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papers/hanzo-operative/hanzo-operative.tex
zeekay 0f01f8b555 papers: best-effort accuracy fixes from swarm (dates, arithmetic, internal consistency, LP numbering)
Resolves high-severity review findings: anachronistic dates aligned to citation
floors; arithmetic corrected to match formulas; internal contradictions
collapsed to the body-supported value; LP self-references aligned to filenames;
liquidity-surface repo paths scrubbed; AI-slop trimmed. PDFs rebuilt separately.
2026-06-27 02:16:30 +00:00

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TeX

\documentclass[11pt,twocolumn]{article}
\usepackage{../shared/hanzocover}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,amsthm}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{algorithm}
\usepackage{algorithmic}
\usepackage{hyperref}
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\newtheorem{definition}{Definition}
\newtheorem{theorem}{Theorem}
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\title{Operative: A Computer Use Framework\\for Autonomous AI Agents}
\author{Antje Worring, Zach Kelling \\
Hanzo AI Research\\
\texttt{[email protected]}\\
\textit{Hanzo AI \quad San Francisco, CA}}
\date{2026}
\begin{document}
\hanzocoverpage
\begin{abstract}
Autonomous AI agents capable of operating graphical user interfaces represent a significant frontier in applied artificial intelligence. We present Operative, an open-source framework for computer use that enables large language models to perceive, reason about, and interact with desktop and browser environments. Operative introduces three key innovations: (1) a hierarchical screen understanding pipeline combining OCR, visual grounding, and accessibility tree parsing to construct structured representations of arbitrary GUIs, (2) a typed action space with 47 primitive operations spanning mouse, keyboard, browser, and system interactions with formal pre- and post-condition specifications, and (3) a multi-step planning architecture that decomposes high-level user goals into executable action sequences with rollback capabilities. Safety is enforced through a configurable boundary system that restricts filesystem access, network egress, and system modifications. In evaluation across three established benchmarks---OSWorld, WebArena, and VisualWebBench---Operative achieves task completion rates of 38.1\%, 54.3\%, and 52.8\% respectively with a Claude backend, establishing state-of-the-art results across all three. The framework processes 4.2 screen observations per second on consumer hardware and supports both cloud-hosted models (via screenshot) and local vision-language models (via direct framebuffer access).
\end{abstract}
\input{sections/01-introduction}
\input{sections/02-background-and-related-work}
\input{sections/03-screen-understanding-pipeline}
\input{sections/04-action-space}
\input{sections/05-safety-boundaries}
\input{sections/06-multi-step-planning}
\input{sections/07-implementation}
\input{sections/08-evaluation}
\input{sections/09-discussion}
\input{sections/99-conclusion}
\end{document}