The biggest weakness of current advanced information processing systems (LLMs), I believe, is “logical collapse,” or hallucination, where the AI tells plausible lies. I view this problem not merely as an issue of data quantity but as a structural flaw. Thus, I propose the “Adiabatic Integration” approach, which involves completely separating the central “Calculation Unit” responsible for logical manipulation from the external memory storing vast “Empirical Knowledge.” This mechanism, somewhat like mathematics, guarantees the validity of inference through logical constraints—in other words, correction codes. The design philosophy is similar to that of Aristotle AI, which possesses a capability level to win a gold medal at the International Mathematical Olympiad. The core of this system, the “Central Circuit,” operates on a “Formal Graph Description Language” based on first-order predicate logic, entirely eliminating ambiguity. By directly encoding these operational rules into a graph-structure-based circuit, the transparency and verifiability of inference are maximized. We all dislike people who speak based on empirical rules, right? Deliberative Inference System (System 2) This is the “brain” of this AI. It holds no knowledge and executes only logical primitives such as formal graph rewriting, integration, and contradiction resolution. Notably, logical relationships like causality and exclusion are embedded as constraint codes within the Intermediate Representation (IR) formal graph itself. During training, weights are fixed to specialize solely in the accurate execution of these logical rules, preventing unnecessary fluctuations. In essence, it’s like solving a puzzle according to rules. Completely Separated RAG (System 1?) This is the database of empirical knowledge, but it’s not just a knowledge repository. It stores not only perceptual attribute vectors (like color and sound) but also the logical constraints derived from experience itself as correction codes within formal graphs. Furthermore, each piece of knowledge holds “contextual relative positional information,” which indicates how highly it is related to a specific query. When a query arrives, this relative coordinate is calculated, and the most relevant knowledge is actively inserted into the inference path, enabling high-speed responses. The key is achieving fast responses while simultaneously ensuring logical verification. Metacognitive Control and Dynamic Switching A “Self-Inspection System (Metacognitive Auditor)” oversees the entire system. It manages the logical auditing of inferences and the dynamic switching between “Fast Response (System 1)” and “Deliberation (System 2)” depending on the situation. The switching triggers are clear. For example, if System 1′s inference steps exceed a threshold, or if a contradiction is detected in the correction codes between the intermediate form and external knowledge, it automatically switches to the deeply deliberative System 2. If the inference result of System 2 passes the logical audit, it switches back to System 1. If the audit fails, the structure of the contradiction and the knowledge ID are output as an external teacher signal log, which is utilized for future off-line training. This feedback loop is the essence of the design philosophy for ceaselessly improving the system’s overall accuracy. It’s like reviewing a test and memorizing the parts you still got wrong. Training Method: Focusing on Logical Correctness This system is trained not by chasing the quantity of data but by focusing on “logical correctness” as its core. Foundation Training of Formal Logical Operations: Using synthetic datasets, the system learns the accurate graph rewriting from a premise graph to a conclusion graph, and the ability to resolve contradictions from graphs containing conflicts. A rigorous method is employed, which evaluates the structural similarity (graph editing distance) between the inference result and the correct answer. In short, it evaluates whether the thought process is correct. Logical Encoding Training of Empirical Knowledge: This trains the ability to accurately encode causal relationships and constraints from raw data into the formal graph structure. Specifically, tasks involving perceptual contradictions, such as a mismatch between sound and video, and requiring the system to output the correct correction code, build the foundation for the fast-response system to intuitively realize “something is wrong.” Normally, you wouldn’t think “spicy” when you see a banana, would you? What I am trying to say is: The model I devised is an honest AI that doesn’t lie and doesn’t speak based on empirical rules. It’s an AI that only considers what is logically correct, making it less susceptible to biases like race. I believe it is fundamentally different from previous models.
Untitled Draft
The Strongest Absolutely Correct AI
The biggest weakness of current advanced information processing systems (LLMs), I believe, is “logical collapse,” or hallucination, where the AI tells plausible lies. I view this problem not merely as an issue of data quantity but as a structural flaw. Thus, I propose the “Adiabatic Integration” approach, which involves completely separating the central “Calculation Unit” responsible for logical manipulation from the external memory storing vast “Empirical Knowledge.” This mechanism, somewhat like mathematics, guarantees the validity of inference through logical constraints—in other words, correction codes. The design philosophy is similar to that of Aristotle AI, which possesses a capability level to win a gold medal at the International Mathematical Olympiad. The core of this system, the “Central Circuit,” operates on a “Formal Graph Description Language” based on first-order predicate logic, entirely eliminating ambiguity. By directly encoding these operational rules into a graph-structure-based circuit, the transparency and verifiability of inference are maximized. We all dislike people who speak based on empirical rules, right? Deliberative Inference System (System 2) This is the “brain” of this AI. It holds no knowledge and executes only logical primitives such as formal graph rewriting, integration, and contradiction resolution. Notably, logical relationships like causality and exclusion are embedded as constraint codes within the Intermediate Representation (IR) formal graph itself. During training, weights are fixed to specialize solely in the accurate execution of these logical rules, preventing unnecessary fluctuations. In essence, it’s like solving a puzzle according to rules. Completely Separated RAG (System 1?) This is the database of empirical knowledge, but it’s not just a knowledge repository. It stores not only perceptual attribute vectors (like color and sound) but also the logical constraints derived from experience itself as correction codes within formal graphs. Furthermore, each piece of knowledge holds “contextual relative positional information,” which indicates how highly it is related to a specific query. When a query arrives, this relative coordinate is calculated, and the most relevant knowledge is actively inserted into the inference path, enabling high-speed responses. The key is achieving fast responses while simultaneously ensuring logical verification. Metacognitive Control and Dynamic Switching A “Self-Inspection System (Metacognitive Auditor)” oversees the entire system. It manages the logical auditing of inferences and the dynamic switching between “Fast Response (System 1)” and “Deliberation (System 2)” depending on the situation. The switching triggers are clear. For example, if System 1′s inference steps exceed a threshold, or if a contradiction is detected in the correction codes between the intermediate form and external knowledge, it automatically switches to the deeply deliberative System 2. If the inference result of System 2 passes the logical audit, it switches back to System 1. If the audit fails, the structure of the contradiction and the knowledge ID are output as an external teacher signal log, which is utilized for future off-line training. This feedback loop is the essence of the design philosophy for ceaselessly improving the system’s overall accuracy. It’s like reviewing a test and memorizing the parts you still got wrong. Training Method: Focusing on Logical Correctness This system is trained not by chasing the quantity of data but by focusing on “logical correctness” as its core. Foundation Training of Formal Logical Operations: Using synthetic datasets, the system learns the accurate graph rewriting from a premise graph to a conclusion graph, and the ability to resolve contradictions from graphs containing conflicts. A rigorous method is employed, which evaluates the structural similarity (graph editing distance) between the inference result and the correct answer. In short, it evaluates whether the thought process is correct. Logical Encoding Training of Empirical Knowledge: This trains the ability to accurately encode causal relationships and constraints from raw data into the formal graph structure. Specifically, tasks involving perceptual contradictions, such as a mismatch between sound and video, and requiring the system to output the correct correction code, build the foundation for the fast-response system to intuitively realize “something is wrong.” Normally, you wouldn’t think “spicy” when you see a banana, would you? What I am trying to say is: The model I devised is an honest AI that doesn’t lie and doesn’t speak based on empirical rules. It’s an AI that only considers what is logically correct, making it less susceptible to biases like race. I believe it is fundamentally different from previous models.
日本語のプラットフォームなら、日本語でいいんですが...