Introduction: The Convergence of Perception, Language, and Simulation
The Paradigm Shift from Unimodal to Multimodal Intelligence
The advent of Large Language Models (LLMs) has marked a watershed moment in artificial intelligence, demonstrating an unprecedented ability to process and generate human-like text.1 Models based on the transformer architecture have achieved remarkable proficiency in a wide array of natural language processing tasks, including translation, summarization, and dialogue generation.3 However, the very foundation of their intelligence—vast corpora of textual data—imposes a fundamental limitation. Text-only LLMs operate within a symbolic, abstract realm, disconnected from the rich, continuous, and physically grounded reality that humans perceive through their senses.4 Their understanding of concepts like “gravity” or “thermodynamics” is not derived from an internal model of physical laws but is instead an inferential construct based on statistical co-occurrences of words in their training data.4 This lack of embodiment and direct environmental feedback results in an intelligence that is linguistically powerful yet fundamentally ungrounded.
The transition to Multimodal Large Language Models (MLLMs) represents a paradigm shift, moving beyond an incremental feature addition to a necessary evolutionary step toward more general and robust intelligence.3 This evolution signifies a move away from single-modality pattern matching toward advanced systems that can perceive, interpret, and act across multiple data formats, including images, audio, video, code, and other sensory streams.6 By integrating diverse sensory inputs, MLLMs begin to synthesize information in a manner that more closely mimics human cognition, where verbal cues are connected with visual signals, gestures, and prior knowledge to infer meaning and guide decisions.6 This integration allows MLLMs to achieve a more comprehensive, nuanced, and holistic understanding of complex scenarios, reducing the ambiguities inherent in unimodal data and paving the way for more accurate and resilient AI systems.7
Core Thesis: Multimodality as the Catalyst for World Models
This report advances the thesis that the integration of diverse sensory modalities is the critical catalyst for the development of internal “world models” within AI systems. A world model is formally defined as a learned, internal simulation of an environment that enables an agent to understand its dynamics, predict future states, and plan sequences of actions to achieve desired outcomes.9 This capability stands in stark contrast to the reactive nature of traditional AI, which primarily learns to map inputs directly to outputs through trial and error.9 By building an internal representation of reality, an agent with a world model can “dream” or “imagine” the consequences of its actions without needing to execute them in the physical world, leading to a more efficient and human-like mode of learning and decision-making.11
The development of such sophisticated internal simulations is contingent upon access to the kind of rich, physically grounded data that only multimodal inputs can provide.7 While text can describe the world, it cannot fully capture the continuous, dynamic, and causal relationships that govern it. Vision provides spatial context, video captures temporal dynamics and motion, audio offers information about events and environmental properties, and other sensory inputs like proprioception provide feedback on an agent’s own state and interaction with the world.10 Without this constant stream of multimodal data to ground its understanding, an LLM’s internal representations remain abstract and untethered from physical reality, incapable of forming the predictive, causal models that are the hallmark of true world understanding.4 Therefore, multimodality is not merely an enhancement but the essential bridge that allows an AI system to transition from knowing
what is said about the world to understanding how the world works.
Distinguishing MLLMs from True World Models
It is crucial to establish a clear distinction between the capabilities of current state-of-the-art MLLMs and the concept of a true world model. Contemporary MLLMs, such as OpenAI’s GPT-4o and Google’s Gemini, are exceptionally powerful perception engines.7 They can process and generate outputs across a combination of text, image, audio, and video, demonstrating sophisticated cross-modal understanding.14 For example, they can answer nuanced questions about an image, generate code from a whiteboard diagram, or engage in real-time spoken conversation that is sensitive to emotional tone.6
However, the ability to perceive and process multimodal inputs does not, in itself, constitute a world model. A true world model is fundamentally defined by its predictive and causal capabilities, not merely its perceptual ones.12 It must be able to simulate “what if” scenarios, predict how an environment will evolve over time, and understand how its own actions will affect that evolution.19 While a modern MLLM can describe a video of a bouncing ball with high fidelity, a system with a true world model could predict the ball’s trajectory, account for friction and gravity, and reason about what would happen if the ball’s mass or elasticity were changed. This report will explore the significant gap that currently exists between the perceptual prowess of MLLMs and the predictive power of world models, using this distinction to structure an analysis of how current architectures are evolving toward this more ambitious goal.
Foundations of Multimodal Understanding in LLMs
The Three Pillars of Multimodal AI
The engineering challenge of building systems that can reason across different data types is rooted in three fundamental characteristics of multimodal information, as outlined in a 2022 paper from Carnegie Mellon University.7 A comprehensive understanding of these pillars—heterogeneity, connections, and interactions—is essential for designing effective MLLMs.
First, heterogeneity refers to the intrinsic diversity in the structure, quality, and representation of different modalities.7 Text is symbolic and discrete, composed of tokens from a finite vocabulary. Images are continuous and spatial, represented by grids of pixel values. Audio is a temporal waveform, and sensor data can take myriad other forms. An MLLM must be able to process these fundamentally different data structures, each with its own statistical properties and levels of noise, and translate them into a common representational language.7
Second, connections describe the complementary information and semantic correspondence that exist between modalities.7 Different modalities often describe the same underlying concept or event, providing overlapping and reinforcing information. For instance, the word “dog” and a photograph of a dog both refer to the same conceptual entity.20 A key task for an MLLM is to learn these connections, identifying statistical similarities and semantic alignments that allow it to build a more robust and complete understanding than would be possible from any single modality alone.21
Third, interactions refer to the complex, emergent relationships that arise when modalities are processed together.7 The meaning of a statement can be altered by the speaker’s tone of voice (text and audio interaction), or a visual scene can be re-contextualized by a caption (image and text interaction). Effective MLLMs must not only process each modality but also model these intricate inter-modal dynamics to capture the full, nuanced meaning of the combined input.
Data Fusion Strategies: When and How to Combine Modalities
The architectural choice of when and how to integrate information from different modalities is a critical decision that fundamentally shapes an MLLM’s capabilities. This process, known as data fusion, can be categorized into three primary strategies: early, intermediate, and late fusion.7
Early fusion, also known as feature-level fusion, combines raw data or low-level features from different modalities at the very beginning of the processing pipeline.8 For example, the pixel values of an image and the embeddings of a corresponding text caption could be concatenated into a single large vector before being fed into a neural network.22 This approach allows the model to learn deep, low-level correlations between modalities from the outset.22 However, it is highly sensitive to data synchronization and alignment—the modalities must be precisely matched in time or space—and can be vulnerable to noise in any one modality dominating the fused representation.7
Late fusion, or decision-level fusion, operates at the opposite end of the spectrum. In this strategy, each modality is processed independently by a separate, specialized model.7 The final outputs or predictions from these unimodal models are then combined to produce a final result, often through a simple mechanism like voting, averaging, or a weighted sum.22 Late fusion is robust and flexible, easily handling asynchronous or missing data from one modality, but it may fail to capture the subtle, fine-grained interactions between modalities that occur at earlier processing stages.22
Intermediate fusion represents a balance between these two extremes and is the most common approach in modern MLLMs.24 In this paradigm, each modality is first processed through its own dedicated encoder to extract a set of high-level features or latent representations.8 These representations are then merged at an intermediate layer within a larger network architecture, allowing for both modality-specific processing and joint learning of cross-modal interactions.7 This approach, often implemented using sophisticated mechanisms like cross-modal attention, offers a powerful compromise, preserving modality-specific features while enabling the model to learn complex, context-dependent relationships between them.8 The choice of fusion strategy is therefore not a minor implementation detail but a core architectural decision that biases the model toward learning different kinds of cross-modal relationships, from low-level statistical correlations (early fusion) to high-level semantic agreement (late fusion) or dynamic, context-aware interactions (intermediate fusion).
The Semantic Bridge: Joint Embedding Spaces
A cornerstone technique for enabling multimodal understanding is the creation of a joint embedding space. This is a shared, high-dimensional vector space where data from different modalities can be represented and compared directly.25 The fundamental principle is to map semantically similar concepts, regardless of their original modality, to nearby points in this common space.27 For instance, an image of a running dog, the text “a brown dog running,” and an audio clip of barking should all be encoded into vectors that are close to one another in the joint embedding space.24 This shared space acts as a semantic bridge or a universal “language” that allows the model to reason about concepts across different data types.26
The training of these joint embeddings typically relies on large-scale datasets of paired or aligned multimodal data, such as images with corresponding captions or videos with transcriptions.25 Each modality is processed by a dedicated encoder (e.g., a Vision Transformer for images, a text Transformer for language), and the model is trained to align the outputs of these encoders.26 A common training objective is a contrastive loss function, famously used in OpenAI’s CLIP model.21 During training, the model is presented with a batch of image-text pairs. It then learns to maximize the similarity (e.g., cosine similarity) between the embeddings of correct pairs (a “positive” pair) while simultaneously minimizing the similarity between embeddings of incorrect, mismatched pairs ( “negative” pairs).25
This process effectively pulls the representations of corresponding concepts together while pushing unrelated ones apart, structuring the embedding space so that proximity equates to semantic relevance. Once trained, this shared space enables powerful downstream capabilities without requiring task-specific architectures. These include cross-modal retrieval (using a text query to find relevant images), zero-shot classification (classifying an image by comparing its embedding to the embeddings of textual class descriptions), and providing the foundation for multimodal generation tasks.27 The joint embedding space is thus a critical component for achieving deep semantic understanding in MLLMs, providing a unified representational framework for diverse sensory inputs.28
Architectural Paradigms for Multimodal Integration
The Anatomy of a Modern MLLM
The architecture of a typical modern Multimodal Large Language Model (MLLM) can be deconstructed into three primary components that work in concert to process and reason about diverse data types.13 This modular design allows MLLMs to leverage powerful, pre-trained models for each component, making their development more efficient than training a massive, end-to-end system from scratch.31
- Modality Encoders: These are specialized neural networks responsible for the initial processing of each non-textual modality, transforming raw data into a format that the central language model can comprehend.30 For visual input,
Vision Transformers (ViTs) are the dominant choice. A ViT works by breaking an image into a series of smaller patches, converting each patch into a vector (embedding), and then processing this sequence of vectors like a sentence of text tokens.21 For audio, models like
Conformer or OpenAI’s Whisper first convert the raw audio waveform into a spectrogram—a visual representation of the spectrum of frequencies—which can then be processed similarly to an image.21 The output of these encoders is a sequence of feature-rich vectors, or “tokens,” that encapsulate the essential information from the input modality. - LLM Backbone: This component is the “brain” of the MLLM, responsible for higher-level reasoning, context integration, and generating the final output.13 The backbone is almost always a powerful, pre-trained LLM, such as those from the Llama, GPT, or Gemini families.13 By using a pre-trained LLM, the MLLM inherits vast world knowledge, linguistic capabilities, and in-context learning abilities, which would be computationally prohibitive to train from the ground up.31 The LLM backbone receives the processed tokens from the modality encoders alongside any textual input and synthesizes this information to perform the required task.
- Modality Interface (Connector): This is the crucial bridge that connects the modality encoders to the LLM backbone.13 Since the feature vectors produced by an image or audio encoder exist in a different mathematical space than the word embeddings the LLM was originally trained on, the connector’s job is to align these different representations.13 The complexity of this interface can vary significantly. The simplest approach is a
linear projection layer, which learns a transformation to map the visual/audio features into the LLM’s text embedding space.32 More sophisticated approaches use a multi-layer perceptron (MLP) for a more powerful, non-linear mapping.32 Advanced architectures employ dedicated Transformer-based modules, such as the
Q-Former used in models like BLIP-2, which uses a set of learnable queries to distill the most relevant information from the visual features, or cross-attention layers, which allow the LLM to dynamically attend to different parts of the encoded modality.13
The Mechanism of Interaction: Cross-Modal Attention
The most sophisticated and powerful mechanism for integrating multimodal information within modern MLLMs is cross-modal attention.34 It serves as a dynamic form of intermediate fusion, enabling fine-grained, context-aware interaction between different data streams.
To understand cross-attention, it is essential to first distinguish it from self-attention, the core mechanism of the Transformer architecture.36
Self-attention operates within a single sequence of data (e.g., the words in a sentence). It allows each token in the sequence to look at and weigh the importance of all other tokens in the same sequence, thereby building a contextually rich representation of each token.36 In contrast,
cross-attention operates between two different sequences.37 It allows tokens in one sequence to attend to tokens in a second, separate sequence, creating a bridge for information to flow between them.35
In a typical MLLM, cross-attention is used to connect the textual information being processed by the LLM backbone with the visual or auditory information processed by a modality encoder.35 The mechanism follows the standard Query-Key-Value (QKV) framework of attention. The sequence being processed by the LLM (the text) generates the
Query (Q) vectors. These queries represent questions like, “What information do I need from the image to understand this part of the text?” The sequence from the other modality (e.g., the patch embeddings from a ViT) provides the Key (K) and Value (V) vectors.35 The model calculates the similarity between each text query and all of the image keys. These similarity scores are then used to compute a weighted sum of the image value vectors. The result is a context vector that represents the most relevant parts of the image, as determined by the current textual context.35 This allows the model to dynamically “look at” specific regions of an image or segments of an audio clip that are most pertinent to the task at hand, enabling a much deeper and more flexible fusion of information than static concatenation or simple projection.22
The following table provides a comparative overview of the primary data fusion strategies discussed, summarizing their mechanisms, complexities, and ideal applications.
| Fusion Strategy | Core Mechanism | Architectural Complexity | Key Advantages | Key Limitations | Ideal Use Cases |
| Early Fusion | Combine raw data or low-level features at the input stage into a single representation. | Low to Moderate | Captures rich, low-level cross-modal correlations. | Requires precisely synchronized and aligned data; sensitive to noise in any single modality. | Tasks with tightly coupled and high-quality data, such as audio-visual speech recognition. |
| Intermediate Fusion | Process modalities separately, then fuse their latent representations at an intermediate layer. | Moderate to High | Balances modality-specific feature learning with deep joint interaction; flexible. | More complex architectures; can be computationally intensive. | Complex reasoning tasks requiring dynamic interaction, such as Visual Question Answering (VQA) and autonomous vehicle perception. |
| Late Fusion | Process each modality through independent models and combine their final predictions. | Low | Robust to missing or asynchronous data; simple to implement; leverages unimodal experts. | May miss fine-grained, low-level interactions between modalities. | Scenarios with asynchronous data streams or varying modality quality; ensemble-based classification. |
Table 1: A comparative analysis of multimodal data fusion strategies, synthesizing data from sources.7
State-of-the-Art Architectures: A Comparative Analysis (2024-2025)
The field of MLLMs is characterized by rapid innovation, with leading technology firms pursuing distinct architectural philosophies to advance the state of the art. An analysis of flagship models from OpenAI, Google, and Meta reveals competing paradigms in modality integration, computational efficiency, and accessibility.
Case Study 1: OpenAI’s GPT-4o – The End-to-End “Omni” Model
OpenAI’s GPT-4o (“o” for “omni”) represents a significant architectural leap toward a truly unified multimodal system.15 Its defining feature is its end-to-end, single-model architecture. Unlike previous systems that relied on a pipeline of separate models (e.g., a speech-to-text model, followed by a text-based LLM, followed by a text-to-speech model), GPT-4o processes all inputs—text, audio, image, and video—within the same, single neural network.15
The core innovation of this unified approach is the elimination of information loss that occurs when data is passed between separate, specialized models. In the previous pipeline approach, crucial non-textual information from audio, such as tone, emotion, laughter, or the presence of multiple speakers, was lost during the transcription phase.15 GPT-4o, by processing the raw audio stream directly, can perceive these nuances and incorporate them into its reasoning process, enabling more natural, responsive, and emotionally aware conversational interactions.41 This end-to-end training across modalities allows the model to develop a more holistic internal state, where different sensory inputs are represented and correlated within a single, coherent framework. To manage the immense computational demands of such a model, it is likely that GPT-4o employs advanced optimization techniques, such as sparse attention mechanisms to focus computation on the most relevant information and mixed-precision training to reduce memory and processing overhead.43 The architecture of GPT-4o signifies a move away from modular, piecemeal multimodality and toward a deeply integrated system, a critical step for building a coherent internal world model.
Case Study 2: Google’s Gemini 1.5 – Efficiency and Long-Context
Google’s Gemini 1.5 family of models, particularly Gemini 1.5 Pro, prioritizes computational efficiency and an unprecedented ability to process long-context information.44 The architectural foundation of Gemini 1.5 is a sparse
Mixture-of-Experts (MoE) Transformer.44 In a standard “dense” transformer, all model parameters are activated for every input token. In an MoE architecture, the model is composed of many smaller “expert” sub-networks. For any given input, a routing mechanism selects only a small subset of these experts to perform the computation.45 This allows the total number of parameters in the model to be massive, endowing it with vast knowledge, while keeping the computational cost of inference relatively low, as only a fraction of the model is used at any one time.44
The key innovation enabled by this efficiency is Gemini 1.5’s massive context window, which can extend up to 10 million tokens in experimental versions.44 This allows the model to ingest and reason over vast amounts of multimodal information in a single pass, such as an hour of video, 11 hours of audio, or entire code repositories with over 30,000 lines of code.16 This long-context capability is profoundly important for world modeling, as it enables the model to understand and track dependencies, relationships, and narratives over extended temporal horizons. While GPT-4o focuses on the richness of real-time interaction, Gemini’s architecture is optimized for deep, comprehensive analysis of large-scale, complex multimodal datasets, making it exceptionally well-suited for tasks that require integrating context over long periods.44
Case Study 3: Meta’s Llama 3.2 – Integrating Vision into an Open-Source Framework
Meta’s Llama 3.2 series brings powerful multimodal capabilities to the open-source ecosystem, focusing on a pragmatic and accessible architectural design.49 The vision-capable models, Llama 3.2 11B and 90B, are built upon the established auto-regressive transformer architecture of the Llama family.51 Vision is integrated through a novel two-stage vision encoder and the systematic insertion of cross-attention layers within the language model’s decoder.53 The first stage of the vision encoder processes local image patches, while a second, global encoder integrates these features to form a coherent scene understanding. The cross-attention layers, placed at regular intervals (every 5th layer), allow the model to continuously ground its text generation process in the visual context, ensuring that the output remains relevant to the image.53
The primary innovation of the Llama 3.2 approach is its combination of strong performance with an open-weight philosophy. By releasing the model weights, Meta enables the global research and developer community to study, fine-tune, and build upon the architecture, accelerating innovation.49 The design is intended to be a “drop-in replacement” for the text-only Llama models, simplifying adoption for developers already working within the Llama ecosystem.54 This strategy democratizes access to state-of-the-art MLLM technology, fostering a diverse range of applications and research directions that might not be pursued within a closed, proprietary model framework.50 Llama 3.2’s architecture thus represents a powerful and practical pathway for adding sophisticated vision capabilities to highly capable, existing LLMs.
The following table summarizes the key architectural differences and strategic priorities of these leading MLLMs.
| Feature | OpenAI GPT-4o | Google Gemini 1.5 Pro | Meta Llama 3.2 90B Vision |
| Core Architecture | End-to-end Unified Transformer | Sparse Mixture-of-Experts (MoE) Transformer | Auto-regressive Transformer |
| Modality Integration | Single neural network processes all modalities natively. | Multimodal inputs processed into a shared token space. | Integrated two-stage vision encoder with cross-attention layers in the decoder. |
| Key Innovation(s) | Unified end-to-end processing, low-latency interaction, perception of audio nuances (tone, emotion). | Massive context window (up to 1-10M tokens), high computational efficiency via MoE. | Open-weight model, systematic visual grounding via periodic cross-attention. |
| Supported Modalities | Input: Text, Audio, Image, Video. Output: Text, Audio, Image. | Input: Text, Audio, Image, Video, PDF. Output: Text. | Input: Text, Image. Output: Text. |
| Availability | Proprietary API | Proprietary API | Open-Weight (Llama 3.2 Community License) |
Table 2: A comparative overview of the architectural features and strategic focus of leading MLLMs as of 2024-2025, synthesizing data from sources.41
From Multimodal Perception to Coherent World Models
Defining the World Model: An Internal Simulator
The transition from multimodal perception to the development of a coherent world model represents a fundamental leap in AI capability. A world model is not merely a system that perceives its environment; it is an internal, generative model that learns a compressed, abstract representation of that environment and uses this representation to simulate future states and plan actions.9 This concept, championed by researchers such as Yann LeCun, is considered a critical component for achieving human-level AI.12 Instead of relying on computationally expensive trial-and-error in the real world, an agent equipped with a world model can perform “mental practice” by imagining various action sequences and predicting their outcomes within its internal simulation.12 This allows for far more efficient learning, long-horizon planning, and the ability to generalize to novel situations by reasoning from first principles about the environment’s dynamics.12
This approach diverges significantly from the standard paradigm of LLMs, which are primarily trained to predict the next token in a sequence based on statistical patterns in data.4 While this enables impressive linguistic fluency, it does not inherently endow the model with a causal or predictive understanding of the world it describes. A world model, by contrast, must learn the underlying rules, physics, and causal relationships that govern its environment, forming an internal representation that is not just descriptive but also predictive and manipulable.56
The Indispensable Role of Multimodal Data
The construction of a robust and accurate world model is fundamentally impossible from textual data alone. Language is a powerful medium for conveying abstract knowledge, but it is an incomplete and often ambiguous representation of physical reality. Multimodal data is indispensable for three key reasons.
First, it provides grounding for abstract linguistic concepts.4 A model can read trillions of words describing gravity, but it only begins to build an intuitive, predictive model of gravity by observing countless videos of objects falling, feeling the proprioceptive feedback of lifting heavy objects, or hearing the sound of an impact.10 Sensory data tethers abstract symbols to concrete, physical phenomena, resolving the ambiguity inherent in language and forming the bedrock of a robust internal model.7
Second, world models must capture dynamics—the principles of motion, interaction, and causality that govern how the world changes over time.10 These principles are most effectively learned from sequential, time-series data like video and continuous sensor streams, which explicitly show how states evolve as a result of actions and physical laws.10 Static text can only describe these dynamics; video and interaction data allow the model to learn them directly.
Third, multimodal inputs provide a richer, more constrained context that resolves ambiguity. A textual description might be open to multiple interpretations, but when combined with a corresponding image or audio clip, the range of plausible meanings is significantly narrowed.7 This data redundancy across modalities helps the model build a more accurate and resilient representation of the world, one that is less susceptible to noise or missing information in any single channel.7
The Current State: Generative Video and its Limitations
The current state of the art in generative AI, particularly text-to-video models, offers a glimpse into the nascent stages of world model development. Systems like Google’s Genie 3 are described as foundational world models because they can generate interactive, dynamic, and temporally consistent virtual environments from a single text prompt.19 These models can simulate aspects of the physical world, such as water and lighting, and allow a user or an AI agent to navigate and interact with the generated environment in real time, with the world responding consistently to actions.19 This represents a significant step beyond static image generation, as the model must maintain a coherent state over time and across user interactions.
However, a critical analysis reveals a significant gap between the ability to generate a visually plausible video and the possession of a true, physically accurate world model. This distinction is the primary frontier in current research. Benchmarks specifically designed to probe for an understanding of physics have shown that today’s generative models are severely lacking. The Physics-IQ benchmark, for example, tests models on their ability to generate videos that adhere to principles of fluid dynamics, optics, and solid mechanics, and finds that even leading models like Sora and Runway perform poorly, demonstrating that “visual realism does not imply physical understanding”.59 Similarly, the
Morpheus benchmark uses physics-informed metrics to evaluate generated videos against conservation laws, concluding that current models struggle to encode physical principles despite creating aesthetically pleasing outputs.60
This evidence highlights a crucial point: current models are exceptionally skilled at learning the surface statistics of the visual world. They have learned what a physically plausible event looks like from being trained on vast amounts of video data. However, they have not necessarily learned an internal, causal model of the underlying physics that governs why the event unfolds in a particular way. They are masters of pattern replication, not yet masters of causal prediction. The challenge for the next generation of world models is to bridge this chasm, moving from simply generating what is likely to see next to predicting what must happen next according to the learned laws of the environment.
Advanced Reasoning in Multimodal World Models
Thinking in Steps: The Rise of Multimodal Chain-of-Thought (MCoT)
The standard auto-regressive generation process of LLMs, where each token is predicted based on the preceding ones, can be brittle when applied to complex, multi-step reasoning problems. To address this, the Chain-of-Thought (CoT) prompting technique emerged, significantly improving reasoning performance by instructing the model to generate a sequence of intermediate steps before providing a final answer.61 This concept has been extended into the multimodal domain, giving rise to
Multimodal Chain-of-Thought (MCoT), a framework designed to make the reasoning process over combined text and visual data more explicit, robust, and interpretable.61
The core idea of MCoT is to structure the reasoning process into distinct stages, often separating rationale generation from final answer inference.61 This allows the model to first build a solid foundation of understanding by leveraging multimodal information before attempting to synthesize a conclusion. MCoT encompasses a variety of structured reasoning paradigms.62 For example, when faced with a complex visual question, an MLLM using MCoT might first generate a textual caption of the image, then identify and localize key objects mentioned in the question, and finally use this structured information to formulate a step-by-step rationale that leads to the answer.63 This approach transforms a single, difficult inference problem into a series of smaller, more manageable sub-tasks, such as perception, localization, and logical deduction.63 By making the intermediate reasoning steps explicit, MCoT not only improves accuracy but also provides a transparent “thought process” that can be evaluated and debugged.65
Beyond CoT: Automated Structured Thinking
Building on the principles of MCoT, recent research is pushing towards more sophisticated and automated reasoning frameworks that endow MLLMs with capabilities akin to human deliberative thinking. These approaches move beyond simple prompting techniques to integrate explicit algorithmic structures for planning, verification, and self-correction into the reasoning process.
One key area is task decomposition and planning, where models learn to autonomously break down a high-level, complex goal into a logical sequence of executable sub-steps.65 This is a foundational capability for any agent that needs to perform multi-step tasks in the real world.
Another powerful technique is the integration of tool use and Retrieval-Augmented Generation (RAG).3 An MLLM can be trained to recognize when its internal knowledge is insufficient and to call upon external tools to augment its reasoning. This could involve executing code to perform a calculation, querying an API for real-time information, or using a retrieval system to pull in relevant facts from a knowledge base.6 This grounds the model’s reasoning in verifiable, external data, reducing hallucinations and improving factual accuracy.
Perhaps the most advanced frontier is the development of iterative refinement and self-correction mechanisms. Frameworks like the Coherent Multimodal Reasoning Framework (CMRF) and Q* cast multi-step reasoning as a search or planning problem rather than a single generative pass.67 In these systems, the model generates a potential reasoning step, evaluates its confidence or consistency, and if necessary, backtracks to explore alternative reasoning paths or re-decomposes the problem.66 This iterative process of generation, evaluation, and refinement mimics human problem-solving and allows the model to correct its own errors, leading to more robust and reliable conclusions.67 The development of these advanced reasoning frameworks is an implicit acknowledgment that the raw auto-regressive process of LLMs is insufficient for complex logic. These techniques act as “cognitive scaffolds,” imposing a more deliberate, structured, and verifiable thought process onto the model, guiding its powerful generative capabilities toward more accurate and coherent reasoning.
Embodied Intelligence: World Models in Robotics and Autonomous Systems
Grounding AI in Physical Reality
The ultimate testbed and application for world models is embodied intelligence—the integration of AI into physical systems like robots and autonomous vehicles that must perceive, reason, and act in the real world.69 For an embodied agent, a world model is not an abstract concept but a practical necessity. To navigate a cluttered room, manipulate an unfamiliar object, or interact safely with humans, the agent must possess an internal model that can predict the consequences of its actions on the physical environment.10 This requirement to ground abstract reasoning in concrete physical action makes robotics the crucible in which the true capabilities and limitations of world models are forged and tested.72
From Vision-Language to Action
Multimodal Large Language Models are rapidly becoming the central nervous system for a new generation of intelligent robots, bridging the gap between high-level human goals and low-level motor control.73 This integration is manifesting in several key areas of robotics.
First, MLLMs excel at instruction following, translating ambiguous, high-level natural language commands (e.g., “clean up the table”) into specific, actionable steps.74 This leverages the commonsense knowledge embedded in the LLM to interpret human intent and formulate a logical plan.
Second, MLLMs are being used for task and motion planning. By leveraging their reasoning capabilities, these models can decompose a complex goal into a sequence of sub-goals and even generate the code or control parameters needed to execute them.75 For example, a model might determine that picking up a cup requires first opening a cabinet, then identifying the cup, then planning a grasp, and finally executing the arm trajectory.75
Third, and most critically for world model development, generative MLLMs are used for environment simulation and model-based reinforcement learning. An embodied agent can use its world model to “imagine” the outcomes of different potential action sequences without physically performing them.77 By simulating the future, the agent can select the plan most likely to succeed, learn from simulated mistakes without real-world consequences, and develop more efficient and safer behaviors.11
The Next Frontier: Integrating Proprioception and Touch
While vision and language provide an agent with the ability to perceive its environment and understand goals, they are insufficient for building a complete world model for physical interaction. True embodiment requires the integration of additional sensory modalities, chief among them proprioception and touch.21
Proprioception is the sense of the body’s own position, orientation, and movement. For a robot, this means integrating data from joint encoders and inertial measurement units to have a precise understanding of its own physical state. Touch, provided by tactile sensors on grippers and other surfaces, provides critical information about contact, force, pressure, and texture that is unavailable through vision alone.21
Integrating these modalities is the next frontier for embodied world models. A model that combines vision, language, proprioception, and touch can learn a much richer and more physically grounded representation of the world. It can learn the difference between hard and soft objects, understand the forces required for manipulation, and predict how objects will behave upon contact. This creates a closed feedback loop: the agent’s actions (informed by its world model) change its state and create new sensory input (proprioception, touch), which in turn updates and refines its world model. This active, interactive learning process is fundamentally different from the passive observation of video data. It forces the world model to be action-conditioned, learning to predict not just what the world will look like, but what the world will feel like as a consequence of its own actions. This tight coupling of perception, action, and prediction is the essence of embodied intelligence and the key to developing truly adaptive and capable autonomous systems.
Benchmarking and Evaluation: Quantifying Coherence and Understanding
The Challenge of Evaluating Internal States
One of the most profound challenges in the development of world models is evaluation. Since a world model is an internal representation of the environment, its coherence and accuracy cannot be directly measured.79 We cannot simply “look inside” the neural network to see if it “understands” physics. Consequently, researchers must design carefully constructed tasks and benchmarks that serve as external proxies for the model’s internal understanding. The quality of a world model can only be inferred by its performance on tasks that are impossible to solve without such an internal model.79 The evolution of these benchmarks provides a clear trajectory of the field’s ambitions, moving from testing basic perception to probing for deep, causal, and predictive reasoning.
Assessing Commonsense and Multidisciplinary Reasoning
The first step beyond simple perception is to evaluate a model’s ability to reason using high-level knowledge. Several benchmarks have been developed to test this in a multimodal context.
The Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark is a prominent example.80 It is composed of over 11,500 questions sourced from college-level exams, quizzes, and textbooks, spanning six core disciplines from Art & Design to Tech & Engineering.80 The questions require not only perceiving complex visual information (from 30 different image types like charts, diagrams, and chemical structures) but also applying expert-level, domain-specific knowledge to reason toward a solution.6 The performance of even the most advanced models on MMMU is telling: GPT-4V, a state-of-the-art MLLM, achieved an accuracy of only 56%.80 This result underscores the significant gap that remains in the ability of current models to integrate perception with deep, specialized knowledge.
Other benchmarks like ScienceQA, MM-Vet, and A-OKVQA similarly test reasoning across visual and textual scientific questions.6 A wide range of datasets also target specific facets of commonsense, such as social, temporal, physical, and moral reasoning, challenging models to go beyond factual recall and demonstrate an intuitive grasp of how the world works.81
Assessing Physical Reasoning and Simulation
While commonsense benchmarks test a model’s descriptive and inferential knowledge, a more direct evaluation of its predictive world model capabilities requires testing its understanding of physical laws. A new class of benchmarks has emerged specifically for this purpose.
The PHYRE (Physical Reasoning) benchmark presents agents with a series of 2D physics puzzles.82 To solve a puzzle, the agent must place one or more objects into a scene such that, when the simulation runs, a goal condition is met (e.g., a green ball touches a blue ball). Success requires an intuitive understanding of concepts like stability, momentum, and object interaction. PHYRE is designed to measure generalization by testing agents on both new configurations of familiar puzzles and on entirely new puzzle types not seen during training.82
More recently, benchmarks like Physics-IQ and Morpheus have been developed to evaluate the physical plausibility of videos generated by large-scale text-to-video models.59 Physics-IQ tests models across five domains—solid mechanics, fluid dynamics, optics, thermodynamics, and magnetism—and finds that current models have “severely limited” physical understanding, even when they produce visually realistic outputs.59 Morpheus uses physics-informed neural networks to assess whether generated videos adhere to fundamental conservation laws, again concluding that models struggle to encode these principles.60
The progression of these benchmarks—from perception (e.g., ImageNet), to knowledge-based reasoning (e.g., MMMU), and finally to predictive physics (e.g., Physics-IQ)—is not arbitrary. It mirrors the research community’s shifting goalposts. As models master one level of capability, the definition of “intelligence” is refined, and new, more challenging evaluations are created to measure progress toward the next frontier. This trajectory clearly indicates that the ultimate objective is not just an AI that can see or talk about the world, but one that possesses a predictive, causal understanding of it.
Grand Challenges and the Path Toward AGI
Technical Hurdles on the Path to Coherent World Models
Despite rapid progress, the path to developing robust, coherent world models is fraught with significant technical challenges that span the entire development pipeline, from data processing to model training and deployment.
Data Alignment: A foundational challenge is the alignment of heterogeneous data streams. This involves not only creating semantic correspondence (e.g., linking the word “car” to an image of a car) but also ensuring precise synchronization in time and space.83 For video and audio, this means exact temporal alignment to the millisecond; for robotics, it means spatially grounding textual commands to specific coordinates or objects in the 3D world.6 Achieving this at scale across massive, noisy datasets is an immense engineering problem.86
Catastrophic Forgetting: Neural networks, including MLLMs, have a tendency to forget previously learned information when trained on a new task or dataset. This phenomenon, known as catastrophic forgetting, is a major barrier to the goal of continual, lifelong learning that a true world model would require.87 An agent that learns to identify birds in a new environment must not forget how to recognize cats. Current research shows that fine-tuning an MLLM on one dataset can lead to a significant performance drop on others, making it difficult to build models that can continuously accumulate and integrate knowledge from new experiences.87
Computational Scalability: The resource requirements for training and deploying state-of-the-art MLLMs are astronomical.89 Training a frontier model requires thousands of high-end GPUs running for weeks or months, costing tens to hundreds of millions of dollars. This creates a significant barrier to entry for academia and smaller companies and raises long-term questions about the environmental and economic sustainability of the current scaling paradigm.89 While research into more efficient architectures and training methods is ongoing, the sheer scale of these models remains a primary bottleneck.90
Hallucinations and Inconsistency: MLLMs are prone to generating outputs that are factually incorrect, logically inconsistent, or contradictory to the provided multimodal input—a problem broadly termed “hallucination”.68 For a world model, which must serve as a reliable basis for prediction and planning, such inconsistencies are a critical failure. Ensuring that a model’s internal representation is coherent and consistently grounded in reality across all modalities is an unsolved and critical research problem.92
Ethical and Safety Considerations
As MLLMs evolve into more capable world models and are integrated into autonomous agents, the ethical and safety implications become increasingly acute.
Bias and Fairness: MLLMs are trained on vast, often uncurated datasets from the internet, which are replete with societal biases related to race, gender, and culture.93 These biases can be learned and amplified by the model, leading to unfair, stereotyped, or discriminatory outcomes, particularly when the model is used in sensitive domains like healthcare or law enforcement.91
Misuse and Misinformation: The ability to generate highly realistic and coherent multimodal content (e.g., video, audio) creates powerful tools for misinformation and malicious use.6 AI-generated media could be used for impersonation, fraud, or propaganda, making the development of robust detection and watermarking techniques a critical safety priority.6
Agentic Risks: The most profound challenges arise from the deployment of autonomous agents that operate based on their internal world models.6 If an agent’s world model is flawed, incomplete, or misaligned with human values, it could take actions that are harmful or unpredictable.94 Ensuring that an agent’s goals and the internal model it uses to pursue them are robustly aligned with human safety and well-being is perhaps the most difficult and important long-term safety problem in AI.89
The Vision for the Future: Competing Paths to AGI
While there is a growing consensus that world models are a crucial step toward Artificial General Intelligence (AGI), there is a significant debate within the research community about how these models will be built.19 This debate represents the central strategic schism in AGI research today, pitting the dominant paradigm of scaling against more structured, cognitively inspired approaches.
The Scaling Hypothesis, implicitly pursued by many of the largest industry labs, posits that many advanced capabilities, including a functional world model, may emerge implicitly as a result of massively scaling up current MLLM architectures on ever-larger datasets and with more computational power.31 From this perspective, a sufficiently large and well-trained model will eventually learn the underlying structure of the world as the most efficient way to compress and predict the data it observes.
In stark contrast, influential researchers like Yann LeCun argue that this approach is fundamentally flawed and that simply scaling auto-regressive models will never lead to true intelligence.55 He advocates for a modular architecture centered on a predictive world model trained with self-supervised objectives, such as his proposed Joint Embedding Predictive Architecture (JEPA).56 This approach is designed to learn abstract representations and predict future states, enabling the kind of planning and common sense that he argues even a house cat possesses but current LLMs lack.55
Similarly, Joshua Tenenbaum‘s research focuses on reverse-engineering the principles of human cognition.97 He proposes building AI systems that integrate the pattern-recognition strengths of neural networks with the symbolic, causal, and probabilistic reasoning capabilities of structured models from cognitive science, such as probabilistic programs.99 This approach aims to create models that can learn new concepts from very few examples and build coherent, lifelong models of the world in a more human-like way, emphasizing deep understanding over brute-force statistical learning.99 The resolution of this fundamental debate—whether intelligence will emerge from scale or must be engineered with cognitive structure—will define the trajectory of AI research for the next decade.
Conclusion and Strategic Recommendations
Recapitulation of Key Findings
This analysis has charted the convergence of multimodal reasoning and world model development, establishing that the integration of diverse sensory data is the critical catalyst for transforming Large Language Models from abstract text processors into systems with a grounded, predictive understanding of the world. The architectural evolution from modular, pipeline-based systems to unified, end-to-end models like GPT-4o, and highly efficient, long-context architectures like Gemini 1.5, demonstrates a clear trajectory toward more holistic information processing. However, a significant gap persists between the advanced perceptual capabilities of current MLLMs and the causal, predictive power of a true world model. Benchmarks in physical reasoning reveal that today’s models excel at generating visually plausible outputs but often fail to adhere to fundamental physical laws, indicating a mastery of surface statistics rather than deep, causal understanding. The path forward is obstructed by formidable challenges, including data alignment, catastrophic forgetting, and computational scalability, while the increasing autonomy of these systems raises profound ethical and safety considerations.
Recommendations for Future Research
To accelerate progress toward coherent and reliable world models, the research community should prioritize efforts in three key areas:
- Data Curation and Generation: There is an urgent need to move beyond static, descriptive datasets like image-caption pairs. Future research should focus on creating and curating large-scale, interactive datasets that capture rich physical dynamics, causality, and long-term temporal dependencies. This includes data from robotics, egocentric video, and simulated environments where actions and their consequences are explicitly recorded. Furthermore, leveraging world models themselves to generate high-quality synthetic data for training in rare or dangerous scenarios (e.g., autonomous vehicle edge cases) is a promising avenue for scalable data creation.10
- Hybrid and Cognitively-Inspired Architectures: While scaling has proven remarkably effective, a singular focus on it may lead to diminishing returns. Research should increasingly explore hybrid architectures that integrate the powerful, scalable pattern recognition of Transformers with more structured, explicit modules for causal reasoning, physical simulation, and hierarchical planning. Drawing inspiration from cognitive science, as advocated by researchers like Tenenbaum and LeCun, by incorporating principles of probabilistic programming, object-centric representations, and predictive self-supervised learning could provide a more direct path to robust world models.56
- Interaction-Centric Evaluation: Current benchmarks, while valuable, are largely passive. The next generation of evaluation methodologies must be interaction-centric. This involves creating dynamic, simulated environments where an AI agent’s world model can be tested through its ability to plan, act, and adapt to unforeseen circumstances. Benchmarks should be designed to assess not only predictive accuracy but also capabilities like active learning—the ability of an agent to identify uncertainty in its own world model and take actions to gather the information needed to refine it.
Final Outlook: The Dawn of Predictive AI
The synthesis of multimodal reasoning and world model development marks a pivotal moment in the evolution of artificial intelligence. It signals a fundamental transition away from AI systems that primarily describe, classify, and retrieve information about the world, toward systems that can understand, predict, and act within it. This shift from descriptive to predictive intelligence is the defining characteristic of the next generation of AI. While the challenges are immense, the continued integration of richer sensory data, the development of more sophisticated reasoning architectures, and the creation of more demanding, interaction-based evaluations form the most promising and direct path toward more general, capable, and ultimately, more intelligent artificial systems.