Large Behavior Models (LBMs): The Next Frontier in Artificial Intelligence

Artificial Intelligence (AI) has rapidly transformed from a niche academic field to a driving force of the global economy, society, and culture. Central to this revolution are Large Language Models (LLMs) like OpenAI’s GPT series, Google’s Gemini, and Anthropic’s Claude. These models have demonstrated a remarkable capacity to understand and generate human language, allowing machines to write essays, code software, compose music, and hold convincing conversations.

Yet, while LLMs excel in manipulating text, they do not inherently understand behavior. They can describe what someone might do, but they do not model actual human actions, sequences, decisions, or context-based responses. This gap between language and action—between what is said and what is done—is where a new class of AI models emerges: Large Behavior Models (LBMs).

LBMs are poised to revolutionize AI by moving beyond text into the realm of decision-making, behavioral prediction, interaction, and simulation. These models will not only predict what you might say, but what you might do, how you’ll respond in various environments, and how agents (human or artificial) interact with one another in dynamic, realistic, and goal-driven ways.

1. What is a Large Behavior Model?

A Large Behavior Model (LBM) is an AI system trained on vast amounts of behavioral data—spanning physical actions, decisions, emotional reactions, social interactions, and contextual responses—designed to understand, predict, and simulate complex agent behavior in dynamic environments.

While LLMs operate primarily on textual data (what people say), LBMs operate on multimodal behavioral data (what people do, how they act, how they respond over time). They can be trained using video, sensor data, logs of physical movement, time series of actions, environmental inputs, decision trees, and even neurophysiological signals.

LBMs aim to develop embodied intelligence—AI that interacts with the world not just through a keyboard or screen, but through physical, social, and temporal engagement. They are a cornerstone in the development of artificial general intelligence (AGI), because general intelligence requires general behavior: the ability to learn, adapt, act, and interact across situations.

2. Why Behavior Matters in AI

Humans are not merely linguistic creatures. We are behavioral beings—moving through space, forming habits, solving problems, collaborating in teams, navigating challenges, adapting to change. Understanding human behavior is key to building machines that can coexist with us.

Language models can simulate empathy in writing, but cannot exhibit true empathetic behavior without modeling physical and emotional context. They can describe how to play a game, but cannot play it unless they can model the sequence of actions and the rewards and consequences. A chatbot might give you advice about fitness, but without behavioral modeling, it cannot adapt to your habits, routines, and environment.

Behavior is where intention, memory, perception, and action converge. LBMs integrate these dimensions to simulate a fuller spectrum of intelligence.

3. Building Blocks of Large Behavior Models

Creating an LBM requires integrating several technologies and methodologies, each contributing to its ability to observe, simulate, and generate behavior.

Multimodal Data Ingestion

LBMs rely on data far beyond text. They must ingest and process:

• Video of people interacting in various contexts (e.g., classrooms, offices, hospitals)
• Motion capture and sensor data from wearables
• Logs from software systems showing sequences of actions
• Decision outcomes from games, simulations, or real-world processes
• Environmental cues such as objects, lighting, temperature, or social proximity

This diversity of input enables LBMs to build rich, contextual representations of environments and agent states.

Behavioral Embedding Spaces

Just as LLMs learn word embeddings that represent meaning, LBMs must develop behavioral embeddings—mathematical representations of actions, decisions, routines, and preferences. These embeddings allow the model to compare, predict, and cluster behaviors across individuals and scenarios.

For instance, "greeting a coworker" might be represented differently depending on culture, context, time of day, and prior history—but all would map to similar points in a behavioral embedding space.

Temporal Memory and Context

Behavior unfolds over time. LBMs must maintain a temporal memory of past actions and anticipated future outcomes. This allows them to generate consistent and coherent behavior across time spans—from seconds (e.g., conversation flow) to weeks (e.g., project planning) to years (e.g., lifestyle modeling).

Goal-Directed Decision Frameworks

Many LBMs incorporate reinforcement learning, which trains agents to take actions that maximize cumulative rewards in simulated environments. This decision-making capacity is crucial for models that must not only describe but choose behaviors.

4. Applications of LBMs

The potential applications of LBMs span nearly every domain where human behavior matters.

Digital Humans and Virtual Assistants

LBMs can power digital characters that remember your preferences, adapt their routines to your needs, and behave with lifelike consistency. These digital humans could become companions, coaches, tutors, or even therapists, behaving not just with verbal competence but emotional intelligence and behavioral continuity.

Simulation and Planning

Government, military, and industrial actors can use LBMs to simulate human or group behavior in scenarios such as:

• Evacuation planning in natural disasters
• Crowd control in public spaces
• Battlefield simulations
• Urban development planning
• Market reaction simulations

By modeling how thousands of agents behave in parallel, LBMs provide powerful foresight into complex systems.

Robotics and Embodied AI

For robots to interact effectively in homes, hospitals, or workplaces, they must understand and predict human behavior. LBMs allow robots to navigate social norms, anticipate human actions, and adjust their own behavior accordingly. A robot nurse, for instance, could approach a patient based on behavioral cues of discomfort or stress.

Education and Personalized Learning

AI tutors powered by LBMs could observe student behaviors over time—engagement levels, attention spans, hesitation patterns—and adapt instruction methods to optimize learning. They could simulate classroom environments for teacher training or even role-play student personas to help educators prepare.

Behavior-Based Cybersecurity

In digital systems, human behavior leaves traces—login patterns, mouse movements, workflow sequences. LBMs can learn normal behavioral patterns and detect anomalies that suggest insider threats, fraud, or cyberattacks. This shift from rule-based to behavior-based security is already underway in sectors like finance and defense.

Consumer Behavior and Market Research

By modeling consumer decision-making behavior—not just from surveys, but from real-time interactions—companies can better understand customer journeys, loyalty drivers, and purchase intentions. LBMs could simulate how a new product might perform under different economic or cultural conditions.

5. The Science Behind LBMs

LBMs are not built from scratch; they evolve from decades of AI research:

• Cognitive Architectures: Frameworks like ACT-R, Soar, and Leabra attempted to model human cognition and behavior. LBMs build on these ideas with neural network power.
• Reinforcement Learning Agents: From DeepMind’s AlphaGo to OpenAI’s Dota agents, behavior modeling in games laid the groundwork for real-world simulations.
• Transformer Architectures: The success of LLMs using transformers has led to multimodal transformers like Gato, Flamingo, and Gemini, which serve as architectural blueprints for LBMs.
• Simulated Environments: Tools like Minecraft, MuJoCo, Habitat, and CARLA allow AI agents to practice behaviors in sandbox worlds before applying them in real ones.

6. Ethical and Social Implications

As with any transformative technology, LBMs raise profound questions about privacy, control, bias, and responsibility.

Behavioral Surveillance

If LBMs are trained on data from surveillance cameras, smart devices, or social platforms, who owns that behavioral data? Could it be used to manipulate, profile, or punish individuals? The line between personalization and coercion becomes dangerously thin.

Bias in Behavior Modeling

Training data reflects the biases of the societies from which it’s drawn. If LBMs learn from discriminatory or exclusionary behavior, they may reproduce or amplify those patterns—whether in hiring simulations, policing models, or digital companions.

Autonomy and Free Will

If an AI can predict your behavior better than you can, does that compromise your autonomy? Could LBMs be used to influence choices in subtle ways—from advertising to political persuasion?

Simulated Humans

As LBMs create increasingly realistic digital humans, how do we distinguish between real and artificial behavior? Could this blur the boundaries of identity, consent, and trust?

7. Toward Artificial General Behavior

Many experts believe that the road to AGI (Artificial General Intelligence) will run through Artificial General Behavior (AGB)—the capacity of an AI to act sensibly across domains, goals, and environments.

LLMs are one component of this; LBMs are another. Together, they enable AI systems to perceive, reason, communicate, and act in human-like ways. The next generation of AI will not just be intelligent—it will be agentic: goal-directed, behaviorally autonomous, and contextually aware.

8. The Future of LBMs: Predictions

Over the next decade, LBMs are likely to evolve along the following lines:

• More Real-Time Adaptability: LBMs will be able to adapt to new environments on the fly, using a combination of pretraining and continual learning.
• Emotionally-Aware Behavior: Models will detect and respond to emotional states, enabling more compassionate and human-centered AI.
• Cross-Agent Modeling: LBMs will simulate multiple agents interacting in shared environments, leading to advances in group behavior, cooperation, and social simulation.
• Behavioral APIs: Developers will use LBMs via APIs to add "behavioral realism" to applications, just as they use LLMs today for text.
• Regulation and Policy: Governments will begin to regulate how behavioral data can be collected, modeled, and used—especially in surveillance and predictive policing contexts.

Behavior is the Missing Link

The story of AI has long focused on perception (computer vision, speech recognition) and language (LLMs). But human intelligence is not just about seeing and saying—it’s about doing.

Large Behavior Models represent the next phase in AI’s evolution: an effort to build systems that do not merely speak or observe but act, adapt, learn from consequences, and interact over time.

By bridging the gap between knowing and doing, LBMs bring us closer to AI that can truly share our world—not just as tools, but as intelligent partners in action.

Whether assisting doctors in surgery, guiding students in classrooms, managing cities, or exploring other planets, LBMs will define a future where behavior itself becomes programmable, predictable, and—if we’re wise—ethical and human-centered.

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