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Deterministic Reasoning Graph (DRG) A New Paradigm for Structuring Information

The Deterministic Reasoning Graph (DRG) introduces an innovative approach to organizing information by eliminating indeterminism through explicit reasoning structures. Unlike probabilistic approaches, DRG guarantees non-probabilistic precision by recording domain-specific reasoning and decision patterns inside a graph. This paradigm serves as a foundation for multiple applications such as reasoning retrieval, controlled generation, and automated decision-making by agents.

I. Introduction to DRG

DRG is designed to provide a deterministic framework for complex reasoning tasks.

What DRG is NOT

DRG is clearly distinct from several existing concepts.

  • It is not a new language model architecture.
  • It is not a learning algorithm.
  • It does not claim to be a universal generalization solution.

II. Use Cases of DRG

DRG provides a flexible structure for multiple applications. Below are four main, non-exhaustive use cases.

1. CoRG (Chain of Reasoning Graph)

CoRG is a structured version of Retrieval-Augmented Generation specifically adapted for business use cases. It is based on a Chain of Tasks, which is a sequence of inferences where the first step consists of a context classification performed by a language model. This classification produces a label that is used to query a graph database. The retrieved context is then injected into a dynamic prompt tuning process before a second inference. This guarantees a coherent output through deterministic graph traversal.

2. Discriminator

DRG can be used as a discriminator to verify the consistency of LLM outputs by relying on a logical graph. This approach validates results against predefined rules and reinforces the reliability of generated responses.

3. Dataset Generator

By applying structured reasoning upstream, DRG ensures the consistency of datasets generated for a specific domain. This makes it possible to produce reliable data adapted to business or scientific needs.

4. Conversational and Decision-Making Agent

Conversational and decision-making agents leverage DRG to automate processes using reasoning and decision trees. Unlike RAG, which focuses on information retrieval, DRG retrieves both the reasoning and the agent’s decisions. This provides a more complete and structured approach.

III. How CoRG and Conversational Agents Work

1. CoRG Principle

CoRG starts with an initial context classification performed by an LLM. This is followed by a query to a graph database. The retrieved context is then dynamically injected into the LLM prompt to generate the output. This process is based on deterministic graph traversal and guarantees reliable results. To maximize accuracy, fine-tuning of the LLM is required and it takes into account the nodes, relations, and clusters of the graph.

2. Conversational Agent Architecture

In an agentic architecture, CoRG differs from RAG because it encodes not only information but also reasoning and decisions. Each agent relies on a DRG structure that encodes complex semantic relations.

  • Triggered after is used to manage temporal or sequential dependencies.
  • Inherits from is used to reuse existing reasoning patterns.
  • Modulated by is used to adapt behavior based on specific conditions.
  • Validated by is used to confirm decision compliance.

These relations transform the system into a controllable, reusable, and unambiguous logical graph. This is different from approaches such as LangGraph, which rely on simpler conditional routing such as if, match, and switch and therefore provide less granularity.

IV. Comparison with LangGraph

DRG stands apart from LangGraph in terms of granularity and flexibility.

  • Routing type in DRG is based on semantic and contextual routing. LangGraph uses explicit conditional routing based on values or booleans.
  • Logical structure in LangGraph is limited to decision trees. DRG applies these trees to a graph, which removes algorithmic limitations and enables a richer structure.

V. Designing a DRG

1. Collaboration with Domain Experts

The DRG graph is designed in collaboration with business teams or scientific experts. It follows a domain-specific ontology. This ensures that the LLM reasons in a way that is explicit and aligned with the requirements of the use case.

2. Constraints and Precision

By structuring reasoning in a non-probabilistic framework, DRG introduces constraints that face the LLM to follow precise business logic. This reduces ambiguity and significantly improves output reliability.

VI. Conclusion

The Deterministic Reasoning Graph (DRG) represents a major advancement in information organization and reasoning structuring. By combining logical graphs with language models, DRG enables robust applications such as CoRG and conversational and decision-making agents. At the same time, it offers a more granular and controllable alternative to systems such as LangGraph. Through its ability to encode complex semantic relationships and to collaborate with domain experts, DRG ensures precise results that are tailored to specific needs.

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