Emergent World Models

• Emergent world models are structured internal representations that develop spontaneously in learning systems, enabling prediction and planning without explicit supervision.
• They arise from self-supervised objectives such as next-step prediction and collective inference, which promote compositional, interpretable, and generalizable dynamics.
• These models enhance cognitive functions in AI and biological agents, impacting reinforcement learning, language modeling, and multi-agent coordination.

Emergent world models are structured internal representations—often high-level, compositional, and predictive—that arise spontaneously within learning systems trained only with generic objectives, without explicit supervision for modeling environmental dynamics. Spanning machine learning, reinforcement learning, cognitive science, and even cosmology, emergent world models are increasingly recognized as a central phenomenon underpinning planning, adaptation, communication, and complex reasoning in artificial and biological agents. Recent empirical and theoretical research demonstrates that such models can form implicitly via prediction, control, communication, or self-preservation objectives, often exhibiting compositionality, interpretability, and generalization capabilities far beyond what is directly supervised.

1. Conceptual Foundations and Formal Definitions

Emergent world models arise when a learning system acquires internal representations or latent dynamics that enable prediction, planning, or abstraction about unobserved environmental states without explicit instruction. In contemporary neural networks, this occurs in several forms:

• Implicit World Models in RL and Meta-RL: Networks trained with only a primary behavioral or reward objective (e.g., homeostatic survival) may develop internal mechanisms—such as recurrent state representations—that encode a world model implicitly, i.e., not via an explicit transition loss but as a by-product of maximizing return or adaptation (Horibe et al., 2024).
• World Modeling in Transformers: Large models trained solely for next-token prediction in domains such as board games, stochastic games, or text, can acquire linear and nonlinear internal state encodings that correspond to the full (sometimes latent) environmental state, even in the absence of direct state supervision (Karvonen, 2024, Kamel et al., 18 Dec 2025).
• Collective and Communicative World Models: In multi-agent systems, shared representations or symbol systems can emerge through decentralized Bayesian inference or predictive coding, aligning with the notion of "collective world models" (Taniguchi et al., 2024).
• Spatial and Social Models: LLMs can develop linearly decodable spatial state-spaces, as well as subspaces that function as integrated "social world models" supporting Theory of Mind and pragmatic reasoning (Tehenan et al., 3 Jun 2025, Tsvilodub et al., 10 Feb 2026).
• Cosmological World Models: In theoretical physics and cosmology, "emergent world models" refer to mathematical models of the universe that avoid initial singularities, evolving from static or quasi-static early states due to exotic matter or quantum corrections (Rudra, 2012).

The precise mathematical nature of these models varies by context. In the language modeling setting, an emergent world model is often a set of latent variables or subspaces in the model's activations $h_t$, where a function $f(h_t)$ linearly or nonlinearly recovers quantities that describe the hidden state of the environment $s_t$, belief distributions $b_t(s)$, or other relevant variables.

2. Mechanisms Driving Emergence

Mechanisms underlying emergent world models are diverse but share common principles:

• Prediction and Self-supervision: In both recurrent and transformer architectures, the next-step prediction objective (over observations or tokens) induces networks to internally integrate perceptual cues, action histories, and context, forming internal representations with predictive sufficiency (Ventura et al., 3 Feb 2026, Molinari et al., 29 Sep 2025).
• In-context Learning (ICL) and Adaptation: In-context environment learning (ICEL) refers to an agent's ability to improve its predictions about a novel environment as a function of context length, using mechanisms of environment recognition (memorization/classification of environments) and environment learning (direct empirical updating) (Wang et al., 26 Sep 2025).
• Collective Predictive Coding: In multi-agent or linguistic settings, prediction errors distributed across agents are collectively aggregated to refine shared latent representations, leading to the emergence of symbol systems or latent spaces that jointly encode environmental state (Taniguchi et al., 2024, Nomura et al., 4 Apr 2025).
• Homeostatic and Survival Objectives: Optimization for robust homeostasis (maintaining interoceptive variables) drives agents to form implicit predictive mechanisms that serve as internal world models, supporting open-ended exploration and adaptation (Horibe et al., 2024).
• Compositionality and Binding: Emergent models often manifest compositional structure: in neural sequence models, this can take the form of binding object identity to spatial or relational roles via distributed representations (Ventura et al., 3 Feb 2026, Tehenan et al., 3 Jun 2025).

3. Empirical Evidence Across Domains

Empirical demonstrations of emergent world models encompass both single-agent and multi-agent systems, and a range of tasks:

• Board Games and Chess: Transformers trained on chess notation acquire high-fidelity, linearly decodable latent board states and even latent vectors for player skill estimation, supporting both causal interventions and improved win rates, despite no explicit state supervision (Karvonen, 2024).
• Stochastic and Partially Observed Games: In poker, GPT-style transformers acquire not only deterministic hand-rank representations but also encode Bayesian belief distributions and stochastic features (hand equity), evidenced via linear and MLP probes of residual activations (Kamel et al., 18 Dec 2025).
• Spatial Reasoning: LLMs encode a linear $\mathbb{R}^3$ subspace affinely mapping contextual embeddings to physical object positions, supporting compositionality, geometric reasoning, and causal interventions that control model outputs (Tehenan et al., 3 Jun 2025).
• Social Cognition: LLMs develop domain-general subspaces used for both Theory of Mind and pragmatic reasoning tasks, with causally implicated subnetworks revealed via functional localization and ablation (Tsvilodub et al., 10 Feb 2026).
• Multi-agent Coordination: In decentralized collective world models, agents communicating via bidirectionally aligned messages converge on low-dimensional symbol manifolds that reflect the environment state and support effective coordination under partial observability (Nomura et al., 4 Apr 2025).
• Minimal Cognitive Models: In GRU-based sequence models, mechanisms for path integration and flexible binding emerge solely from prediction objectives, resulting in the ability to learn and generalize novel object-location mappings during inference (Ventura et al., 3 Feb 2026).

Evidence consistently shows that such emergent representations are linearly or near-linearly accessible, causally intervene on behavioral outputs, and possess compositional generalization.

4. Theoretical Frameworks and Analysis

Several rigorous frameworks formalize the conditions and error bounds for emergent world modeling:

• ICEL Error Bounds: In world models for MDPs/POMDPs, error upper bounds for environment recognition vs. environment learning modes of ICEL are derived, showing $T^{-1/2}$ scaling in context length and highlighting trade-offs between environment diversity, context size, and model capacity (Wang et al., 26 Sep 2025).
• Collective Bayesian Inference: Generative EmCom formalizes shared communication and internal model formation as decentralized Bayesian inference with variational objectives, unifying emergent language, predictive coding, and world modeling (Taniguchi et al., 2024).
• Linear Probing, Koopman Operators: Probing experiments in VLA models and LLMs are justified theoretically via Koopman operator models and convergence arguments under ergodicity/p-independence assumptions (Molinari et al., 29 Sep 2025).
• Causal and Functional Localization: Functional subspaces supporting Theory of Mind, pragmatic inference, and syntax are experimentally isolated via activation-based localizers and ablation, establishing statistical and causal evidence for integration (Tsvilodub et al., 10 Feb 2026).

5. Architectural and Algorithmic Innovations

Architectures and training protocols displaying emergent world models implement various principles:

• Masked and Autoregressive Pretraining: Progression from masked models (BERT, MAE) to autoregressive and unified models (multimodal transformers, discrete diffusion) leads to richer, more persistent world representations (Bai et al., 23 Oct 2025).
• Systematic Probe Pipelines: Matryoshka sparse autoencoders and linear probes are used for dictionary learning and interpretability of world representations, enabling real-time human-in-the-loop verification in safety-critical settings (Molinari et al., 29 Sep 2025).
• Decentralized InfoNCE-Alignments: Bidirectional, contrastively-aligned message channels in decentralized multi-agent models ensure that emergent symbols accurately reflect high-dimensional environmental state under autonomy constraints (Nomura et al., 4 Apr 2025).
• Recurrent Memory and Consistency Policies: Long-horizon coherence and object permanence are maintained in memory-augmented models via architectural recurrence, external retrieval, and explicit consistency constraints (Bai et al., 23 Oct 2025).

6. Implications, Limitations, and Open Problems

The emergence of world models has broad implications:

• Generalization and Transfer: Emergent models often support compositional generalization to unseen instructions, objects, or environments, especially when environment diversity and long-range context are maximized during training (Wang et al., 26 Sep 2025, Ventura et al., 3 Feb 2026).
• Interpretability and Intervention: The linear decodability and causal manipulability of internal representations make emergent world models attractive for mechanism analysis, safety vetting, and interpretability in both RL and LLMs (Karvonen, 2024, Molinari et al., 29 Sep 2025).
• Limits of Current Findings: Many results leverage synthetic data or simplified environments—questions of scalability and real-world grounding remain open (Tehenan et al., 3 Jun 2025). Datasets targeting broader semantics or more nuanced causal dynamics are needed.
• Trade-offs and Regimes: Over-training on narrow environments can suppress environment-learning and favor memorization; architectural bottlenecks and context window sizes critically shape emergence (Wang et al., 26 Sep 2025).
• Multimodal, Social, and Cosmological Extensions: Emergent world models extend to multimodal integration, social cognition, communication systems, and theoretical cosmology, uniting structured prediction, communication emergence, and physical modeling under a common design space (Tsvilodub et al., 10 Feb 2026, Rudra, 2012).

7. Future Directions and Research Opportunities

Outstanding objectives include:

• Measuring World Model Quality: Defining metrics for internal logic, coherence, and alignment to “true” world dynamics in simulation and real environments (Bai et al., 23 Oct 2025).
• Scaling and Compression: Developing architectures and learning rules that sustain predictive world models under scaling, compression, and long-term memory constraints.
• Alignment and Safety: Leveraging interpretability pipelines and interventional protocols to align emergent world models with human values, norms, or safety requirements.
• Unifying Theoretical Principles: Further formalizing the relationships between predictive coding, Bayesian inference, information bottlenecks, and world model emergence in a range of neural and multi-agent settings (Taniguchi et al., 2024, Wang et al., 26 Sep 2025).
• Empirical Extensions: Applying and validating these principles in richer domains, such as robotics (safety-critical planning), open-ended virtual worlds, and societal-scale multi-agent simulations.

Emergent world models—whether implicit in single agents, collective across multi-agent populations, or distributed in self-supervised transformers—constitute a central mechanism enabling abstraction, planning, communication, and adaptation in complex systems. Their study provides a conceptual and technical bridge across reinforcement learning, language modeling, embodied AI, social cognition, and physical cosmology.