Blogs / Large Action Models(LAM): When AI Moves from Talking to Acting

Large Action Models(LAM): When AI Moves from Talking to Acting

Large Action Models(LAM): وقتی هوش مصنوعی از حرف زدن به عمل کردن می‌رسد

Introduction

Imagine telling your AI assistant "Book a flight to Istanbul for next week" and it not only understands your request, but directly navigates to various websites, compares prices, selects the best option, and completes the reservation. This is exactly what Large Action Models or LAMs are designed to make reality.
While large language models like ChatGPT and Claude excel at generating text and answering questions, LAMs are the next evolutionary step. They don't just understand what you want—they do it for you. This is a transformation that could fundamentally change how we interact with technology.

What is LAM and How Does It Work?

A Large Action Model is an advanced AI system capable of understanding human intent and converting it into practical actions in digital environments. Unlike language models that primarily focus on content generation, LAMs are designed for executing tasks.

The Fundamental Difference Between LLM and LAM

The difference between LLM and LAM can be explained as follows:
  • LLM (Large Language Model): "To book a flight ticket, you can visit sites like Skyscanner, Booking, or Google Flights and follow these steps..."
  • LAM (Large Action Model): "Your ticket is booked. Turkish Airlines flight at 10 AM, price $450. Confirmation sent to your email."
This small but fundamental difference is crucial. LLM gives you information, LAM takes action.

LAM Architecture and Structure

LAMs are built on transformer models and deep neural networks, but with one key difference: they are specifically trained to generate actions instead of words.
The general structure of a LAM includes several layers:
  1. Understanding Layer: Analyzing user intent through natural language processing
  2. Planning Layer: Breaking down tasks into executable actions
  3. Execution Layer: Interacting with user interfaces, APIs, or digital environments
  4. Feedback Layer: Learning from results and improving performance
This architecture allows LAMs to not only execute simple commands but also manage complex multi-step tasks.

Technologies Behind LAM: From Data to Action

Training LAM: Learning from Human Interactions

Training a LAM is a complex process based on reinforcement learning and observation of human behavior. Researchers use the following techniques:
1. Imitation Learning: The model learns by watching millions of hours of human interaction with graphical interfaces. For example, it learns that for online shopping, you need to click "Add to Cart," fill out the address form, and select payment method.
2. Feedback Learning: LAM learns from the outcomes of its actions. If an action is successful, that pattern is reinforced. This process is similar to how humans learn.
3. Multi-task Training: The model is trained on a wide range of tasks—from restaurant reservations to email management—to be able to interact generally with various digital environments.

Advanced Techniques in LAM

Modern LAMs leverage several advanced techniques:
  • Vision-Language Models: Combining machine vision and language understanding to interact with graphical interfaces
  • Reinforcement Learning from Human Feedback (RLHF): Improving performance based on real user feedback
  • Multi-modal Understanding: Ability to process text, images, and audio simultaneously
  • Chain-of-Thought Reasoning: Using chain of thought to solve complex problems

Practical Applications of LAM: From Theory to Reality

Rabbit R1: LAM Pioneer in the Real World

One of the most prominent implementations of LAM is the Rabbit R1 device. This pocket-sized smart assistant, introduced in early 2024, demonstrated how LAMs can work in the real world.
Rabbit R1 can:
  • Order food: By watching how you order from different apps, it learns and later orders on its own
  • Book services: From Uber reservations to movie ticket purchases
  • Smart home management: Control temperature, lighting, and connected devices
  • Perform web tasks: Domain registration, online shopping, web-based games
In October 2024, Rabbit released the LAM Playground capability—the first public web agent that can navigate different websites, review information, and perform actions. This was an important milestone showing LAMs can work beyond controlled environments.

LAM in Various Industries

1. Customer Service and Support
LAMs can play the role of intelligent customer agents that not only answer questions but solve problems:
  • Change user account settings
  • Process product returns and replacements
  • Troubleshoot technical issues with step-by-step practical guidance
  • Manage subscriptions and payments
2. Digital Marketing and E-commerce
In digital marketing and advertising, LAMs can:
  • Launch and optimize advertising campaigns across different platforms
  • Generate and publish marketing content
  • Analyze performance and implement necessary changes
  • Facilitate the purchasing process for customers
3. Human Resources and Recruitment
LAMs can transform the recruitment process:
  • Automatic search and filtering of resumes across different sites
  • Interview scheduling
  • Sending follow-up emails
  • Initial review of documents and credentials
4. Healthcare Management
  • Schedule medical appointments
  • Medication reminders and treatment follow-up
  • Manage electronic health records
  • Coordinate between different medical centers
5. Finance and Investment
LAMs in financial analysis and trading can:
  • Execute investment strategies
  • Portfolio management
  • Perform financial transactions
  • Market analysis and trade execution

LAM Challenges and Limitations

Technical and Architectural Issues

1. Complexity of Digital Environments
One of LAMs' biggest challenges is the enormous variety of user interfaces. Every website and application has a unique design. LAM must be able to cope with this diversity—from simple websites to complex applications.
2. High Computational Cost
Training and running LAMs require significant computational resources. Unlike LLMs that only need to generate text, LAMs must:
  • Visually process graphical interfaces
  • Make complex decisions
  • Interact with the environment
  • Learn from results
This process can be 10 to 100 times more expensive than a typical LLM.
3. Planning and Execution Problems
LAMs sometimes struggle with planning complex multi-step tasks. If one stage fails, the entire process might collapse. This is similar to problems seen in intelligent agents.

Security and Privacy Challenges

1. Access to Sensitive Information
LAMs need extensive access to function—bank accounts, email, personal information. This raises serious concerns about cybersecurity.
2. Risk of Abuse
A hacked LAM could be catastrophic—unauthorized purchases, fund transfers, access to confidential information. For this reason, developers must implement multi-layered security systems.
3. Tracking and Monitoring
LAMs can completely track users' online activities. This issue exacerbates the illusion of privacy.

Ethical and Social Challenges

1. Accountability
What if LAM makes a mistake? If it transfers money to the wrong account or publishes incorrect information, who is responsible? This is one of the biggest questions in AI ethics.
2. Impact on Employment
LAMs can automate many administrative, customer support, and digital tasks. This means the impact of artificial intelligence on jobs and the possibility of workforce displacement.
3. Dependency and Declining Human Skills
The more we rely on LAMs, the more we might lose our skills in performing daily tasks—similar to our dependence on GPS causing us to lose mental navigation ability.

The Future of LAM: Where Are We Going?

Evolution of LAM Architectures

Researchers are working on newer architectures that are more efficient and powerful:
1. Mixture of Experts Models
Using MoE architecture can help LAMs perform different tasks better. In this approach, different expert models are trained for specific tasks, and a coordinating system selects the most appropriate expert for each task.
2. Federated Learning
Using federated learning, LAMs can learn from collective experiences without sharing users' sensitive data. This helps preserve privacy.
3. Smaller and More Efficient Models
Developing small language models (SLMs) that can run on local devices promises a future where LAMs work without needing to send data to cloud servers.

Integration with Emerging Technologies

1. LAM and Internet of Things (IoT)
Combining LAM with IoT can take smart homes to a new level. Imagine your LAM automatically:
  • Adjusting home temperature by analyzing your lifestyle patterns
  • Creating and ordering shopping lists
  • Determining the best time to use electrical appliances
2. LAM and Metaverse
In the metaverse world, LAMs can play the role of intelligent avatars that act for you in virtual worlds, conduct transactions, and interact with other users.
3. LAM and Blockchain
Integrating LAM with blockchain can increase transparency and security. Every LAM action can be recorded in a decentralized ledger, providing auditability and accountability.

Multi-Agent Systems

The future likely belongs to multi-agent systems—where multiple LAMs cooperate:
  • One LAM dedicated to financial tasks
  • One LAM for health management
  • One LAM for social networks
  • One coordinating LAM that connects them
This autonomous AI agents approach can provide more flexibility and specialization.

LAM and the Path Toward AGI

One interesting question is whether LAMs bring us closer to Artificial General Intelligence (AGI)? Some researchers believe that the ability to act in the real world is a critical step toward true intelligence.
AGI requires interacting with the environment, learning from experience, and adapting to new conditions—exactly what LAMs are learning to do. Although there's still a long way to AGI, LAMs are an important bridge.

Practical Guide: How to Use LAM?

For Regular Users

If you want to benefit from LAM capabilities today:
1. Use Available Platforms
  • Try Rabbit R1
  • Use LAM Playground on the web
  • Explore LAM-based automation tools
2. Start with Simple Tasks
  • Restaurant reservations
  • Reminders and calendar management
  • Product search and purchase
3. Gradually Make It More Complex
  • Automate daily workflow
  • Manage multiple platforms simultaneously
  • Complex multi-step tasks

For Developers

If you want to build a LAM, your path includes:
1. Familiarize Yourself with Tools
2. Data Collection
  • Record human interactions with UI
  • Label actions
  • Build diverse datasets
3. Training and Evaluation
4. Focus on Security
  • Implement strong authentication
  • Limit access permissions
  • Encrypt data
  • Continuous auditing

Key Points for the Future

1. Transparency and Explainability
LAMs must be explainable. Users should know why a LAM made a specific decision or took a specific action.
2. Human Control
Even with the most advanced LAMs, there should be a "human approval" option for sensitive tasks. We must never lose complete control.
3. Standardization
The industry needs common standards for LAMs—security protocols, data formats, API interfaces.
4. User Education
People need to be educated about LAMs' capabilities and limitations to have realistic expectations and use this technology properly.

Comparing LAM with Other AI Models

LAM vs LLM

While new language models like GPT-5, Claude 4, and Gemini are exceptional at content generation, LAMs focus on execution. This is a key difference:
Feature LLM LAM
Primary Output Text, code, analysis Practical actions
Environment Interaction Limited Extensive
Need for External Tools Yes No (self-sufficient)
Training Complexity Medium High
Execution Cost Low to medium Medium to high

LAM and Multimodal Models

Multimodal models can process image, audio, and text simultaneously. LAMs use this capability for better environment understanding but go further: they act.

LAM and Reasoning Models

Reasoning models like O3 Mini and O4 Mini are strong at solving complex logical problems. LAMs combine this reasoning ability with execution capability—they not only find the solution but execute it too.

LAM in the New AI Ecosystem

LAM's Role in Web 4.0

LAMs can play a central role in Web 4.0—a generation of web that is intelligent, automated, and personalized. In this new world:
  • Websites are optimized with LAMs
  • User experience is fully personalized
  • Tasks are performed proactively

LAM and Physical AI

Combining LAM with Physical AI and robotics can create a revolution in industries:
  • Manufacturing: A robot that not only executes commands but makes decisions itself
  • Agriculture: Smart farming that automatically manages planting, maintaining, and harvesting
  • Construction: A robot that reads architectural plans and builds on its own

LAM in Smart Cities

In future smart cities, LAMs can:
  • Optimize traffic
  • Manage energy consumption
  • Coordinate public services
  • Automate crisis response

Challenges Ahead and Solutions

The Hallucination Problem

LAMs, like LLMs, may suffer from hallucination—performing actions that seem logical but are incorrect. Solutions include:
  • Multi-layer verification systems: Recheck before performing sensitive tasks
  • Immediate feedback: Display actions to user before execution
  • Continuous learning: Correct errors and prevent repetition

Cost and Accessibility

Advanced LAMs are currently expensive. To democratize this technology:
  • Model optimization: Reduce computational costs
  • Tiered models: Different versions for different needs
  • Resource sharing: Use shared cloud processing

Legal and Regulatory Issues

LAMs need legal frameworks:
  • Liability: Who is responsible for LAM mistakes?
  • Privacy: How is user data protected?
  • Consent: Users must knowingly use LAM

Business Opportunities with LAM

LAM Startups

LAM creates unprecedented opportunities for AI startups:
1. LAM as a Service (LAMaaS)
  • Provide LAM through API
  • Revenue model based on number of actions
  • Tiered pricing
2. Industry-Specific LAMs
  • Dedicated LAM for medicine
  • LAM for legal and judicial
  • LAM for real estate
3. LAM Development Platforms
  • Tools for building custom LAMs
  • LAM marketplace
  • Training and consulting

LAM Market

The LAM market is estimated to reach $50 billion by 2030. Profitable areas:
  • Business process automation
  • Customer experience management
  • Intelligent analysis and decision-making
  • Personalized services

Future Outlook: LAM in the Next Decade

Predictions

By 2035, we will likely see:
1. Universal LAMs: A single LAM that manages all your digital needs
2. Emotional LAMs: Using emotional AI for better understanding of human states
3. Self-Improving LAMs: Using self-improving models that are constantly learning
4. Social LAMs: Agents that act for you on social networks
5. Collaborative LAMs: Systems that cooperate with other LAMs to solve complex problems

Transformation of Work and Life

LAMs may transform the future of work:
  • Shorter work week: Automation of repetitive tasks
  • Focus on creativity: Humans focus on creative and strategic work
  • New skills: Need for LAM management and supervision skills

Conclusion: A New Era of Human-Machine Interaction

Large Action Models represent a paradigm shift in artificial intelligence. We have moved from the era of "AI that thinks" to "AI that acts." This change is deeper than it appears—it shows that AI can truly interact with the physical and digital world.
LAMs are still in their early stages. Many challenges—from security to ethics, from cost to accessibility—must be solved. But their potential is undeniable. In the not-too-distant future, each person may have a personal LAM assistant managing a large portion of their daily digital tasks.
The question is not whether LAMs will be our future—but how we can shape this future in a way that is beneficial, secure, and fair for everyone. By thoughtfully addressing challenges, investing in research, and collaborating between developers, policymakers, and society, we can use LAMs to build a better world.
The digital future is no longer just about getting information—it's about getting things done. And LAMs make exactly that possible.