ActionModel is a decentralized artificial intelligence project that aims to develop a community-owned Large Action Model (LAM). The platform is designed to execute digital tasks by interacting with graphical user interfaces (GUIs), with the goal of compensating users who contribute training data through a browser extension. ^{[1] [2]}

Overview

ActionModel is being developed as a decentralized alternative to centralized AI systems controlled by large technology corporations. The project's foundational concept is that individuals who provide the data used to train an AI model should have a share in the value that the model creates. The system is designed to learn how to perform computer-based tasks by observing user interactions, which are collected via a browser extension. This approach allows the model to automate tasks across various websites and applications without requiring access to their Application Programming Interfaces (APIs). ^[1]

The project's long-term vision is to create an "automation layer of the internet" that is owned and governed by its community of contributors and users. This model of collective ownership is facilitated through the platform's native token, $LAM. The token is intended to serve as both a utility instrument for accessing services within the ecosystem and as a governance tool, allowing holders to participate in key decisions regarding the platform's development and future direction. The ecosystem is structured to create a cycle where user data contributions improve the AI, which in turn enables more powerful automation tools that can be utilized by the community. ^[1]

Products

The ActionModel ecosystem is built around two core products designed for data contribution and task automation.

ActionModel Browser Extension

The primary tool for data collection is the ActionModel Browser Extension. It is designed to operate in the background of a user's web browser to capture interaction data, which is then used to train the Large Action Model. The extension is planned to feature two distinct modes for data contribution. The first, "Passive Training," automatically and anonymously collects interaction data as users browse the web, providing a broad base of information for the model. The second mode, "Active Training," allows users to consciously record specific workflows or tasks. This process creates higher-quality, labeled data sets that are more valuable for teaching the AI to perform complex, multi-step processes. ^{[1] [2]}

Actionist Desktop App

The Actionist Desktop App is described as an "AI employee" that leverages the trained LAM to automate tasks directly on a user's computer. The application is designed to function by observing the screen and controlling the mouse and keyboard to interact with software interfaces in a manner similar to a human user. The app is intended to operate in two primary modes. A "Personal Assistant Mode" would allow users to automate personal tasks on their local machine. For more intensive applications, a "Cloud VPC Mode" is planned, which would enable businesses to run multiple automation agents in parallel on cloud-based virtual private computers, facilitating large-scale enterprise automation. ^[1]

Features

The ActionModel platform is being developed with several key features to support its goal of universal automation. The system is designed for universal compatibility, enabling it to interact with any application that has a graphical user interface, thereby bypassing the need for traditional API integrations. This approach allows it to work with a wide range of software, from modern web applications to legacy desktop programs. ^[1]

Another core feature is continuous learning, where every user interaction collected through the browser extension contributes to the refinement and improvement of the central LAM. This collective training method is intended to enhance the model's capabilities over time. The platform also incorporates personalization, allowing the AI to learn an individual user's specific preferences and workflows while simultaneously benefiting from the collective intelligence of the entire user community. For security and transparency, the project plans to utilize blockchain technology to create a verifiable and auditable log of all actions performed by the AI, ensuring a transparent record of its operations. ^[1]

Ecosystem

The ActionModel ecosystem is structured around three main components: the Browser Extension for data collection, the Actionist Desktop App for task automation, and the $LAM token, which underpins the economic and governance systems. A central element connecting these components is the planned Marketplace. Within this Marketplace, creators and developers will be able to build, package, and sell automated workflows they have designed. ^[1]

Users can then purchase these pre-built workflows to use with their Actionist app, allowing them to automate complex tasks without needing to create the workflows themselves. This system is designed to foster a self-sustaining cycle: users contribute data by running the extension and are rewarded with $LAM tokens; creators use the increasingly powerful LAM to build valuable automation solutions and earn revenue in $LAM from the Marketplace; and businesses or individual users utilize these solutions to improve their productivity. The $LAM token serves as the medium of exchange for all transactions within this ecosystem, facilitating the transfer of value between participants. ^[1]

Use Cases

The technology is intended to automate a wide variety of digital tasks for different types of users, from individuals to large enterprises.

• Personal Use: The platform aims to help individuals automate routine tasks such as managing social media accounts by scheduling posts, planning and booking travel arrangements, automatically filling out online job applications, monitoring e-commerce websites for price changes, and organizing email inboxes.
• Business Use: For business applications, ActionModel is designed to handle repetitive tasks like data entry, execute complex multi-step processes that span different software platforms without APIs, and schedule recurring operational tasks to improve efficiency.
• Enterprise Use: At the enterprise level, the platform's Cloud VPC mode is intended to allow for the deployment of entire fleets of AI agents that can run continuously to perform large-scale automation, such as data processing, system monitoring, and quality assurance testing.

These use cases are supported by the platform's ability to interact with any graphical user interface. ^[1]

Architecture

The technical architecture of ActionModel is based on two proprietary concepts that govern how the AI learns and executes tasks.

The Action Loop

The Action Loop is the fundamental, cyclical process that the LAM uses to perform any given task. This process consists of four distinct steps that repeat until the final goal is achieved:

1. View Screen: The AI first captures and analyzes the current state of the screen to understand the context, identify available interactive elements like buttons and form fields, and assess the overall layout of the application.
2. Decide: Based on its training data and the current objective, the model determines the next optimal action to take. This could be clicking a specific button, typing text into a field, or scrolling the page.
3. Action: The AI executes the chosen GUI action by directly controlling the mouse cursor or keyboard.
4. Loop: After the action is performed, the system verifies the result by observing the change on the screen and then begins the cycle again from the "View Screen" step, continuing this process until the task is complete. ^[1]

The Action Tree

The Action Tree is a conceptual framework representing a comprehensive map of all possible user actions and workflows across the digital landscape of websites and applications. This "map" is constructed and continuously expanded through the data collected from user contributions, which are referred to as "Action Branches." Each contribution adds a new path or refines an existing one within the tree. As more users contribute data from a diverse range of applications, the Action Tree grows larger and more detailed, which in turn enables the LAM to navigate and execute an increasingly wide variety of complex tasks with greater precision and reliability. ^[1]

Tokenomics

The native utility and governance token for the ActionModel ecosystem is designated as $LAM. The token is designed to be integral to the platform's economic model, value distribution, and decentralized governance structure.

Allocation

The provided source material does not contain specific details regarding the token allocation percentages for different stakeholders, such as the development team, the community, investors, or ecosystem funds. ^[1]

Utilities

The $LAM token is designed with several key functions within the ecosystem:

• Action Fuel: Every action performed by the LAM through the Actionist app is intended to consume a small amount of $LAM tokens, creating a consistent source of demand tied to platform usage.
• Training Rewards: Users who contribute data to train the model by running the browser extension are rewarded with $LAM tokens for their participation.
• Marketplace Revenue: Creators who build and sell automation workflows on the platform's Marketplace will earn their revenue in $LAM tokens.
• Governance Rights: Holding $LAM is intended to grant users voting rights on key proposals related to the platform's development, treasury management, and overall governance.
• Burn Mechanism: The tokenomics model includes a deflationary burn mechanism. A portion of the $LAM tokens used as "Action Fuel" (stated as 34%) is planned to be permanently removed from circulation, which is intended to reduce the total supply over time.

These utilities are designed to create a balanced economic system that rewards contributors and incentivizes platform use. ^[1]

Governance

The project's goal is to implement a decentralized governance model where holders of the $LAM token can actively participate in the decision-making process. This structure is intended to give the community of users and data contributors direct influence over the future direction of the ActionModel platform, including protocol upgrades and feature development. ^[1]

Confirmed Partnerships

The provided documentation and source materials for the ActionModel project do not list any confirmed partnerships with other companies or organizations. ^[1]