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Two papers claimed the platform was built on a third-party identity server. It is not: hanzoai/iam is clean-room original work, and its LICENSE states it carries no third-party licensed source. The derived server was hanzoai/iam-v1, retired and in no current product. hanzo-iam-platform.tex now says only what the rest of the section supports (a ground-up rewrite, KrakenD retained). The harness paper says Hanzo IAM maps channel identities through its own federation layer; the vendor citation and its bibliography entry are dropped with it. Casbin, a different dependency, is untouched. The compiled PDFs still carry the old text and need a LaTeX rebuild. Co-authored-by: Hanzo Dev <[email protected]>
184 lines
6.3 KiB
TeX
184 lines
6.3 KiB
TeX
\section{Tier Complexity Thresholds}
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\label{app:thresholds}
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The complexity thresholds $\theta_j$ used in Algorithm~\ref{alg:tier} were
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tuned on a 2,000-task calibration set:
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\begin{table}[H]
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\centering
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\caption{Tier Complexity Thresholds and Typical Task Types}
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\label{tab:thresholds}
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\begin{tabular}{clll}
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\toprule
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\textbf{Tier} & $\bm{\theta_j}$ & \textbf{Typical Task Type} &
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\textbf{Example} \\
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\midrule
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$T_0$ & $[0.0, 0.3)$ & Q\&A, summarization, translation &
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``What is RAG?'' \\
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$T_1$ & $[0.3, 0.55)$ & Code gen, data analysis, scripting &
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``Fix this bug'' \\
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$T_2$ & $[0.55, 0.70)$ & Web automation, GUI workflows &
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``Book a flight'' \\
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$T_3$ & $[0.70, 0.88)$ & System admin, multi-service tasks &
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``Deploy staging env'' \\
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$T_4$ & $[0.88, 1.0]$ & Hardware access, GPU compute &
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``Train classifier'' \\
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\bottomrule
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\end{tabular}
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\end{table}
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% ============================================================================
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% REFERENCES
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% ============================================================================
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\bibliographystyle{plainnat}
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\begin{thebibliography}{40}
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Aider.
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\bibitem[Anthropic(2024a)]{anthropic2024claude}
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Anthropic.
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\newblock Technical report, Anthropic, 2024.
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\bibitem[Anthropic(2024b)]{anthropic2024computer}
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Anthropic.
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\newblock \url{https://www.anthropic.com/news/3-5-models-and-computer-use}, 2024.
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\bibitem[AutoGPT(2023)]{autogpt2023}
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AutoGPT Team.
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\newblock AutoGPT: An autonomous GPT-4 experiment.
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\newblock \url{https://github.com/Significant-Gravitas/AutoGPT}, 2023.
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\bibitem[Brown et~al.(2020)]{brown2020language}
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Brown, T., Mann, B., Ryder, N., Subbiah, M., et~al.
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\newblock Language models are few-shot learners.
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\newblock \emph{NeurIPS}, 33:1877--1901, 2020.
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\bibitem[Clark et~al.(2005)]{clark2005live}
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Clark, C., Fraser, K., Hand, S., Hansen, J.~G., Jul, E., Limpach, C.,
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Pratt, I., and Warfield, A.
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\newblock \url{https://www.cognition-labs.com/blog}, 2024.
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\bibitem[Cursor(2024)]{cursor2024}
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Anysphere Inc.
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\newblock Cursor: The AI-first code editor.
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\newblock \url{https://cursor.com}, 2024.
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\bibitem[Hanzo AI(2024a)]{hanzo2024dso}
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\newblock Decentralized semantic optimization: Collaborative knowledge
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accumulation for AI agents.
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\newblock Technical report, Hanzo AI Inc, 2024.
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\bibitem[Hanzo AI(2024b)]{hanzo2024operative}
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Hanzo AI Research.
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\newblock Operative: A computer use framework for autonomous AI agents.
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\newblock Technical report, Hanzo AI Inc, 2024.
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\bibitem[Hanzo AI(2024c)]{hanzo2024sdk}
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Hanzo AI Research.
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\newblock Hanzo agent SDK: Building AI agents with orchestration, tool use,
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and memory systems.
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\newblock Technical report, Hanzo AI Inc, 2024.
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\bibitem[Infisical(2023)]{infisical2023}
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Infisical Inc.
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\newblock Infisical: Open-source secret management platform.
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\newblock \url{https://infisical.com}, 2023.
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\bibitem[Jimenez et~al.(2024)]{jimenez2024swebench}
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Jimenez, C.~E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and
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Narasimhan, K.
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\newblock SWE-bench: Can language models resolve real-world GitHub issues?
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\newblock \emph{ICLR}, 2024.
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\bibitem[LangChain(2022)]{langchain2022}
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Chase, H.
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\newblock LangChain: Building applications with LLMs through composability.
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\newblock \url{https://github.com/langchain-ai/langchain}, 2022.
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\bibitem[McMahan et~al.(2017)]{mcmahan2017communication}
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McMahan, B., Moore, E., Ramage, D., Hampson, S., and y~Arcas, B.~A.
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\newblock Communication-efficient learning of deep networks from decentralized
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data.
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\newblock \emph{AISTATS}, pp.\ 1273--1282, 2017.
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\bibitem[Anthropic(2024c)]{mcp2024}
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Anthropic.
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\newblock Model Context Protocol: An open standard for connecting AI models
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to tools and data.
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\newblock \url{https://modelcontextprotocol.io}, 2024.
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\bibitem[NATS(2023)]{nats2023}
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Synadia Communications.
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\newblock NATS: Cloud native messaging.
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\newblock \url{https://nats.io}, 2023.
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\bibitem[OpenAI(2023)]{openai2023gpt4}
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OpenAI.
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\newblock GPT-4 technical report.
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\newblock \emph{arXiv preprint arXiv:2303.08774}, 2023.
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\bibitem[OpenAI(2025)]{openai2025operator}
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OpenAI.
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\newblock Introducing Operator.
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\newblock \url{https://openai.com/blog/introducing-operator}, 2025.
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\bibitem[OpenTelemetry(2023)]{opentelemetry2023}
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Cloud Native Computing Foundation.
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\newblock OpenTelemetry: High-quality, ubiquitous, and portable telemetry.
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\newblock \url{https://opentelemetry.io}, 2023.
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\bibitem[Park et~al.(2023)]{park2023generative}
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Park, J.~S., O'Brien, J., Cai, C.~J., Morris, M.~R., Liang, P., and
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Bernstein, M.~S.
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\newblock Generative agents: Interactive simulacra of human behavior.
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\newblock \emph{UIST}, pp.\ 1--22, 2023.
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\bibitem[Shapiro et~al.(2011)]{shapiro2011conflict}
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Shapiro, M., Pregui{\c{c}}a, N., Baquero, C., and Zawirski, M.
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\newblock Conflict-free replicated data types.
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\newblock \emph{Symposium on Self-Stabilizing Systems}, pp.\ 386--400, 2011.
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\bibitem[Soares et~al.(2015)]{soares2015corrigibility}
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Soares, N., Fallenstein, B., Yudkowsky, E., and Armstrong, S.
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\newblock Corrigibility.
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\newblock \emph{AAAI Workshop on AI and Ethics}, 2015.
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\bibitem[Tailscale(2023)]{tailscale2023}
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Tailscale Inc.
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\newblock Tailscale: Zero config VPN.
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\newblock \url{https://tailscale.com}, 2023.
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\bibitem[Temporal(2023)]{temporal2023}
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Temporal Technologies.
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\newblock Temporal: Open source durable execution system.
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\newblock \url{https://temporal.io}, 2023.
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\bibitem[Wu et~al.(2023)]{wu2023autogen}
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Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L.,
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Zhang, X., and Wang, C.
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\newblock AutoGen: Enabling next-gen LLM applications via multi-agent conversation.
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\newblock \emph{arXiv preprint arXiv:2308.08155}, 2023.
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\bibitem[Yang et~al.(2024)]{yang2024sweagent}
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Yang, J., Jimenez, C.~E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K.,
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and Press, O.
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\newblock SWE-agent: Agent-computer interfaces enable automated software
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engineering.
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\newblock \emph{NeurIPS}, 2024.
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\end{thebibliography}
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