I. The essential bottleneck of the current paradigm of the universal large model
The current mainstream universal large model is essentially based oninductive statisticsAbove Paradigms: Learning from Massive Texts and Multimodal DataCo-occurrence distribution among tokens generates output by predicting the next token. It excels at imitating human language, repeating knowledge, and generating fluent text, but there are inherent constraints on this underlying mechanism that are difficult to eradicate:
1. There is no independent and persistent explicit world state, all information is temporarily carried in the context window, and it is easy to forget facts, self-contradiction and illusion when reasoning in long chain;
2. What you learn is correlations in the data, not stable causal mechanisms. Models can write seemingly rigorous inferences, but cannot reliably distinguish them."Correlation" and "causation", counterfactual assumptions are easily distorted;
3. Knowledge, rules and reasoning logic are all embedded in the black box of model parameters. If you want to modify a core constraint, you can only retrain; the reasoning process is unauditable, and the credibility of the conclusion cannot be traced.
In short: existing large models are excellenttext/Multimodal Generator, but it is not a simulator that can continuously perceive, maintain the state of the world, and preview the causal evolution of events. When the task is upgraded from copy generation, simple question and answer to complex event judgment, fact confirmation, and multi-scenario counterfactual deduction, the shortcomings of pure statistical induction routes will be rapidly enlarged.
Industry consensus: general artificial intelligence wants to make further breakthroughs, can not only continue to expand model parameters and training data, paradigm needs to be frompredict the nexttoken, towardsPredicting the Next World Stateevolution--which is the orientation of the world model.
II. Evolution of Universal Large Model: Large Model+ Fusion architecture for world models
The future universal large model will not be replaced by the world model, and the two will form a complementary symbiotic system:The large model serves as the interface of language, perception and expression; the world model serves as the internal virtual simulator, responsible for state maintenance, causal deduction and scenario rehearsal.
The evolutionary path is roughly divided into three layers:
1. Tier 1: Large model base ability continues to increase
The Pedestal Model continues to optimize multimodal understanding, long-text memory, and basic symbolic reasoning, assuming the roles of natural language interaction, information extraction, and concept translation. It is responsible for transforming human questions, instructions, and external information into states and actions that the simulator can recognize, and translating the state results derived by the simulator back into human-readable language. This layer is still an inductive statistical paradigm, retaining the flexible and open expression capabilities of the large model.
2. Layer 2: Built-in world model simulator to build state space and state transition mechanism
inAn explicit state space is maintained independently of LLM. When new information is input or intervention actions are applied, the new state is updated according to the established transition rules. The model no longer relies on text to complete reasoning, but repeatedly simulates event evolution in the internal virtual space, and carries out multi-step chain deduction, counterfactual assumptions, and risk prediction.
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The core change in this layer: inference from"Text probability continuation" becomes "state evolution simulation". |
3. The third layer: hybrid rule constraint system, to solve the problem of causal unreliability of pure data learning
Relying entirely on data induction to automatically learn all rules cannot guarantee the credibility of key scenarios. Future high-performance general simulators will adopttop-level artificial axiomatic constraint + inductive evolution parameter of underlying dataCore axioms, value constraints and fact confirmation rules are explicitly defined by people as hard bottom lines, while complex rules such as event propagation and dynamic evolution are induced by historical cases to obtain soft parameters.
This fusion architecture solves the biggest pain point of the current large model: the reasoning process can be traced, the fact change can be audited, the causal deduction is more stable, and the factual illusion is greatly reduced.
However, the research and development focus of the universal world model is currently concentrated on the physical world, robots, and visual scenes. The universal world model does not deal with cognitive issues such as truth, evidence traceability, and multi-subject acceptance conflicts. This is also the blank point of the universal world model landing public opinion, brand recognition, and fact governance scenarios.
3. The original cast of Vientiane Yuan Drive (MDSE) route
Vientiane MirrorMDSE elemental drive solidification casting technical route,Subject world modelInspired by the "state space + state transfer" paradigm, it is not a simple replica of the universal world model.It migrates this set of simulation ideas to the information cognitive space, providing an engineerable new paradigm for large models to land credible cognitive tasks, bringing three key inspirations:
enlighten1: The world model can not only simulate the physical world, but also build a cognitive world simulator.
Universal world model simulates objects, space, physical dynamics; Vientiane solidification transforms state space intocognitive state space: StatusS is no longer pixel, object position, but atomic facts, evidence chain, information subject, credibility, confirmation mark. Action A is information intervention behavior such as new report, subject voice, evidence supplement, etc. State transition corresponds to dynamic evolution of information dissemination, fact conflict and credibility.
This proves that the world model paradigm has the ability of cross-domain migration. Besides physical simulation, it can be used to simulate abstract information systems such as facts, public opinion and cognitive games.
enlighten2: Credible scenarios must follow the mixed route of "artificial top-level rules + data induction parameters", rejecting pure induction or pure symbols.
pureLLM relies on inductive statistics, with uncontrollable cause and effect and serious hallucinations; traditional expert systems rely on manual writing of massive rules, and their generalization ability is weak in the face of opening up new scenarios.
Vientiane casting adoptsKUCR four-layer confirmation axiom serves as the top-level hard constraint to ensure the credibility of the bottom line of fact verification; then the propagation evolution parameters are learned from a large number of historical public opinion cases, giving consideration to controllability and generalization ability.
This provides a reference for the future universal credible large model: scenarios with high risk and strong credibility requirements cannot be completely automatically learned by data, and the core underlying axioms must be explicitly solidified.
enlighten3. Decoupling of reasoning, generation and confirmation is the key to realizing auditable AI.
Traditional large model couples information extraction, inference and conclusion generation into model parameters, so it is impossible to trace the change process of each fact separately.
In Vientiane Solidification Architecture:
• Large models are responsible for information extraction and natural language interaction;
• MDSE meta-drive is responsible for cognitive state maintenance and state transition deduction;
• The KUCR confirmation module independently verifies factual conflicts and records a snapshot of each status change.
The three are decoupled, and each step of fact acquisition and state update leaves an auditable link.
This decoupling idea is exactly the future highly credible universalImportant design directions for AI.
IV. Summary
The long-term evolution of the universal large model is not simply to make the model larger, but to upgrade from a single text probability generator toA composite agent of "large model interaction interface + world model internal simulator". World model is the core direction of this evolution route, but the universal world model naturally prefers physical environment simulation, and there are shortcomings in the field of fact credibility and evidence confirmation.
Vientiane Yuan drive solidification casting route, drawing lessons from the world model state deduction thought, landing it into the cognitive fact space, exploring a set ofThe engineering implementation scheme of "atomic fact modeling, explicit cognitive state, mixed rule deduction, and full auditable". It is not necessarily the ultimate form of a general large model, but it provides a paradigm sample with great reference value for credible AI, fact deduction, and event simulation scenarios.
