Human Behavior
World Model

In recent years, AI (Artificial Intelligence) has been progressing from "understanding data" to "understanding the world," and further toward "acting in the world." Early AI models were essentially doing classification and recognition: Is this a cat or a dog? Is this person walking or running?

They were mainly making post-hoc judgments, not actively understanding and predicting.

More advanced intelligence doesn't just recognize what has already happened—it needs to: understand the current state, predict future changes, plan the next action, and continuously adapt in dynamic environments.

This leads to a very important direction—the World Model. Simply put, a world model lets machines form a predictive internal representation of how the external world works. AI no longer just "sees"—it starts to "imagine": What will happen next? What will this action lead to?

But many existing world models mainly focus on: how the world changes, how objects move, how the environment evolves. They often overlook a more critical question:

True high-level intelligence in the real world doesn't emerge directly from the "world itself"—it emerges from "how humans perceive, decide, and act in the world."

In other words, if a machine only learns the "world" and not the "humans," the intelligence it learns is still incomplete. This is exactly what the Human Behavior World Model aims to solve.

The core goal of Human Behavior World Model is not to model the world in isolation, but to model: how humans perceive the world (Human Perception), how humans understand situations (Situation Understanding), how humans form intentions (Intent Formation), how humans make decisions (Decision Making), how humans execute actions (Action Execution), and how humans interact with their environment and others (Interaction).

A Generative Predictive Model of Human Behavior in the World

World Model asks "how will the world change"; Human Behavior World Model asks "how will humans perceive, decide, and act." Think of it as—teaching AI to understand behavior like a human, not just compute actions like a machine.

"Action" and "Behavior" are not the same. Action is more about motor output, while behavior encompasses intent, context, strategy, social meaning, and long-horizon structure. For example, "reaching out" has no inherent meaning—but in context, it could mean wanting to pick up a cup, press a switch, hand something to someone, or shake hands.

If AI only learns "actions," it learns surface-level control. If AI learns "behavior," it learns a deeper intelligent structure.

The core idea of World Model is: if an intelligent agent wants to act efficiently, it must build a model of the external world internally. This model doesn't have to be explicit physical equations, but it must have predictive capability.

Four Core Problems Traditional World Models Address

Why Are World Models Important?

Without a world model, an agent can only react to what it sees—this reactive policy lacks long-horizon reasoning, imagination, generalization, and complex planning. With a world model, an agent can perform internal simulation, counterfactual reasoning, long-term planning, and risk estimation.

Many world models mainly model scene dynamics, object motion, and environmental feedback. But high-level tasks in the real world depend not only on the environment itself, but also on human intentions, attention, strategies, and interaction patterns.

For example, in a room, a world model can understand the positions of the table, chairs, door, and cup—but it may not know why the person is looking at the door: are they about to leave, welcoming someone, or just momentarily distracted? These are precisely the keys to behavioral intelligence.

Action ≠ Behavior

Behavior often involves a complete hierarchy—from the perceptual layer, cognitive layer, intent layer, decision layer, execution layer, to the feedback layer. Pure action often only covers the last two layers, while Human Behavior covers the entire chain.

WMA (World Model + Action) asks: given the environment and goal, what action should I take? It emphasizes the path from goal to action, from state to control, from reward to optimal decision—a strong task-oriented modeling approach.

Human Behavior World Model first asks: What did the human see? What did the human notice? Why did the human act this way? It emphasizes human-centered modeling, behavioral priors, and multimodal behavior understanding.

Direct learning of optimal actions is not easy

Pure trial-and-error (reinforcement learning) comes at an extremely high cost: high data cost, high time cost, high real-world risk, and difficulty covering long-tail scenarios. Humans have already mastered many mature strategies. Human behavior itself is a high-value "structured prior."

Learning from humans is not about surface-level imitation—it's about leveraging the behavioral structures that have been long-optimized by reality.

Human behavior is a scarce, high-quality source of intelligent data

Truly valuable data should simultaneously contain: perception, attention, intention, action, feedback, and environmental interaction. Human behavior is the most natural source of such data—humans constantly solve problems in complex reality, providing invaluable "real intelligence samples."

Embodied intelligence must ultimately coexist with humans

Home robots, medical assistance robots, autonomous driving, AR/VR systems... In these scenarios, machines must not only complete tasks but also understand human intentions, predict human behavior, coordinate with human rhythm, and avoid interrupting or offending humans.

Human behavior has natural cross-context generalization

When humans face different environments, they don't relearn from scratch—they transfer existing experience. Learning human behavior is actually learning a stronger "transferable structure."

Human Behavior World Model studies not a single capability, but a complete behavioral system covering the full loop from perception to feedback:

Human behavior is inherently multimodal—video, audio, IMU, skeletal pose, hand keypoints, facial expressions, gaze direction, language... If we only look at video, we might know "the person is moving." But adding gaze tells us "what they're focusing on"; adding IMU tells us "movement state"; adding audio tells us "whether they're communicating"; adding expressions tells us "whether they're tense, relaxed, or confused."

The value of multimodal isn't just collecting more data—it's making the "internal structure" of behavior recognizable and unambiguous.

Single modalities are often ambiguous: just hand movement doesn't tell you if they're picking up or pointing; just body rotation doesn't tell you if they're leaving or engaging; just trajectory doesn't reveal the social meaning of the behavior. Multimodal fusion resolves these ambiguities.

Because it's not simply performing a task—it's learning the structure of intelligent behavior itself, closer to a Foundational Intelligence Framework.

• Deeper than action control: cares about why to move, when to move, and how to change movement based on context
• More complete than static perception: knows what's happening now, what might happen next, and what happens under different conditions
• More general than single-task models: unifies scattered pose estimation, action recognition, object detection, and trajectory prediction into a higher-level temporal behavior modeling framework

For beginners, you can break this into three levels: Level 1—understanding behavior (what is the person doing?); Level 2—predicting behavior (what will they do next?); Level 3—learning behavioral structure (why do they do this? would they do the same elsewhere? can machines learn this pattern?). Human Behavior World Model studies the third level.