Retake the control with Deterministic Reasoning Graph (DRG)

1. Core concept: an explicit hybrid structure

👉 Why this approach?

Current text generation systems (LLMs) rely on statistical associations between words without true logical structuring. This makes traceable, verifiable, or controllable reasoning impossible. The DRG was designed to introduce an explicit and deterministic structure, capable of reflecting understandable and controllable human reasoning, ensuring precision.

The deterministic reasoning graph (DRG) is a new paradigm for structuring knowledge and reasoning. It cleverly combines three foundations:

• algorithms: reasoning and decision trees
• graphs: nodes, edges, clusters
• vector databases and LLMs:
  • chunks, derived from implicit contexts (used in vector databases)
  • query-key-value (QKV) logic: query for retrieval, key/value for indexing
  • labels drawn from indexing logic in vector databases and LLMs (K/V)

Unlike traditional LLMs that reason through probabilistic associations, the DRG structures information explicitly, hierarchically, labeled, and organized, based on a taxonomy and ontology built by domain and/or with business expertise.

A chunk is typically a description but can also be specific information about an entity, enabling highly granular and contextual knowledge capture.

Labels ensure targeted and explicit searches within the graph environment.

2. DRG use cases

👉 What are the applications?

A structured graph becomes a driver for verification, generation, decision-making, or structuring. DRG is not just a visualization; it’s an active architecture.

• CoRG (chain of reasoning graph): structured RAG (retrieval-augmented generation), tailored to business needs
• discriminator: verifying an LLM’s output through a logical graph
• dataset generator: logic and reasoning are applied upstream of the dataset to ensure domain-specific coherence and logic
• conversational and decision-making agent: automation driven by reasoning and decision trees

→ The DRG enables native data structuring, fundamentally changing how LLMs are trained or corrected.

To create a robust dataset, each piece of information must first be segmented into labeled chunks. This segmentation relies on strict compartmentalization, where every piece of information is structured, indexed, and validated through the graph. The more granular the information, the more explicit and generalizable it becomes, a fundamental rule, even in implicit learning systems.

A dataset structured with DRG doesn’t just store input/output pairs. It prepares a complete reasoning process in advance, validated through logical graph traversal (checking paths, conditions, duplicates, etc.). Then, the data is presented in a standard format that an LLM can process, without visible structure.

This is the “trick”: explicit reasoning is injected before training, even if the final format remains implicit.

Result: The LLM learns determined behavior without even knowing it’s following a graph. Welcome AGI!

3. Explicit vs. implicit reasoning

👉 Why contrast these two logics?

Because raw power isn’t enough if a system is unpredictable. By structuring information from the start, we shift from fuzzy generation logic to deterministic, verifiable, and reproducible reasoning logic.

LLMs operate through probabilistic associations: weighted vectors within a sequence (context window) without explicit structuring.

The DRG relies on a reasoning grammar:

• a node = an entity with a short, concise, precise chunk
• each chunk is semantically labeled
• reasoning or decision trees are formed from labels, with the node as the tree’s leaf
• in the graph, the node is the visible, labeled part: the entity is both a node and the label leading to it
• relationships are explicit, directed, logical, bidirectional, and multiple; they are also labeled, moving beyond the binary/Boolean logic of traditional algorithms (e.g., “validated by experimentation,” “contradicted by jurisprudence”)

→ This achieves 100% business precision in a given domain, as no probabilistic ambiguity remains.

→ Crucially: there isn’t just one path to the final result. Multiple logical reasoning paths can lead to the same output. The model doesn’t guess, it follows reasoning. From input → query (via label) + relationship (context) → a single output via multiple explicit paths.

4. DRG & CoRG

👉 What’s the difference?

DRG is a universal foundation for structured reasoning, while CoRG is a specific use case for retrieval-augmented generation (RAG). CoRG works only because DRG exists upstream.

DRG (deterministic reasoning graph) is the overarching system. It encompasses three semantic relationship trees:

• reasoning trees
• decision trees
• structured information storage trees

CoRG (chain of reasoning graph) is a RAG use case of DRG: it enables structured, verifiable, contextualized, and reusable reasoning chains, powered by the chain of task.

5. Fundamental structure: cluster > label > node > edge

👉 What’s the hierarchy?

Structuring data deterministically requires a rigorous framework. Each level (cluster, label, node, relation) provides an essential building block to encode intent, reasoning, and business meaning in an exploitable and generalizable organization.

• clusters: global (root) and local (categories), logical hierarchy
• labels: tree branches defining the semantic nature of reasoning or decisions
• nodes: leaves containing a chunk (contextualized, short, precise entity) identified by a title. Nodes are labeled; the more labels, the more versatile and granular they are
• relations (edges): named, logical, directed, bidirectional, and multiple, moving beyond binary/Boolean logic for business reasoning and decision schemas. They are labeled, a critical dimension for enriching reasoning

6. Generalization strategies

👉 How to achieve generalization?

A graph isn’t meant to contain everything, it must access the maximum amount of information with minimal nodes. This leans toward singularity, achievable only through highly granular structuring. This is called modular reasoning.

Here are the four strategies:

1. High granularity
   A node contains minimal information but is associated with many labels, increasing specificity while anchoring it in multiple logics, approaching singularity through minimal structure.

2. Maximized node reuse
   By factoring common entities, logic is centralized, and outgoing semantic relationships are enriched. This involves identifying the common denominator of nodes to create richer contexts.

3. Complementary algorithms
   Using tools like Jaccard V2F, SimRank, Leiden, Louvain, or GNNs, in an optimized order, to dynamically enrich and populate the graph without size explosion.

4. Chunk contextualization
   Each chunk is not raw data but information contextualized in a specific domain, reinforcing the graph’s logical coherence.

→ The secret to generalization is granularity. The finer the information, the more explicit and reusable it becomes.

7. Why DRG > tree of thought (ToT)

👉 Why go beyond ToT?

While tree of thought (ToT) is an interesting concept, it doesn’t structure relationships or categories. It offers a sequence of ideas, whereas DRG provides a complete, driven reasoning architecture.

ToT relies solely on a generation tree, with:

• no semantic labels
• no explicit relationships between thoughts
• no taxonomy or clustering

DRG goes much further:

• it’s ontological, algorithmically structured, and semantic
• it’s deterministic, verifiable, and domain-specific
• it handles mathematical, legal, marketing, etc., reasoning with different rules. Explicit reasoning varies by domain

8. Construction methodology

👉 What’s the procedure?

For reasoning to be valid, its structure must be precise. Each ontology is built with business expertise or by domain. Every environment starts with creating the code knowledge. DRG construction is based on business analysis, following a rigorous pipeline to ensure every entity, relationship, and tree aligns with the domain’s real reasoning.

Standard pipeline:
business analysis → entity detection → taxonomy and ontology creation → cluster and label generation → tree construction → node insertion with chunks → adding explicit logical relationships → core knowledge creation → graph population from other documents.

This process is detailed, time-consuming, and business-dependent, which is why the Turing LLM was developed to automate all or part of this chain.

9. Why it’s revolutionary

👉 Why does this change everything?

Instead of interpreting a black box, we replace it with an explicit, logical, and verifiable architecture. DRG gives semantic intelligence a concrete, stable, and exploitable form.

DRG fuses the best approaches from all worlds:

• vector DB: for implicit contextualization (chunks)
• graph: for explicit structuring (labels, relationships)
• taxonomy + ontology: for tailored business hierarchy
• granularity: for singularity and generalization
• tree algorithms: for driving decisions and reasoning
• semantic relationships: replacing fuzzy probabilistic chains with verifiable logic

We no longer reason by guessing what the model will output. We structure reasoning from the start.

⚠️ Warning – DRG functioning and limitations

The deterministic reasoning graph (DRG) is an explicit reasoning system whose performance relies entirely on three fundamental pillars:

• a rigorously defined, contextualized, and validated business ontology
• formalized reasoning and decision trees aligned with domain rules
• logical schemas and semantic relationships designed specifically for targeted use cases

Unlike probabilistic models (like traditional LLMs), DRG relies on a deterministic, structured, traceable, and verifiable architecture. It makes no implicit assumptions: it follows a logical path defined by the user, scientist, or business.

This apparent 100% precision is achievable only in fully modeled and validated use cases. DRG reliability depends on:

• the accuracy of the graph
• the completeness of modeled entities and relationships
• the structure’s coherence with real business logic

Any omission, contradiction, or approximation in modeling can invalidate reasoning. DRG doesn’t guess, it executes the logic you define, to the letter.

Thus, DRG must be seen not as an autonomous generation tool but as a structured logical execution system. Its strength lies in the quality of the structure, not the quantity of data.