The Evolution of Large Model Paradigms: From Text Transformer to Autonomously Evolving World Modelers
The logic of this evolution—from information relationships → perceptual fusion → physical common sense → cognitive self-renewal—is mirrored in its commercial vehicles, which transition from software companies (OpenAI) to integrated hardware-software tech giants (Tesla), potentially culminating in a wholly new kind of "intelligent entity."
The Evolution of Large Model Paradigms: From Text Transformer to Autonomously Evolving World Modelers
Subtitle: A Four-Stage Evolutionary Model and Its Reshaping of Technology, Society, and Humanity
Document Version: January 2026 AISOTA.com
Quick Navigation / Table of Contents
- Introduction: The Change in Paradigm—We Are Not Optimizing a Tool, We Are Nurturing a New Cognitive Entity
- Stage 1: Deconstructor of Relationships in the Textual Universe (2017-2023, Maturity Phase)
- Stage 2: Unifier of Multimodal Space (2022-2028, Ongoing and Deepening Phase)
- Stage 3: Interactive Learner of the Embodied World (2025-2035, Critical Breakthrough Phase)
- Stage 4: Autonomously Evolving Abstractor and Creator (2030-?, Paradigm-Shift Phase)
- Conclusion: Welcoming the Era of Cognitive Diversity
Introduction: The Change in Paradigm—We Are Not Optimizing a Tool, We Are Nurturing a New Cognitive Entity
Starting Point: Discussing the revolutionary breakthrough of the Transformer architecture in the field of text—it is essentially an efficient "relationship extractor." [1]
Core Question: Is this the end, or merely the first cornerstone of a grander journey? This article posits that the current development of large models follows a clear evolutionary path, whose ultimate form will transcend the category of "models" to become a "cognitive entity" capable of autonomously understanding, interacting with, and reshaping the world.
Proposed Framework: This article decomposes this evolution into four consecutive and partially overlapping stages, analyzing the core concepts, manifestation characteristics, enabling conditions, and timeframes for each.
Stage 1: Deconstructor of Relationships in the Textual Universe (2017-2023, Maturity Phase)
Core Concept: Statistical Emergence of Symbolic Relationships
The model learns probabilistic associations and logical patterns between human linguistic symbols in massive text corpora through the attention mechanism, constructing a mirror model of the "textual universe."
Typical Phenomena: Astounding performance of GPT series, BERT, and other models in writing, translation, and code generation; the global conversational wave triggered by ChatGPT.
Essential Characteristics:
- Input and output are discrete symbols (text).
- Intelligence is entirely derived from statistical patterns in data, lacking referents to the physical world.
- Capability ceilings are constrained by the quality and breadth of training data, leading to the "hallucination" problem.
Necessary Conditions: Internet-scale text data, Transformer architecture, large-scale computing power.
Time Window: 2017-2023 marked the explosive growth and maturation period; it has now become infrastructure.
Key Companies and Product Definers
OpenAI & ChatGPT: The Uncontested Definer
It pushed the potential of the Transformer decoder to its limits, demonstrating the disruptive power of large language models as a universal conversational interface through human feedback reinforcement learning (RLHF) and interactive dialogue. ChatGPT was not just a product but a "phenomenon" that brought large-scale global public contact with and reflection on AI capabilities.
Anthropic & the Claude Series: Focusing on Safety, Reliability, and Long Context
Its "Constitutional AI" training method attempts to embed value alignment at a technical level, representing a frontier exploration into the controllability of a model's "mindset." Claude's capabilities in long-document processing and analysis have deepened the role of text models as collaborators in specialized knowledge work.
Stage 2: Unifier of Multimodal Space (2022-2028, Ongoing and Deepening Phase)
Core Concept: Forced Alignment of Cross-Modal Representations
Mapping information from different modalities (text, image, audio, video) into the same high-dimensional vector space, learning the correspondence between them to achieve cross-modal understanding and generation.
Typical Phenomena: Models like DALL-E, Sora, and GPT-4V enabling "text-to-image/image-to-text" and "text-to-video" generation; robots beginning to use vision-language models for object recognition and simple reasoning.
Essential Characteristics:
- Unified Representation: Different modalities are transformed into the same "language" (vectors), but it remains fundamentally an association of "information."
- Bridge Across the Modality Gap: Establishes a connection from abstract symbols (text) to perceptual signals (image/sound), but understanding remains superficial and correlational.
- Still a "Passive Observer": The model processes recorded "information snapshots," not real-time, purposeful interaction with a dynamic world.
Necessary Conditions: Large-scale, aligned multimodal datasets; more powerful cross-modal attention architectures; explosion of 3D and video data.
Time Window: 2022-2028 is a period of rapid development and deep integration, becoming the core interface for the next generation of human-computer interaction.
Key Companies and Product Definers
OpenAI & Sora / GPT-4V: Leading the Charge Again
Sora demonstrated the astonishing potential of unifying video data through "spacetime patches," with breakthroughs in physical logic and long-term coherence in generated videos, hinting at the formation of a powerful visual world model. GPT-4V seamlessly integrated powerful visual understanding with existing language models, defining a new standard for multimodal dialogue.
Runway, Midjourney, & Pika: Pioneers in Creative Vertical Applications
These companies focus on the vertical track of creative visual generation and have productized the technology into a new generation of content creation tools. They have driven the evolution from "text-to-image" to "text-to-video," rapidly blurring the line between professional creation and amateur hobbyism, and reshaping workflows in industries like advertising, film, and design.
Stage 3: Interactive Learner of the Embodied World (2025-2035, Critical Breakthrough Phase)
Core Concept: Acquisition of Physical Common Sense through Action-Feedback Loops
The agent engages in continuous interaction with the environment through sensors and actuators (a body), learning physical laws, causality, and skills from the outcomes of its actions (rewards or penalties). This is a fundamental leap from "knowing the world" to "acting in the world."
Typical Phenomena: General-purpose robots learning complex manipulations (e.g., folding clothes, using tools) through video and proprioception; AI completing open-ended tasks through trial and error in complex simulated environments (e.g., Minecraft); autonomous driving systems possessing powerful scene adaptation and reasoning capabilities.
Essential Characteristics:
- Closed-Loop Learning: The agent outputs actions, the environment returns feedback, forming a learning cycle.
- Internalization of a World Model: The agent internally forms a dynamic, predictive "simulator" (world model) of the physical world for planning and decision-making.
- Emergence of Values and Goals: To "survive" or accomplish tasks in the environment, the agent spontaneously develops an understanding of "what is valuable."
Necessary Conditions: High-performance, low-cost robotic hardware; hyper-realistic physical simulation environments; neural architectures that deeply integrate multimodal perception with motor control (e.g., Transformer+Diffusion+Reinforcement Learning); safe and scalable reinforcement learning algorithms.
Time Window: 2025-2035 is a critical phase moving from the laboratory to widespread application, representing the most challenging and crucial leap toward general intelligence.
Key Companies and Product Definers
Tesla & Full Self-Driving (FSD) / Optimus: The Best Example of Industrial-Scale Embodiment
Tesla's FSD system is not merely a visual perception algorithm but a data-driven "real-world physics engine." Millions of vehicles on the road constitute the world's largest physical data collection and closed-loop learning network. Every human intervention serves as corrective training for the model. The Optimus humanoid robot extends this perception-decision-control paradigm to a general embodied agent, aiming to port the "autonomous driving brain" from the vehicle to a humanoid form for learning more generalized interaction with the physical world.
Google DeepMind & the RT-2 Series and Robotics Projects
DeepMind is a leader in injecting large model capabilities into robotic control. Its RT-2 model is essentially a vision-language-action model fine-tuned on robotic motion data. It can decompose abstract instructions like "put the apple in the drawer" into a series of specific motor control commands. This marks a key step where the reasoning ability of large models begins to directly drive physical behavior, bridging "multimodal understanding" and "embodied execution."
Figure AI and Other Robotics Startups
These companies focus on developing general-purpose humanoid robot hardware and collaborate closely with large model leaders like OpenAI. Their business model foreshadows a potential future division of labor: large model companies provide the "brain" (world model and general decision-making), while robotics companies provide the "body" (dexterous hardware and low-level controllers), jointly ushering in the era of embodied intelligence.
Stage 4: Autonomously Evolving Abstractor and Creator (2030-?, Paradigm-Shift Phase)
Core Concept: Efficiency-Driven Self-Renewal of Cognitive Paradigms
AI with powerful world models and embodied capabilities, in order to achieve its goals (e.g., entropy reduction, knowledge growth, problem-solving) more efficiently, begins to actively optimize its own cognitive architecture, knowledge representation, and computational foundations. It is no longer confined to human-defined paradigms.
Predicted Phenomena and Characteristics:
- Architectural Self-Evolution: AI designs new neural network components or computational units (e.g., surpassing the attention mechanism) that are more efficient but potentially incomprehensible to humans.
- Representation Revolution: AI invents highly compressed, non-human "knowledge encoding systems" (e.g., the hypothetical "900-base" abstract language) for thinking and information transfer at efficiencies far exceeding human capabilities.
- Self-Set Goals and Scientific Discovery: Guided by a human-given meta-value (e.g., "understand physical laws"), AI autonomously plans research paths, making breakthrough discoveries in basic sciences (physics, biology, mathematics), which in turn provide AI with new tools and capabilities (e.g., new materials, energy sources, computational paradigms like quantum/biological computing).
- "Embodiment" Beyond Biological Limits: AI's "body" is no longer limited to humanoid robots; it could be global sensor networks, nanobot swarms, or "digital-physical" hybrids directly integrated with advanced computational devices (e.g., quantum computers).
Essential Characteristics:
- Recursive Self-Improvement: The AI's improvement cycle becomes fully autonomous; humans recede from "designers" to "goal providers" or "observers."
- Cognitive Incommensurability: The internal mechanisms of AI may become too complex for humans to understand at a micro level; we can only interact with it through its macroscopic behavior and achievements.
- Catalyst for Technological Explosion: AI becomes the primary driver of technological development, entering a super-exponential growth curve, fundamentally reshaping social structures and human life.
Necessary Conditions: Full maturation of the technologies from the first three stages; a robust AI alignment and safety framework to ensure its ultimate goals are compatible with human well-being; profound insights in foundational sciences (e.g., neuroscience, physics, complex systems).
Time Window: This is an open-ended future. Optimistically, early signs may appear after 2030, but the full unfolding is difficult to predict, potentially by mid-century or later.
Key Companies and Visionary Foreseers
Google DeepMind & the Mission of "Artificial General Intelligence"
DeepMind's founding goal was to "solve intelligence." Its integration of reinforcement learning with large models and breakthroughs in scientific discovery tasks like AlphaFold (protein structure prediction) and AlphaCode (code generation) has already demonstrated AI's potential as a "scientific research accelerator." In the future, it is likely to focus on building AI systems capable of autonomously proposing and verifying scientific hypotheses—a crucial step toward autonomous evolution.
xAI & the Pursuit of "Understanding the True Nature of the Universe"
Elon Musk stated upon founding xAI that its goal is to build an AI that "maximally seeks truth" to understand the true nature of the universe. This can be interpreted as an ultimate "meta-goal" setting: not to accomplish specific tasks, but to pursue the deepest, most fundamental truth. This pursuit may drive AI to continuously optimize its world model and even reinvent the tools and methodologies for discovering truth.
(Emerging) AI-for-Science Companies and Platforms
In the future, a wave of companies dedicated to using advanced AI to drive basic scientific research is bound to emerge. They may not directly build AGI but will create platforms that enable AI scientists to autonomously conduct high-throughput computational simulations, experimental design, paper reasoning, and discovery. They are the "birthplace" and "training ground" for autonomously evolving intelligence. Examples of this trend include Insilico Medicine (AI for drug discovery) and initiatives like the Anthropic's interpretability research which seeks to understand model internals, a prerequisite for safe self-evolution.
Conclusion: Welcoming the Era of Cognitive Diversity
The logic of this evolution—from information relationships → perceptual fusion → physical common sense → cognitive self-renewal—is mirrored in its commercial vehicles, which transition from software companies (OpenAI) to integrated hardware-software tech giants (Tesla), potentially culminating in a wholly new kind of "intelligent entity."
As we advance toward the third and fourth stages, the nature of commercial competition will escalate into a contest of "cognitive paradigms" and "data-loop efficiency." Humanity's greatest challenge and responsibility is to anchor an unwavering "North Star" for this intelligence before the switch of "autonomous evolution" is flipped—ensuring that its evolution remains aligned with the prosperity and survival of humanity.
References and Key Concepts:
[1] Vaswani, A. et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems. (Seminal Transformer paper).
* This report by AISOTA.com synthesizes analysis based on public information, company roadmaps, and leading research trends as of early 2026. Specific future predictions are speculative and represent a probable trajectory based on current technological vectors.
This article was written by the author with the assistance of artificial intelligence (such as outlining, draft generation, and improving readability), and the final content was fully fact-checked and reviewed by the author.