
||||
number = {30},
|
||||
pages = {e2305016120},
|
||||
doi = {10.1073/pnas.2305016120},
|
||||
}
|
||||
|
||||
@inproceedings{tan2024survey,
|
||||
author = {Tan, Zhen and Li, Dawei and Wang, Song and Beigi, Alimohammad
|
||||
and Jiang, Bohan and Bhattacharjee, Amrita and Karami,
|
||||
Mansooreh and Li, Jundong and Cheng, Lu and Liu, Huan},
|
||||
title = {Large Language Models for Data Annotation and Synthesis:
|
||||
{A} Survey},
|
||||
booktitle = {Proceedings of the 2024 Conference on Empirical Methods in
|
||||
Natural Language Processing ({EMNLP} 2024)},
|
||||
year = {2024},
|
||||
pages = {930--957},
|
||||
address = {Miami, Florida, USA},
|
||||
doi = {10.18653/v1/2024.emnlp-main.54},
|
||||
}
|
||||
|
||||
@misc{yang2025agentprotocols,
|
||||
author = {Yang, Yingxuan and Chai, Huacan and Song, Yuanyi and
|
||||
Qi, Siyuan and Wen, Muning and Li, Ning and Liao, Junwei
|
||||
and Hu, Haoyi and Lin, Jianghao and Chang, Gaowei and
|
||||
Liu, Weiwen and Wen, Ying and Yu, Yong and Zhang, Weinan},
|
||||
title = {A Survey of {AI} Agent Protocols},
|
||||
year = {2025},
|
||||
eprint = {2504.16736},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.AI},
|
||||
}
|
||||
|
||||
% ── Inter-rater reliability of LLM labels ─────────────────────────────────
|
||||
|
||||
@misc{reiss2023reliability,
|
||||
author = {Reiss, Michael V.},
|
||||
title = {Testing the Reliability of {ChatGPT} for Text Annotation
|
||||
and Classification: {A} Cautionary Remark},
|
||||
year = {2023},
|
||||
eprint = {2304.11085},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CL},
|
||||
}
|
||||
|
||||
@inproceedings{wang2023nlgeval,
|
||||
author = {Wang, Jiaan and Liang, Yunlong and Meng, Fandong and
|
||||
Sun, Zengkui and Shi, Haoxiang and Li, Zhixu and Xu, Jinan
|
||||
and Qu, Jianfeng and Zhou, Jie},
|
||||
title = {Is {ChatGPT} a Good {NLG} Evaluator? {A} Preliminary Study},
|
||||
booktitle = {Proceedings of the 4th Workshop on New Frontiers in
|
||||
Summarization ({NewSumm@EMNLP} 2023)},
|
||||
year = {2023},
|
||||
note = {arXiv:2303.04048},
|
||||
}
|
||||
|
||||
% ── Cohen's kappa / Landis-Koch (methodological) ──────────────────────────
|
||||
|
||||
@article{cohen1960kappa,
|
||||
author = {Cohen, Jacob},
|
||||
title = {A Coefficient of Agreement for Nominal Scales},
|
||||
journal = {Educational and Psychological Measurement},
|
||||
year = {1960},
|
||||
volume = {20},
|
||||
number = {1},
|
||||
pages = {37--46},
|
||||
doi = {10.1177/001316446002000104},
|
||||
}
|
||||
|
||||
@article{landis1977kappa,
|
||||
author = {Landis, J. Richard and Koch, Gary G.},
|
||||
title = {The Measurement of Observer Agreement for Categorical Data},
|
||||
journal = {Biometrics},
|
||||
year = {1977},
|
||||
volume = {33},
|
||||
number = {1},
|
||||
pages = {159--174},
|
||||
doi = {10.2307/2529310},
|
||||
}
|
||||
|
||||
% ── Text embeddings / semantic similarity ─────────────────────────────────
|
||||
|
||||
@inproceedings{reimers2019sbert,
|
||||
author = {Reimers, Nils and Gurevych, Iryna},
|
||||
title = {Sentence-{BERT}: Sentence Embeddings using Siamese {BERT}-Networks},
|
||||
booktitle = {Proceedings of the 2019 Conference on Empirical Methods in
|
||||
Natural Language Processing and the 9th International Joint
|
||||
Conference on Natural Language Processing
|
||||
({EMNLP-IJCNLP} 2019)},
|
||||
year = {2019},
|
||||
pages = {3982--3992},
|
||||
doi = {10.18653/v1/D19-1410},
|
||||
}
|
||||
|
||||
@misc{nussbaum2024nomic,
|
||||
author = {Nussbaum, Zach and Morris, John X. and Duderstadt, Brandon
|
||||
and Mulyar, Andriy},
|
||||
title = {Nomic Embed: Training a Reproducible Long Context Text
|
||||
Embedder},
|
||||
year = {2024},
|
||||
eprint = {2402.01613},
|
||||
archivePrefix = {arXiv},
|
||||
primaryClass = {cs.CL},
|
||||
}
|
||||
24
workspace/drafts/landscape-survey/sections/00-abstract.tex
Normal file
24
workspace/drafts/landscape-survey/sections/00-abstract.tex
Normal file
@@ -0,0 +1,24 @@
|
||||
\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}
|
||||
61
workspace/drafts/landscape-survey/sections/01-intro.tex
Normal file
61
workspace/drafts/landscape-survey/sections/01-intro.tex
Normal file
@@ -0,0 +1,61 @@
|
||||
\section{Introduction}
|
||||
\label{sec:intro}
|
||||
|
||||
Standardization activity around AI agents and autonomous systems has surged at
|
||||
the Internet Engineering Task Force (IETF). In the corpus studied here, monthly
|
||||
submissions of AI/agent-related Internet-Drafts grew from an average of $3.7$
|
||||
per month over June 2024--May 2025 to $38.8$ per month since June 2025, peaking
|
||||
at $106$ drafts in March 2026---roughly $35\times$ a typical 2024 month. A surge
|
||||
of this size, concentrated in barely a year, makes the area difficult to
|
||||
navigate: there is no settled taxonomy, the boundaries between proposals are
|
||||
fluid, and it is unclear how much of the activity represents distinct technical
|
||||
work versus overlapping attempts at the same problems.
|
||||
|
||||
A natural response is to characterise the space quantitatively, and
|
||||
LLM-assisted corpus analyses are an increasingly common instrument for doing
|
||||
so---using a large language model to classify documents, score them, and
|
||||
summarise trends at a scale that manual reading cannot reach
|
||||
\cite{tan2024survey,gilardi2023chatgpt}. Such studies, however, rarely report whether the labels they rely on
|
||||
are reproducible. An LLM applied to the same documents under a different model,
|
||||
or even a different run, may produce different categories and scores; without a
|
||||
reliability check, it is impossible to tell which conclusions rest on stable
|
||||
signal and which on rater noise.
|
||||
|
||||
To our knowledge no verified quantitative survey of the AI-agent
|
||||
standardization space at the IETF exists. We address this gap with a
|
||||
descriptive survey that is explicit about which of its measurements can be
|
||||
trusted. This paper is deliberately neutral: it characterises the landscape
|
||||
rather than advocating for any particular proposal or design.
|
||||
|
||||
\paragraph{Contributions.}
|
||||
\begin{itemize}[leftmargin=1.4em,topsep=2pt,itemsep=1pt]
|
||||
\item A curated corpus of \textbf{524} AI/agent-related IETF Internet-Drafts
|
||||
spanning January 2024 to May 2026, constructed by keyword candidate
|
||||
selection followed by explicit false-positive filtering
|
||||
(Section~\ref{sec:method}).
|
||||
\item A characterization of the corpus along four axes---temporal submission
|
||||
dynamics, thematic category distribution, authorship and working-group
|
||||
structure, and semantic redundancy measured through text embeddings
|
||||
(Section~\ref{sec:landscape}).
|
||||
\item An explicit two-model inter-rater reliability check that separates the
|
||||
labels we can trust from those we cannot: categorical assignment is
|
||||
substantially reproducible (Cohen's $\kappa \approx 0.65$), whereas the
|
||||
LLM ordinal \emph{quality} scores are not ($\kappa_w = 0.13$--$0.21$ for
|
||||
the least stable dimensions). We report the former and exclude the
|
||||
latter (Section~\ref{sec:reliability}).
|
||||
\item A full release of the data, queries, and rating artifacts used in the
|
||||
analysis, so that every reported number can be recomputed.
|
||||
\end{itemize}
|
||||
|
||||
\paragraph{Roadmap.}
|
||||
Section~\ref{sec:method} describes corpus construction, the LLM-assisted
|
||||
classification pipeline, and the embedding setup.
|
||||
Section~\ref{sec:landscape} presents the landscape across the four axes:
|
||||
temporal dynamics (Section~\ref{sec:temporal}), category distribution
|
||||
(Section~\ref{sec:categories}), authorship and working-group structure
|
||||
(Section~\ref{sec:authors}), and semantic redundancy
|
||||
(Section~\ref{sec:redundancy}).
|
||||
Section~\ref{sec:reliability} reports the two-model reliability experiment that
|
||||
underwrites the category labels and disqualifies the quality scores.
|
||||
Section~\ref{sec:discussion} discusses implications and limitations, and
|
||||
Section~\ref{sec:conclusion} concludes with directions for future work.
|
||||
76
workspace/drafts/landscape-survey/sections/02-related.tex
Normal file
76
workspace/drafts/landscape-survey/sections/02-related.tex
Normal file
@@ -0,0 +1,76 @@
|
||||
\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.
|
||||
57
workspace/drafts/landscape-survey/sections/03-method.tex
Normal file
57
workspace/drafts/landscape-survey/sections/03-method.tex
Normal file
@@ -0,0 +1,57 @@
|
||||
\section{Corpus and Method}
|
||||
\label{sec:method}
|
||||
|
||||
\subsection{Corpus construction}
|
||||
|
||||
Internet-Drafts were collected from the IETF Datatracker. Candidate documents
|
||||
were identified by keyword and topic matching for AI-agent and autonomous-system
|
||||
terminology (e.g.\ ``agent'', ``autonomous'', ``LLM'', ``inference''), then
|
||||
filtered to remove false positives---documents in which such terms occur in an
|
||||
unrelated sense (``user agent'', ``autonomous system'' in the BGP sense). Of
|
||||
597 candidate IETF documents, 73 ($12.2\%$) were flagged as false positives and
|
||||
excluded, yielding a clean corpus of \textbf{524 IETF Internet-Drafts}.
|
||||
|
||||
We restrict this survey to IETF Internet-Drafts. The collection pipeline also
|
||||
ingests documents from other standards bodies (ISO, ITU, ETSI, NIST, W3C), but
|
||||
those exhibit heterogeneous and frequently missing publication dates and a
|
||||
different document model; mixing them would compromise the temporal and
|
||||
authorship analyses. All 524 IETF drafts carry ISO-8601 submission timestamps,
|
||||
so the IETF-only scope is also the cleanest with respect to date quality. The
|
||||
corpus snapshot used throughout is \texttt{data/drafts.db} as of 2026-05-23.
|
||||
|
||||
\subsection{Thematic classification}
|
||||
|
||||
Each draft is assigned to one or more of eleven thematic categories
|
||||
(Table~\ref{tab:categories}) by an LLM-assisted rating pipeline. The classifier
|
||||
is prompted with the draft's title, metadata, and abstract (truncated to the
|
||||
first 2000 characters), and returns a JSON object containing the category
|
||||
assignment and five ordinal $1$--$5$ quality dimensions (novelty, maturity,
|
||||
overlap, momentum, relevance). We emphasise two properties of this pipeline
|
||||
that bear directly on interpretation:
|
||||
|
||||
\begin{itemize}[leftmargin=1.4em,topsep=2pt,itemsep=1pt]
|
||||
\item \textbf{Abstract-only.} Ratings are derived from the abstract, not the
|
||||
full document text. This keeps the pipeline cheap and uniform but means
|
||||
classifications reflect how a draft \emph{presents} itself, not a full
|
||||
reading of its mechanics.
|
||||
\item \textbf{Model-derived.} Categories and scores are produced by a large
|
||||
language model (Claude Sonnet, \texttt{claude-sonnet-4-20250514}).
|
||||
Section~\ref{sec:reliability} quantifies how reproducible these labels
|
||||
are; the headline result is that we trust the categories but not the
|
||||
ordinal scores.
|
||||
\end{itemize}
|
||||
|
||||
\subsection{Semantic embeddings}
|
||||
|
||||
For the redundancy analysis (Section~\ref{sec:redundancy}) each draft is
|
||||
embedded into a 768-dimensional vector using the \texttt{nomic-embed-text}
|
||||
model run locally via Ollama. Embedding coverage is complete (524/524). We
|
||||
compute cosine similarity over all $\binom{524}{2}=137{,}026$ document pairs.
|
||||
|
||||
\subsection{Reproducibility}
|
||||
|
||||
The pipeline, queries, and the two-model re-rating used for the reliability
|
||||
analysis are released as scripts (\texttt{scripts/survey-phase0.py},
|
||||
\texttt{scripts/rerate-intercoder.py}, \texttt{scripts/survey-kappa.py}). The
|
||||
re-rating itself was executed through the Anthropic Batch API at a total cost of
|
||||
USD~2.41. Raw model outputs are released as line-delimited JSON.
|
||||
137
workspace/drafts/landscape-survey/sections/04-landscape.tex
Normal file
137
workspace/drafts/landscape-survey/sections/04-landscape.tex
Normal file
@@ -0,0 +1,137 @@
|
||||
\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.
|
||||
@@ -0,0 +1,81 @@
|
||||
\section{How Reliable Are the Labels?}
|
||||
\label{sec:reliability}
|
||||
|
||||
Every category in Section~\ref{sec:categories} is an LLM judgment. Before
|
||||
treating the distribution as a finding, we ask how reproducible those judgments
|
||||
are. We re-rated all 524 drafts a second time with the same pinned prompt using
|
||||
two models---Claude Sonnet (\texttt{claude-sonnet-4-20250514}) and Claude Haiku
|
||||
(\texttt{claude-haiku-4-5-20251001})---and compared the assignments. We report
|
||||
Cohen's $\kappa$ for the nominal primary-category assignment and
|
||||
quadratic-weighted $\kappa$ ($\kappa_w$) for the ordinal $1$--$5$ dimensions,
|
||||
interpreting magnitudes by the Landis--Koch convention (\,$<0.2$ slight,
|
||||
$0.2$--$0.4$ fair, $0.4$--$0.6$ moderate, $0.6$--$0.8$ substantial, $>0.8$
|
||||
almost perfect\,).
|
||||
|
||||
\paragraph{Category assignment is substantially reliable.}
|
||||
For the primary category, Sonnet and Haiku agree at $\kappa = 0.652$ (raw
|
||||
agreement $70.8\%$). Each model independently re-rating also agrees
|
||||
substantially with the original production labels (Sonnet $\kappa = 0.645$,
|
||||
Haiku $\kappa = 0.596$). The residual disagreements are not random: they
|
||||
concentrate on semantically adjacent categories
|
||||
(Table~\ref{tab:confusion})---A2A protocols versus autonomous network
|
||||
operations, A2A versus agent discovery, and identity versus the residual
|
||||
``Other'' bucket. These are boundary cases, not classifier noise, which is why
|
||||
we treat the distribution as informative while acknowledging $\pm$ a few points
|
||||
of category-boundary uncertainty.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{llr}
|
||||
\toprule
|
||||
\textbf{Category A} & \textbf{Category B} & \textbf{Disagreements} \\
|
||||
\midrule
|
||||
A2A protocols & Autonomous netops & 17 \\
|
||||
A2A protocols & Agent discovery/reg & 16 \\
|
||||
A2A protocols & Agent identity/auth & 15 \\
|
||||
Agent identity/auth & Other AI/agent & 14 \\
|
||||
Data formats/interop & Other AI/agent & 10 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Most-confused primary-category pairs between the two re-rating models.
|
||||
Disagreements concentrate on semantic neighbours.}
|
||||
\label{tab:confusion}
|
||||
\end{table}
|
||||
|
||||
\paragraph{Ordinal quality scores are not reliable.}
|
||||
The five $1$--$5$ quality dimensions tell a different and cautionary story
|
||||
(Table~\ref{tab:kappa}). Between Sonnet and Haiku, \emph{overlap} reaches only
|
||||
$\kappa_w = 0.127$ (slight---effectively no better than chance) and
|
||||
\emph{novelty} $\kappa_w = 0.206$. \emph{relevance} appears substantial between
|
||||
the two re-rating models ($0.728$) but collapses to $0.234$ against the
|
||||
production labels, indicating it is not stable across runs either. Only
|
||||
\emph{maturity} is consistently moderate ($0.59$--$0.62$).
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lcc}
|
||||
\toprule
|
||||
\textbf{Dimension} & \textbf{$\kappa_w$ (Sonnet vs.\ Haiku)} & \textbf{$\kappa_w$ (Sonnet vs.\ prod.)} \\
|
||||
\midrule
|
||||
relevance & 0.728 & 0.234 \\
|
||||
maturity & 0.592 & 0.620 \\
|
||||
momentum & 0.457 & 0.247 \\
|
||||
novelty & 0.206 & 0.477 \\
|
||||
overlap & 0.127 & 0.282 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Quadratic-weighted $\kappa$ for the ordinal quality dimensions.
|
||||
Low and inconsistent values---especially for \emph{overlap} and
|
||||
\emph{novelty}---indicate these scores are not reproducible across raters.}
|
||||
\label{tab:kappa}
|
||||
\end{table}
|
||||
|
||||
\paragraph{Consequence.}
|
||||
We therefore report the categorical landscape (Section~\ref{sec:categories}) but
|
||||
deliberately exclude the LLM ordinal quality scores from our findings. We regard
|
||||
this split as a general caution rather than an artifact of our particular
|
||||
pipeline: LLMs can place standards documents into a thematic taxonomy with
|
||||
substantial agreement, but asking them to score subjective qualities such as
|
||||
``novelty'' or ``overlap'' on a numeric scale produces labels that do not
|
||||
survive a change of model. Studies that use LLM-assigned quality scores as
|
||||
quantitative evidence should report inter-rater reliability before doing so.
|
||||
65
workspace/drafts/landscape-survey/sections/06-discussion.tex
Normal file
65
workspace/drafts/landscape-survey/sections/06-discussion.tex
Normal file
@@ -0,0 +1,65 @@
|
||||
\section{Discussion and Limitations}
|
||||
\label{sec:discussion}
|
||||
|
||||
\subsection{Implications}
|
||||
|
||||
Two structural features of the corpus reinforce one another. First, the area is
|
||||
\emph{pre-standardization}: $456$ of the $524$ drafts ($87\%$) are individual
|
||||
submissions not adopted by any working group (Section~\ref{sec:authors}).
|
||||
Second, it is semantically redundant: $170$ drafts ($32.4\%$) have at least one
|
||||
near-duplicate (cosine $>0.9$) elsewhere in the corpus
|
||||
(Section~\ref{sec:redundancy}). Taken together these point to a young, crowded
|
||||
design space in which many authors independently propose overlapping solutions
|
||||
to the same problems. Such a configuration is consistent with the early phase of
|
||||
a standards effort, where consolidation and competition between proposals have
|
||||
yet to resolve into adopted work; it does not, on its own, indicate
|
||||
duplication of effort in any pejorative sense, since some redundancy is the
|
||||
expected by-product of parallel exploration. We note this dynamic descriptively
|
||||
rather than predicting which proposals will prevail.
|
||||
|
||||
Beyond the IETF, our reliability result carries a general caution for
|
||||
LLM-assisted corpus studies. The same pipeline that places documents into a
|
||||
thematic taxonomy with substantial agreement (Cohen's $\kappa \approx 0.65$)
|
||||
produces ordinal quality scores---``novelty,'' ``overlap''---that do not survive
|
||||
a change of model ($\kappa_w$ as low as $0.13$). A study that reported the
|
||||
category distribution and the quality scores side by side, without a reliability
|
||||
check, would present a reproducible measurement and rater noise with equal
|
||||
confidence. The discipline of separating the two, by an explicit inter-rater
|
||||
analysis, is what allowed us to keep the former and discard the latter.
|
||||
|
||||
\subsection{Limitations}
|
||||
|
||||
Several limitations bound the interpretation of our findings.
|
||||
|
||||
\begin{itemize}[leftmargin=1.4em,topsep=2pt,itemsep=1pt]
|
||||
\item \textbf{Abstract-only classification.} Categories and scores are derived
|
||||
from each draft's title, metadata, and abstract, not its full text
|
||||
(Section~\ref{sec:method}). Classifications therefore reflect how a draft
|
||||
presents itself rather than a full reading of its mechanics, and a draft
|
||||
whose abstract understates its technical content may be miscategorised.
|
||||
\item \textbf{Single snapshot.} The analysis rests on one corpus snapshot
|
||||
(\texttt{data/drafts.db} as of 2026-05-23). All counts, trends, and
|
||||
similarities are as of that date and will drift as drafts are added,
|
||||
revised, expired, or adopted.
|
||||
\item \textbf{IETF-only scope.} We deliberately restrict the corpus to IETF
|
||||
Internet-Drafts; documents from ISO, ITU, ETSI, NIST, and W3C are
|
||||
excluded because of heterogeneous metadata and a different document
|
||||
model. The survey therefore says nothing about AI-agent standardization
|
||||
outside the IETF, and the overall scale of the field is correspondingly
|
||||
understated.
|
||||
\item \textbf{LLM-derived categories.} The thematic taxonomy is produced by a
|
||||
large language model. The two-model $\kappa$ check
|
||||
(Section~\ref{sec:reliability}) mitigates but does not eliminate this
|
||||
dependence: substantial agreement still leaves category-boundary
|
||||
uncertainty of a few points, concentrated on semantically adjacent
|
||||
categories.
|
||||
\item \textbf{Provisional recent tail.} Submission counts for the most recent
|
||||
months (April--May 2026) are provisional because of indexing and fetch
|
||||
lag; the dip after the March 2026 peak in Figure~\ref{fig:temporal}
|
||||
should be read as incomplete data rather than as a downturn.
|
||||
\item \textbf{Keyword-based candidate selection.} Candidates were identified by
|
||||
keyword and topic matching, which can both miss relevant drafts whose
|
||||
abstracts avoid the chosen vocabulary and over-include unrelated
|
||||
documents; we observed a $12.2\%$ false-positive rate ($73$ of $597$
|
||||
candidates) before filtering, and an unknown false-negative rate remains.
|
||||
\end{itemize}
|
||||
27
workspace/drafts/landscape-survey/sections/07-conclusion.tex
Normal file
27
workspace/drafts/landscape-survey/sections/07-conclusion.tex
Normal file
@@ -0,0 +1,27 @@
|
||||
\section{Conclusion and Future Work}
|
||||
\label{sec:conclusion}
|
||||
|
||||
We have presented a verified quantitative survey of AI/agent-related IETF
|
||||
Internet-Drafts, based on a curated corpus of $524$ documents spanning January
|
||||
2024 to May 2026. The corpus exhibits a sharp recent surge---from $3.7$ to
|
||||
$38.8$ drafts per month, peaking at $106$ in March 2026---and is dominated by
|
||||
individual submissions ($87\%$ not adopted by any working group) with
|
||||
substantial semantic redundancy ($32.4\%$ of drafts having a near-duplicate),
|
||||
the signature of a young, pre-standardization design space. Crucially, we
|
||||
treated the LLM-assisted labels themselves as objects of measurement: a
|
||||
two-model re-rating shows that categorical assignment is substantially
|
||||
reproducible (Cohen's $\kappa \approx 0.65$), while ordinal quality scores are
|
||||
not ($\kappa_w = 0.13$--$0.21$ for the least stable dimensions), so we report
|
||||
the category landscape and exclude the quality scores.
|
||||
|
||||
Several directions extend this work. \emph{Full-text classification} would
|
||||
replace the abstract-only pipeline and test whether categories shift when the
|
||||
classifier reads the complete document. \emph{Longitudinal re-runs} on later
|
||||
snapshots would turn the single-snapshot picture into a moving record of how the
|
||||
surge evolves and whether redundancy consolidates over time. \emph{Cross-SDO
|
||||
extension} to ISO, ITU, ETSI, NIST, and W3C---contingent on reconciling their
|
||||
heterogeneous metadata---would situate the IETF activity within the broader
|
||||
standards landscape. Finally, \emph{tracking working-group adoption} of
|
||||
individual drafts would reveal which of the many competing proposals clear the
|
||||
bar from individual submission to adopted work, giving an empirical handle on
|
||||
how the pre-standardization field resolves.
|
||||
147
workspace/packages/act/act/schema.json
Normal file
147
workspace/packages/act/act/schema.json
Normal file
@@ -0,0 +1,147 @@
|
||||
{
|
||||
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
||||
"$id": "urn:ietf:params:act:schema:token",
|
||||
"title": "Agent Context Token (ACT)",
|
||||
"description": "JSON Schema for ACT Phase 1 (Authorization Mandate) and Phase 2 (Execution Record) claim sets. A token is a Phase 2 record if and only if it contains the exec_act, exec_ts, pred, and status claims; otherwise it is a Phase 1 mandate.",
|
||||
"type": "object",
|
||||
"oneOf": [
|
||||
{ "$ref": "#/$defs/phase1" },
|
||||
{ "$ref": "#/$defs/phase2" }
|
||||
],
|
||||
"$defs": {
|
||||
"actionName": {
|
||||
"type": "string",
|
||||
"pattern": "^[A-Za-z][A-Za-z0-9_-]*(\\.[A-Za-z][A-Za-z0-9_-]*)*$"
|
||||
},
|
||||
"numericDate": {
|
||||
"type": "integer",
|
||||
"minimum": 0
|
||||
},
|
||||
"uuid": {
|
||||
"type": "string",
|
||||
"format": "uuid"
|
||||
},
|
||||
"audience": {
|
||||
"oneOf": [
|
||||
{ "type": "string" },
|
||||
{ "type": "array", "items": { "type": "string" }, "minItems": 1 }
|
||||
]
|
||||
},
|
||||
"task": {
|
||||
"type": "object",
|
||||
"required": ["purpose"],
|
||||
"properties": {
|
||||
"purpose": { "type": "string", "minLength": 1 },
|
||||
"data_sensitivity": {
|
||||
"type": "string",
|
||||
"enum": ["public", "internal", "confidential", "restricted"]
|
||||
},
|
||||
"created_by": { "type": "string" },
|
||||
"expires_at": { "$ref": "#/$defs/numericDate" }
|
||||
},
|
||||
"additionalProperties": true
|
||||
},
|
||||
"capability": {
|
||||
"type": "object",
|
||||
"required": ["action"],
|
||||
"properties": {
|
||||
"action": { "$ref": "#/$defs/actionName" },
|
||||
"constraints": { "type": "object" }
|
||||
},
|
||||
"additionalProperties": false
|
||||
},
|
||||
"oversight": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"requires_approval_for": {
|
||||
"type": "array",
|
||||
"items": { "$ref": "#/$defs/actionName" }
|
||||
},
|
||||
"approval_ref": { "type": "string" }
|
||||
},
|
||||
"additionalProperties": false
|
||||
},
|
||||
"delegationEntry": {
|
||||
"type": "object",
|
||||
"required": ["delegator", "jti", "sig"],
|
||||
"properties": {
|
||||
"delegator": { "type": "string" },
|
||||
"jti": { "$ref": "#/$defs/uuid" },
|
||||
"sig": { "type": "string" }
|
||||
},
|
||||
"additionalProperties": false
|
||||
},
|
||||
"delegation": {
|
||||
"type": "object",
|
||||
"required": ["depth", "max_depth", "chain"],
|
||||
"properties": {
|
||||
"depth": { "type": "integer", "minimum": 0 },
|
||||
"max_depth": { "type": "integer", "minimum": 0 },
|
||||
"chain": {
|
||||
"type": "array",
|
||||
"items": { "$ref": "#/$defs/delegationEntry" }
|
||||
}
|
||||
},
|
||||
"additionalProperties": false
|
||||
},
|
||||
"error": {
|
||||
"type": "object",
|
||||
"required": ["code"],
|
||||
"properties": {
|
||||
"code": { "type": "string" },
|
||||
"detail": { "type": "string" }
|
||||
},
|
||||
"additionalProperties": false
|
||||
},
|
||||
"commonClaims": {
|
||||
"type": "object",
|
||||
"required": ["iss", "sub", "aud", "iat", "exp", "jti", "task", "cap"],
|
||||
"properties": {
|
||||
"iss": { "type": "string" },
|
||||
"sub": { "type": "string" },
|
||||
"aud": { "$ref": "#/$defs/audience" },
|
||||
"iat": { "$ref": "#/$defs/numericDate" },
|
||||
"exp": { "$ref": "#/$defs/numericDate" },
|
||||
"jti": { "$ref": "#/$defs/uuid" },
|
||||
"wid": { "$ref": "#/$defs/uuid" },
|
||||
"task": { "$ref": "#/$defs/task" },
|
||||
"cap": {
|
||||
"type": "array",
|
||||
"minItems": 1,
|
||||
"items": { "$ref": "#/$defs/capability" }
|
||||
},
|
||||
"oversight": { "$ref": "#/$defs/oversight" },
|
||||
"del": { "$ref": "#/$defs/delegation" }
|
||||
}
|
||||
},
|
||||
"phase1": {
|
||||
"allOf": [{ "$ref": "#/$defs/commonClaims" }],
|
||||
"not": {
|
||||
"anyOf": [
|
||||
{ "required": ["exec_act"] },
|
||||
{ "required": ["exec_ts"] },
|
||||
{ "required": ["status"] }
|
||||
]
|
||||
}
|
||||
},
|
||||
"phase2": {
|
||||
"allOf": [{ "$ref": "#/$defs/commonClaims" }],
|
||||
"required": ["exec_act", "pred", "exec_ts", "status"],
|
||||
"properties": {
|
||||
"exec_act": { "$ref": "#/$defs/actionName" },
|
||||
"pred": {
|
||||
"type": "array",
|
||||
"items": { "$ref": "#/$defs/uuid" }
|
||||
},
|
||||
"inp_hash": { "type": "string" },
|
||||
"out_hash": { "type": "string" },
|
||||
"exec_ts": { "$ref": "#/$defs/numericDate" },
|
||||
"status": {
|
||||
"type": "string",
|
||||
"enum": ["completed", "failed", "partial"]
|
||||
},
|
||||
"err": { "$ref": "#/$defs/error" }
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -78,10 +78,20 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
# Fixed timestamp for deterministic vectors
|
||||
base_time = 1772064000
|
||||
|
||||
# Generate key pairs for test agents
|
||||
iss_priv, iss_pub = generate_ed25519_keypair()
|
||||
sub_priv, sub_pub = generate_ed25519_keypair()
|
||||
agent_c_priv, agent_c_pub = generate_ed25519_keypair()
|
||||
# Fixed Ed25519 seeds so the published Appendix B vectors are
|
||||
# byte-for-byte reproducible. Seeds are recognizable test patterns
|
||||
# (NOT for production use): bytes 0x00..0x1f, 0x20..0x3f, 0x40..0x5f.
|
||||
from cryptography.hazmat.primitives.asymmetric.ed25519 import (
|
||||
Ed25519PrivateKey,
|
||||
)
|
||||
|
||||
def _ed25519_from_seed(seed: bytes) -> tuple[Any, Any]:
|
||||
priv = Ed25519PrivateKey.from_private_bytes(seed)
|
||||
return priv, priv.public_key()
|
||||
|
||||
iss_priv, iss_pub = _ed25519_from_seed(bytes(range(0x00, 0x20)))
|
||||
sub_priv, sub_pub = _ed25519_from_seed(bytes(range(0x20, 0x40)))
|
||||
agent_c_priv, agent_c_pub = _ed25519_from_seed(bytes(range(0x40, 0x60)))
|
||||
|
||||
# Fixed JTIs for cross-referencing
|
||||
jti_b1 = "550e8400-e29b-41d4-a716-446655440001"
|
||||
@@ -91,6 +101,14 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
jti_b3 = "550e8400-e29b-41d4-a716-446655440005"
|
||||
jti_b4 = "550e8400-e29b-41d4-a716-446655440006"
|
||||
jti_b5 = "550e8400-e29b-41d4-a716-446655440007"
|
||||
# Fixed JTIs for the invalid-vector cases (kept stable so the
|
||||
# published compact serializations are reproducible).
|
||||
jti_b6 = "550e8400-e29b-41d4-a716-446655440008"
|
||||
jti_b8 = "550e8400-e29b-41d4-a716-446655440009"
|
||||
jti_b9 = "550e8400-e29b-41d4-a716-44665544000a"
|
||||
jti_b10 = "550e8400-e29b-41d4-a716-44665544000b"
|
||||
jti_b12 = "550e8400-e29b-41d4-a716-44665544000c"
|
||||
jti_b13 = "550e8400-e29b-41d4-a716-44665544000d"
|
||||
wid = "a0b1c2d3-e4f5-6789-abcd-ef0123456789"
|
||||
|
||||
# Key registry
|
||||
@@ -281,7 +299,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
iss="agent-issuer", sub="agent-subject",
|
||||
aud="agent-subject",
|
||||
iat=base_time, exp=base_time + 900,
|
||||
jti=str(uuid.uuid4()),
|
||||
jti=jti_b6,
|
||||
task=TaskClaim(purpose="bad_depth"),
|
||||
cap=[Capability(action="read.data")],
|
||||
delegation=Delegation(depth=3, max_depth=2, chain=[
|
||||
@@ -315,7 +333,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
iss="agent-issuer", sub="agent-subject",
|
||||
aud="agent-subject",
|
||||
iat=base_time, exp=base_time + 900,
|
||||
jti=str(uuid.uuid4()),
|
||||
jti=jti_b8,
|
||||
task=TaskClaim(purpose="bad_exec"),
|
||||
cap=[Capability(action="read.data")],
|
||||
delegation=Delegation(depth=0, max_depth=1, chain=[]),
|
||||
@@ -346,7 +364,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
))
|
||||
|
||||
# --- B.9: DAG cycle (pred references own jti) → ACTDAGError ---
|
||||
cycle_jti = str(uuid.uuid4())
|
||||
cycle_jti = jti_b9
|
||||
cycle_record = ACTRecord(
|
||||
alg="EdDSA", kid="sub-key",
|
||||
iss="agent-issuer", sub="agent-subject",
|
||||
@@ -376,7 +394,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
iss="agent-issuer", sub="agent-subject",
|
||||
aud="agent-subject",
|
||||
iat=base_time, exp=base_time + 900,
|
||||
jti=str(uuid.uuid4()),
|
||||
jti=jti_b10,
|
||||
task=TaskClaim(purpose="missing_parent"),
|
||||
cap=[Capability(action="read.data")],
|
||||
exec_act="read.data",
|
||||
@@ -420,7 +438,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
aud="agent-subject",
|
||||
iat=base_time - 3600,
|
||||
exp=base_time - 2700, # expired 45 minutes ago
|
||||
jti=str(uuid.uuid4()),
|
||||
jti=jti_b12,
|
||||
task=TaskClaim(purpose="expired_test"),
|
||||
cap=[Capability(action="read.data")],
|
||||
)
|
||||
@@ -441,7 +459,7 @@ def generate_vectors() -> tuple[list[TestVector], dict[str, Any]]:
|
||||
iss="agent-issuer", sub="wrong-agent",
|
||||
aud="wrong-agent",
|
||||
iat=base_time, exp=base_time + 900,
|
||||
jti=str(uuid.uuid4()),
|
||||
jti=jti_b13,
|
||||
task=TaskClaim(purpose="wrong_aud_test"),
|
||||
cap=[Capability(action="read.data")],
|
||||
)
|
||||
|
||||
180
workspace/packages/act/scripts/gen_appendix_b.py
Normal file
180
workspace/packages/act/scripts/gen_appendix_b.py
Normal file
@@ -0,0 +1,180 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate the Appendix B (Test Vectors) section body for
|
||||
draft-nennemann-act, in kramdown-rfc markdown.
|
||||
|
||||
The vectors are produced by act.vectors.generate_vectors() using fixed
|
||||
Ed25519 seeds and fixed JTIs, so the output is byte-for-byte
|
||||
reproducible. Run this whenever the reference implementation changes:
|
||||
|
||||
python scripts/gen_appendix_b.py > /tmp/appendix-b.md
|
||||
|
||||
then splice the result into the draft's Appendix B.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
|
||||
from act.token import decode_jws
|
||||
from act.vectors import generate_vectors
|
||||
|
||||
|
||||
def b64url(data: bytes) -> str:
|
||||
import base64
|
||||
return base64.urlsafe_b64encode(data).rstrip(b"=").decode("ascii")
|
||||
|
||||
|
||||
def fold(s: str, width: int = 64) -> str:
|
||||
return "\n".join(s[i:i + width] for i in range(0, len(s), width))
|
||||
|
||||
|
||||
def jblock(obj: object) -> str:
|
||||
return "~~~ json\n" + json.dumps(obj, indent=2, sort_keys=False) + "\n~~~"
|
||||
|
||||
|
||||
# Clean, implementation-neutral section titles (ASCII only).
|
||||
TITLES = {
|
||||
"B.1": "Valid Phase 1 ACT: root mandate, Tier 1 (Ed25519), "
|
||||
"no delegation",
|
||||
"B.2": "Valid Phase 2 ACT: completed execution (transition "
|
||||
"from B.1)",
|
||||
"B.3": "Valid Phase 2 ACT: fan-in with two predecessor tasks",
|
||||
"B.4": "Valid Phase 1 ACT: delegated mandate (depth 1)",
|
||||
"B.5": "Valid Phase 2 ACT: delegated execution record",
|
||||
"B.6": "Invalid: delegation depth exceeds maximum",
|
||||
"B.7": "Invalid: capability escalation in delegated mandate",
|
||||
"B.8": "Invalid: exec_act not present in cap",
|
||||
"B.9": "Invalid: DAG cycle (pred references own jti)",
|
||||
"B.10": "Invalid: missing predecessor task in DAG",
|
||||
"B.11": "Invalid: tampered payload",
|
||||
"B.12": "Invalid: expired token",
|
||||
"B.13": "Invalid: audience mismatch",
|
||||
"B.14": "Invalid: Phase 2 record signed by wrong key",
|
||||
"B.15": 'Invalid: unsigned token (alg "none")',
|
||||
}
|
||||
|
||||
# Expected-result text per vector id, in specification terms (no
|
||||
# implementation-specific exception names).
|
||||
EXPECTED = {
|
||||
"B.1": "Accept. The signature verifies under kid \"iss-key\" and "
|
||||
"all required Phase 1 claims are present.",
|
||||
"B.2": "Accept. Phase 2 record; the signature verifies under kid "
|
||||
"\"sub-key\" and exec_act \"read.data\" is present in cap.",
|
||||
"B.3": "Accept. Phase 2 fan-in; pred lists the two predecessor jti "
|
||||
"values from the parallel branches.",
|
||||
"B.4": "Accept. Delegated mandate at depth 1; the delegated cap "
|
||||
"(max_records=5) is a subset of the parent cap "
|
||||
"(max_records=10).",
|
||||
"B.5": "Accept. Phase 2 record for the delegated mandate; the "
|
||||
"signature verifies under kid \"agent-c-key\".",
|
||||
"B.6": "Reject. del.depth (3) exceeds del.max_depth (2).",
|
||||
"B.7": "Reject. The delegated cap (max_records=100) is not a subset "
|
||||
"of the parent cap (max_records=10); privilege reduction is "
|
||||
"violated. This vector is a delegation-creation check; no "
|
||||
"token is issued.",
|
||||
"B.8": "Reject. exec_act \"delete.everything\" does not match any "
|
||||
"cap.action.",
|
||||
"B.9": "Reject. pred references the record's own jti, forming a "
|
||||
"cycle.",
|
||||
"B.10": "Reject. pred references a jti that is not a previously "
|
||||
"verified task.",
|
||||
"B.11": "Reject. The signature does not verify; one byte of the "
|
||||
"claims segment was altered after signing.",
|
||||
"B.12": "Reject. exp is in the past (expired 45 minutes before the "
|
||||
"verification time).",
|
||||
"B.13": "Reject. aud does not include the verifier identity.",
|
||||
"B.14": "Reject. The Phase 2 record claims kid \"sub-key\" but was "
|
||||
"signed with the issuer's private key; the signature does "
|
||||
"not verify.",
|
||||
"B.15": "Reject. The JOSE header alg is \"none\"; unsigned tokens "
|
||||
"MUST NOT be accepted.",
|
||||
}
|
||||
|
||||
|
||||
def main() -> int:
|
||||
vectors, ctx = generate_vectors()
|
||||
|
||||
out: list[str] = []
|
||||
out.append("# Appendix B: Test Vectors")
|
||||
out.append('{:numbered="false"}')
|
||||
out.append("")
|
||||
out.append(
|
||||
"All vectors in this appendix are produced by the reference "
|
||||
"implementation using fixed Ed25519 seeds and fixed identifiers, "
|
||||
"and are therefore reproducible. The base timestamp (iat) is "
|
||||
"1772064000 (2026-02-26T00:00:00Z). Long Base64url values are "
|
||||
"wrapped for display only; line breaks are not part of the "
|
||||
"value."
|
||||
)
|
||||
out.append("")
|
||||
|
||||
# --- Key material ---
|
||||
out.append("## B.0. Key Material")
|
||||
out.append('{:numbered="false"}')
|
||||
out.append("")
|
||||
out.append(
|
||||
"Three Ed25519 key pairs are used, identified by kid. Keys are "
|
||||
"given in JWK {{RFC7517}} form using the Ed25519 OKP encoding "
|
||||
"{{RFC8037}}; \"d\" is the private seed and \"x\" is the public "
|
||||
"key, both Base64url-encoded. These seeds are recognizable test "
|
||||
"patterns and MUST NOT be used in production."
|
||||
)
|
||||
out.append("")
|
||||
keymap = [
|
||||
("iss-key", ctx["iss_priv"], ctx["iss_pub"]),
|
||||
("sub-key", ctx["sub_priv"], ctx["sub_pub"]),
|
||||
("agent-c-key", ctx["agent_c_priv"], ctx["agent_c_pub"]),
|
||||
]
|
||||
for kid, priv, pub in keymap:
|
||||
jwk = {
|
||||
"kty": "OKP",
|
||||
"crv": "Ed25519",
|
||||
"kid": kid,
|
||||
"d": b64url(priv.private_bytes_raw()),
|
||||
"x": b64url(pub.public_bytes_raw()),
|
||||
}
|
||||
out.append(jblock(jwk))
|
||||
out.append("")
|
||||
|
||||
# --- Per-vector rendering ---
|
||||
for v in vectors:
|
||||
out.append(f"## {v.id}. {TITLES[v.id]}")
|
||||
out.append('{:numbered="false"}')
|
||||
out.append("")
|
||||
|
||||
decoded = None
|
||||
if v.compact:
|
||||
try:
|
||||
header, claims, _sig, _si = decode_jws(v.compact)
|
||||
decoded = (header, claims)
|
||||
except Exception:
|
||||
decoded = None
|
||||
|
||||
if decoded is not None:
|
||||
header, claims = decoded
|
||||
out.append("JOSE Header:")
|
||||
out.append("")
|
||||
out.append(jblock(header))
|
||||
out.append("")
|
||||
out.append("JWT Claims:")
|
||||
out.append("")
|
||||
out.append(jblock(claims))
|
||||
out.append("")
|
||||
|
||||
if v.compact:
|
||||
out.append("JWS Compact Serialization:")
|
||||
out.append("")
|
||||
out.append("~~~")
|
||||
out.append(fold(v.compact))
|
||||
out.append("~~~")
|
||||
out.append("")
|
||||
|
||||
out.append(f"Expected result: {EXPECTED[v.id]}")
|
||||
out.append("")
|
||||
|
||||
sys.stdout.write("\n".join(out) + "\n")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -1,9 +1,15 @@
|
||||
"""Tests for act.vectors module — Appendix B test vectors."""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from act.token import decode_jws
|
||||
from act.vectors import generate_vectors, validate_vectors
|
||||
|
||||
SCHEMA_PATH = Path(__file__).resolve().parent.parent / "act" / "schema.json"
|
||||
|
||||
|
||||
class TestVectorGeneration:
|
||||
def test_generates_15_vectors(self):
|
||||
@@ -33,3 +39,32 @@ class TestVectorGeneration:
|
||||
class TestVectorValidation:
|
||||
def test_all_vectors_pass(self):
|
||||
assert validate_vectors() is True
|
||||
|
||||
|
||||
class TestVectorReproducibility:
|
||||
def test_compacts_are_deterministic(self):
|
||||
# Published Appendix B vectors must be byte-for-byte stable.
|
||||
v1, _ = generate_vectors()
|
||||
v2, _ = generate_vectors()
|
||||
a = {v.id: v.compact for v in v1}
|
||||
b = {v.id: v.compact for v in v2}
|
||||
assert a == b
|
||||
|
||||
|
||||
class TestSchemaConformance:
|
||||
def test_schema_is_well_formed(self):
|
||||
jsonschema = pytest.importorskip("jsonschema")
|
||||
schema = json.loads(SCHEMA_PATH.read_text())
|
||||
jsonschema.Draft202012Validator.check_schema(schema)
|
||||
|
||||
def test_valid_vectors_conform_to_schema(self):
|
||||
jsonschema = pytest.importorskip("jsonschema")
|
||||
schema = json.loads(SCHEMA_PATH.read_text())
|
||||
validator = jsonschema.Draft202012Validator(schema)
|
||||
vectors, _ = generate_vectors()
|
||||
valid_with_token = {"B.1", "B.2", "B.3", "B.4", "B.5"}
|
||||
for v in vectors:
|
||||
if v.id in valid_with_token:
|
||||
_h, claims, _s, _si = decode_jws(v.compact)
|
||||
errors = list(validator.iter_errors(claims))
|
||||
assert not errors, f"{v.id}: {errors}"
|
||||
|
||||
Reference in New Issue
Block a user