Adaptive Reasoning Graph
1. Introduction
TL;DR
- Explicit context management in Adaptive Reasoning Graph, without delegation to the language model.
- Context structuring through set theory and hierarchical taxonomy.
- Maintenance and controlled evolution of the context structure by the Context Weaver.
- Possible evolution of the context, always constrained by foundational knowledge.
- Controlled, secure, and predictable environment, without free learning or autonomous creation of actions.
- These principles are formalized and applied at the Adaptive Reasoning Graph protocol level.
2. What context actually is
Context is not a formulation. It is neither an enriched prompt nor a simple memory. Context corresponds to the set of specific elements an agent needs to act while respecting the values, policies, processes, and knowledge of a company or project. In other words, it is everything that defines what the agent is allowed to understand, interpret, and do.
Each context element is treated as a distinct unit. In Adaptive Reasoning Graph, this unit is called an entity. Context is therefore not a monolithic block, but a structured set of clearly identified elements.
3. The role of the Context Weaver
The management of these entities is handled by a specific component called the Context Weaver. Its role is not to produce language or to reason on behalf of the agent. It is responsible for the structure of the context that the agent receives and utilizes.
The Context Weaver maintains a coherent, stable, and exploitable structure. It acts solely on the form of the context, never on its linguistic interpretation. This separation is essential. It ensures that reasoning always relies on a mastered framework rather than on implicit or variable understanding.
4. Structural foundations
Context construction is based on set theory and taxonomy. Each context entity belongs to a set, which corresponds to a category. These categories are organized hierarchically according to parent and child relationships. This hierarchy constitutes the taxonomy of the context.
The Context Weaver only preserves this structure. It can evolve, be validated, and expand, but always in compliance with the foundational knowledge. This foundational knowledge corresponds to the initial state of the system. It defines the rules to be respected and constitutes the bedrock from which any evolution is authorized. Without it, there is no constraining framework. The Context Weaver thus serves as a semantic context stabilizer.
5. Difference between ARG and DRG
The main difference between Adaptive Reasoning Graph and its predecessor, DRG, lies in how evolution is managed. ARG evolves, but never randomly. Evolution is only possible if it respects the foundational knowledge and the existing structure.
This point changes everything. The system can adapt without losing its bearings or creating unforeseen behaviors. Evolution becomes a controlled mechanism rather than a drift.
6. Why this choice is decisive
In a controlled environment, you do not want your agent to learn freely, whether in short-term or long-term memory. You do not want the agent's structure to evolve autonomously. Nor do you want it to create new actions.
The objective is to maintain a safe and predictable environment, where every decision can be explained and controlled. Adaptive Reasoning Graph is designed specifically to maintain this state of control.
7. Context typologies and ontology
The associated illustration shows two distinct contexts, each composed of four different context typologies. These typologies group together clusters and labels, which correspond to an intermediate level in the taxonomy.
Below this is the node level, also called the leaf. It is at this level that the source of truth unit, called the block, belongs. The leaves are connected to each other by edges. These relationships constitute the ontology of the system. This ontology can be understood as reasoning, a process, or a retrieval strategy, depending on the perspective adopted.
8. Representation choices
For reasons of clarity and efficiency, the representation used is a tree structure. It is simpler to generate and easier to utilize in practice. In reality, the complete structure of the system is a graph, but the tree provides a more readable abstraction without altering the nature of the reasoning.
9. Conclusion
Adaptive Reasoning Graph does not seek to make the agent smarter. It seeks to make its intelligence controllable. This distinction is fundamental. It allows for the construction of agents capable of evolving while remaining aligned with the constraints, rules, and objectives of a real-world environment.